Podcasts about NMS

I got out late today. Waited for the rain to move off. Talked about NMS and the Walker and Cooper book Dawn of Mankind. Among other things.

Slept in. Beautiful night’s sleep for a change but up-shot is that I didn’t get going early this morning. It’s already coming up on “broiling” season. Talked about yesterday’s bread, why NMS turning into a grind is a good thing, … Continue reading →

SWTOR 7.9 is here with a story conclusion, 007 First Light is really good, and the NMS expedition is still in progress. That and more on this episode of the New Overlords Podcast with Sema and @MaxTheGrey. MP3 Direct Download Link YouTube Link Catch us in Discord at http://newoverlords.com/discord for notes on when we record … Better than Goldeneye? – New Overlords Podcast 609: 007 First Light Read More » The post Better than Goldeneye? – New Overlords Podcast 609: 007 First Light first appeared on NEW OVERLORDS.

NMS has been divided into 3 teams in order to battle the Hive Of Glass. Fight the swarms and collect the ichor. Who will be the champions of the Universe.

Už piaty rok po sebe naša krajina čelila poklesu obyvateľov. Aktuálne sa pôrodnosť dostala na také historické minimum, ktorého úroveň bola ešte horšia ako počas Druhej svetovej vojny. Slovensko vymiera. Rodí sa stále menej a menej detí, žijeme dlhšie ako v minulosti a tak sa veková štruktúra našej spoločnosti zásadne mení - a zásadné budú i dôsledky. Napriek tak obľúbeným, neustále opakovaným a hlasným deklaráciám o dôležitosti rodiny, či o deťoch ako priorite spoločnosti, však žiadne vlády za ostatné 2-3 dekády nedokázali dať účinné a ani funkčné odpovede ako negatívny demografický trend zvrátiť a uistiť mladých ľudí, že byť rodičom a založiť si rodinu je nielen dobrým, ale aj bezpečným riešením vlastnej budúcnosti. Kočíkovné, krúžkovné či všakovaké rodičovské bonusy zjavne nezaberajú, otázkou preto je, či vôbec poznáme skutočné dôvody, prečo sa mladí ľudia obávajú zakladať rodiny, byť rodičmi a privádzať do tohto sveta svoje vlastné deti. Jednou z odpovedí môže byť aj Veľký prieskum rodín, ktorý pre finančný dom UNIQUA pripravila agentúra NMS Market Research. Podľa neho pocit bezpečia v našich rodinách opäť klesol pričom naše rodiny najviac trápia finančné starosti, ale aj obavy z budúcnosti či zdravotné problémy. Čoho sa mladí ľudia tak veľmi obávajú, ako sa s tým dá popasovať a čo skutočne trápi naše rodiny, sú to len obvyklé peniaze alebo je to i obava z budúcnosti či dokonca z členov vlastnej rodiny? Ráno Nahlas, s Michalom Mislovičom z agentúry NMS. Pekný deň a pokoj v duši praje Braňo Dobšinský.

Už piaty rok po sebe naša krajina čelila poklesu obyvateľov. Aktuálne sa pôrodnosť dostala na také historické minimum, ktorého úroveň bola ešte horšia ako počas Druhej svetovej vojny. Slovensko vymiera. Rodí sa stále menej a menej detí, žijeme dlhšie ako v minulosti a tak sa veková štruktúra našej spoločnosti zásadne mení - a zásadné budú i dôsledky. Napriek tak obľúbeným, neustále opakovaným a hlasným deklaráciám o dôležitosti rodiny, či o deťoch ako priorite spoločnosti, však žiadne vlády za ostatné 2-3 dekády nedokázali dať účinné a ani funkčné odpovede ako negatívny demografický trend zvrátiť a uistiť mladých ľudí, že byť rodičom a založiť si rodinu je nielen dobrým, ale aj bezpečným riešením vlastnej budúcnosti. Kočíkovné, krúžkovné či všakovaké rodičovské bonusy zjavne nezaberajú, otázkou preto je, či vôbec poznáme skutočné dôvody, prečo sa mladí ľudia obávajú zakladať rodiny, byť rodičmi a privádzať do tohto sveta svoje vlastné deti. Jednou z odpovedí môže byť aj Veľký prieskum rodín, ktorý pre finančný dom UNIQUA pripravila agentúra NMS Market Research. Podľa neho pocit bezpečia v našich rodinách opäť klesol pričom naše rodiny najviac trápia finančné starosti, ale aj obavy z budúcnosti či zdravotné problémy. Čoho sa mladí ľudia tak veľmi obávajú, ako sa s tým dá popasovať a čo skutočne trápi naše rodiny, sú to len obvyklé peniaze alebo je to i obava z budúcnosti či dokonca z členov vlastnej rodiny? Ráno Nahlas, s Michalom Mislovičom z agentúry NMS. Pekný deň a pokoj v duši praje Braňo Dobšinský.

Podle květnového volebního průzkum agentury NMS by volby do Sněmovny vyhrálo ANO s 32,5 % hlasů. ODS a STAN by volilo mezi 14–15 % voličů. Do Sněmovny by se dostali ještě SPD a Piráti. Těsně pod pětiprocentní hranicí nutnou pro vstup do Sněmovny se nacházejí Motoristé sobě. „To, že vznikl nový projekt, jako je Naše Česko, zatím s ničím moc nehýbe,“ konstatuje Jakub Bakule. V pořadu Jak to vidí… dále představuje velké šetření týkající se rodinných vazeb v Česku a na Slovensku.Všechny díly podcastu Jak to vidí... můžete pohodlně poslouchat v mobilní aplikaci mujRozhlas pro Android a iOS nebo na webu mujRozhlas.cz.

Advances in immunotherapies for multiple sclerosis and related disorders have increased the risk of infections and raised important questions about vaccination efficacy. This episode reviews infection risks across treatment classes, emphasizes the importance of monitoring and patient education, and discusses optimal vaccine timing to preserve protective immune responses. In this episode, Aaron L. Berkowitz, MD, PhD, FAAN, speaks with Avindra Nath, MBBS, FAAN, coauthor of the article "Infection Risk and Vaccine Considerations in Multiple Sclerosis and Related Disorders" in the Continuum® April 2026 Multiple Sclerosis and Related Disorders issue. Dr. Berkowitz is a Continuum® Audio interviewer and a professor of neurology in the Department of Neurology at the University of California, San Francisco, in San Francisco, California. Dr. Nath is the chief of the Section of Infections of the Nervous System at the National Institute of Neurological Disorders and Stroke, National Institutes of Health, in Bethesda, Maryland Additional Resources Read the article: Infection Risk and Vaccine Considerations in Multiple Sclerosis and Related Disorders Subscribe to Continuum®: shop.lww.com/Continuum Earn CME (available only to AAN members): continpub.com/AudioCME Continuum® Aloud (verbatim audio-book style recordings of articles available only to Continuum® subscribers): continpub.com/Aloud More about the American Academy of Neurology: aan.com Social Media facebook.com/continuumcme @ContinuumAAN Host: @AaronLBerkowitz Full episode transcript available here Dr Berkowitz: Over the last decades, there has been a revolution in the treatment of multiple sclerosis, neuromyelitis optica spectrum disorder, and other immune-mediated neurologic conditions with countless new, highly effective medications. However, with every new treatment comes new risks; and in the case of immunomodulatory therapy, many of those risks relate to infection. Today, I have the privilege of talking with an expert on this topic, Dr Avindra Nath, about the infectious risks of treatments for multiple sclerosis and other immune-mediated neurologic disorders.  Dr Jones: This is Dr Lyell Jones, Editor-in-Chief of Continuum. Thank you for listening to Continuum Audio. Be sure to visit the links in the episode notes for information about earning CME, subscribing to the journal, and exclusive access to interviews not featured on the podcast.  Dr Berkowitz: This is Dr Aaron Berkowitz, and today I'm interviewing Dr Avi Nath about his article on vaccine considerations and infection risk in multiple sclerosis and related disorders, which he coauthored with Dr Amit Bar-Or. This article appears in the April 2026 Continuum issue on multiple sclerosis. Welcome to the podcast, Dr Nath, and could you please introduce yourself to our audience?  Dr Nath: Thanks very much for inviting me to this podcast. I'm absolutely delighted to have the opportunity to discuss our areas of interest and expertise related to infections and vaccinations for MS patients. My area has been studying the infections of the nervous system since the beginning of the AIDS pandemic, and over the years and decades, we've developed expertise related to various types of CNS infections. That includes ones that are developing in individuals who have immune compromise due to a variety of different reasons. Dr Berkowitz: Fantastic. Well, glad to have the opportunity to speak with you today. When I was in medical school---and you were my attending, actually, we were just reminiscing, which we probably think was not that long ago, but is now over twenty years ago---there were just two medications for MS, right? Beta interferon and glatiramer acetate. And now we have over a dozen, and it's amazing to think of all the progress in these last two decades, as well as for related diseases like NMO. I don't think we even had the aquaporin-four biomarker, right, when I was working with you as a med student in the early 2000s. Dr Nath: And that certainly dates me a lot.  Dr Berkowitz: Both of us.  Dr Nath: Yeah.  Dr Berkowitz: Of course, with all these new treatments, these have been amazing advances for our patients, right? But these come with new treatment-related risks to monitor for with the immunomodulatory medications for MS and related disorders. And one of those most important risks is that of infection. So, your article reviews the potential infectious complications of medications used to treat MS, NMO, etc, and also covers considerations related to thinking about vaccines in this patient population. So, as the MS treatment landscape grows, I can say as a general neurologist, keeping up with all these medications and what to screen for and what to worry about and when to vaccinate just becomes more challenging every year. And your article has so many helpful tables, some organized by medicine, some organized by- sorry, medication, some organized by infection, some by vaccines. So, this is gonna be a great resource for our providers to print out and tape up in their clinic rooms. We won't be able to get into all the depth and detail that you have in this article today, but I do want to focus on some of the key points here related to the common medications we use for MS and which infections to think about and which vaccine considerations we might need to keep in mind for these medications. But before we delve into the drugs, I just wanna ask you more broadly, you talk in the article about the challenge of patients with immune-mediated diseases who are on immunomodulatory therapy being at risk for both flares of their disease and for infections; and these infections can present somewhat atypically, right, in immunomodulated hosts, to maybe coin a term you can correct me on, because they can't mount the full inflammatory response. So how do you approach new symptoms in patients on these immunomodulatory medicines as far as distinguishing disease flare from a treatment-related infection?  Dr Nath: So, I have to say that although a lot of new treatments have come along for MS, and they've really, you know, improved the outcome tremendously and there are so many different options, it has also kept people like me relevant because they cause a lot of various types of infections, and so keeps me in business all the same. But just as you mentioned, there's so many of them, even I have difficulty keeping track of what does what. So, you do need to be able to refer back to published literature, and the tables, I hope, will be quite useful in that regard. You're absolutely right, and you can get new infections, you can get reactivation of existing infections, and you can get atypical presentations of various types of infections that you may not normally think of. So that presents multiple challenges to the treating physician. The other interesting thing about MS is, just as you mentioned, that you already have CNS lesions to begin with. Now, on top of it, you have an infection, so now how to sort out what is the existing disease and what is the infection, it can again become challenging. But one thing is for sure: all these infections are caused by an organism. So, what you really need to do is, the underlying diagnostic is to demonstrate the presence of the organism. Whether you demonstrate it depending on the infection in the spinal fluid or in the brain or, you know, some peripheral organ system, that is going to be key to making the diagnosis. So, all your clinical acumen is good, but that alone may not be sufficient. Dr Berkowitz: Very good. So, when you see a, a patient now who has a new neurologic symptom in the context of an immune-mediated disease who's on immunomodulatory therapy, what goes through your mind? Are you thinking this disease and this drug, and sort of what are the infections, and does the syndrome match? Or are you thinking, you know, you can't always rely on the imaging to distinguish between, say, a flare of an MS and PML because white matter lesions could look similar? How do you sort of approach this scenario when it comes up?  Dr Nath: So, you're right. You have to keep an open mind so that even though you know some infections are more likely to occur with certain types of medications, that doesn't mean that others cannot occur. So, I think when you first see the patient, you should not jump to conclusions, but rather have an open mind. But yes, for example, your patient is on natalizumab, the chances of PML are going to be high. It's a very interesting drug. It does not cause immune compromise in the periphery, but what it's doing is preventing these cells from getting into the brain. So, because then it's acting at the blood-brain barrier. So that means that organisms that are already present in the brain have an opportunity to get reactivated. Turns out you don't have a lot of organisms in the brain, except JC virus seems to be one of them that does somehow, in some individuals, manage to reside out there. And so that can get reactivated. It can get reactivated in the periphery and then enter the brain, too. So, where the very specific mutations have to occur in that virus in order to take residence in the brain. That would be a suspicion that you might have, and MRI can be useful in, again, helping you think about that possibility. If you have typical lesions involving the U fibers, they're demyelinating, usually you do not have much edema around them because patient is immune compromised, but certainly within the brain in these individuals. And so, then you need to demonstrate the organism. The demonstration of the organism should be in the spinal fluid and not in the blood because in the virus, it can-- is reservoir in the kidneys and in the lymph nodes, and periodically it'll shed into the blood. Detection of the organism in the blood can be a false positive, but in the spinal fluid, it shouldn't be there unless you have an infection. Or if you cause a traumatic tap, I guess, if a patient is viremic, that's a possibility, but those are extremely rare. So at least for PML, that's the way that you would diagnose it. Now, you can develop, for example, if an individual is on fingolimod, you can get a wide variety of infections. Here it's a totally different type of mechanism of action. Here the cells are trapped within the lymph nodes, so that means now your entire periphery is immune compromised, right?  So here you can get viral infections, bacterial infections, fungal infections. So here, if a patient presents with new neurological symptoms, you have to have a really open mind for all these possibilities. Now, let's say a patient was on dimethyl fumarate, and dimethyl fumarate causes neutropenia early on. So here you have to worry about an individual developing bacterial infections, so latent tuberculosis or bacterial meningitis can occur in these individuals. That's something to keep in mind. It's not that other infections cannot occur with dimethyl fumarate, you can see PML and other things too, but the chances of bacterial infections are greater. So, you got to make sure that you draw all the cultures for that purpose. Similarly, if you're on a complement inhibitor, like a C5 inhibitor or the thing that I could use in NMO, there are the chances of meningococcal meningitis. So, these patients, you need to prevaccinate them before you start these kinds of treatments and look for that possibility. When you suspect bacterial infections, particularly acute bacterial meningitis, there time is of essence. Also, in some of the acute viral infections, for example---herpes encephalitis is another one---you have to be so careful, and if you suspect any of them, even if they're with possibly atypical manifestations, you treat first and then diagnose later, and draw all your cultures, whatever you need to, and just treat them. And these infections can also cause cerebral edema, so one has to be careful about doing spinal taps in these individuals. You want some kind of neuroimaging before you do them. In the days when we didn't have neuroimaging, we used to say, "Okay, if your patient has focal neurological signs or is comatose, you don't do it." But these days, you can get imaging very quickly and very easily. All the-- Because of our stroke management, we've learned how to do them so quickly. So, I think there's little excuse not to do imaging and prevent herniation from occurring.  Dr Berkowitz: That's very helpful. So, using the information we know about the drug, and we're going to rapid-fire review some of that in a bit to know what infections the patient is susceptible to, but acknowledging that any patient can get any infection, right? Whether they're on particular medications or not. And then if you're not sure, based on the neuroimaging, which as you said, is helpful, but not always helpful in distinguishing between infections and flares or, as you said, in the case of meningitis, encephalitis, early on at least, especially in immunocompromised or immunomodulated, quote unquote, patient might not see the typical imaging. So really, when safe, getting CSF or cultures, PCRs, and other infectious studies too is really gonna be the definitive diagnostic maneuver here. Is that fair summary across the board?  Dr Nath: I think you said that absolutely right. And you summarized that correctly. And, you know, thing about infection, a lot of neurological diseases are, you know, diagnosed by clinical acumen, like your Parkinson's and Alzheimer's and others. Think about infections is caused by an organism, demonstrate the organism, right? That should be your goal. It doesn't mean that clinical acumen is not important, but here you have an opportunity to demonstrate the organism, so you should depend upon that.  Dr Berkowitz: Okay. Well, you gave us a nice segue by talking about some of the infections to worry about with some of the medications. So what I'd like to do now for the sort of second half of our interview here is to go through some of the more common medications used for MS, and if we have time, for NMO, and just sort of go kind of rapid fire here, and for each medication, if you can tell us the kind of top infectious concerns and whether when to consider them or what screening needs to take place before or during administration of the medication, and then any vaccine considerations we should be aware of. Some of these will obviously be quite short depending on the medicine. So, going back to the two medications I alluded to earlier that were the only ones in play when you and I last saw each other on the wards when I was a medical student, beta interferon, glatiramer acetate, any infections or vaccine considerations with these medications?  Dr Nath: No, I think they're probably your safest medications now as far as immunomodulatory therapies are concerned. These two, and IVIG, if you ever use them, are probably the safest, do not require any vaccine considerations, per se. Dr Berkowitz: Perfect. Okay. So, moving on to fingolimod and others in the sphingosine-one phosphate receptor modulator family, what are the infectious considerations? Any prescreening or vaccination considerations?  Dr Nath: I think all your patients should be prescreened for antibodies to JC virus, because there is a risk for PML, and those who are positive should be closely monitored. So, it's not an absolute contraindication for using these medications, but they just require closer monitoring. With this class of drugs, PML is of consideration. Also, these varicella-zoster virus infection, yeah, with that you can develop zoster encephalitis or myelitis. It can present with motor symptoms as well, which can be atypical. You don't usually see them otherwise in immune-competent individuals. So, varicella-zoster, sometimes you can develop encephalitis, also vasculitis with varicella-zoster, so one has to be careful. So, getting the shingles vaccine can be actually very helpful to prevent these things. And then some patients can even develop herpes simplex encephalitis also, and that can be extremely atypical. So, they don't- they can involve the basal ganglia, can involve the brain stem and cerebellum. So again, your index of suspicion should be very high. Interestingly, although HSV encephalitis has been associated with NMDA receptor encephalitis, those reports of NMDA receptor encephalitis have not been published yet with NMS patients. Not sure why, maybe they just have been missed. But that doesn't seem to be a major concern. And then there are a whole host of other infections that can occur with this class of drugs, and that can include toxo; fungal infections, particularly crypto. There's a case report of histoplasmosis; hepatitis virus, particularly hepatitis C; and then the poxvirus is a good example. You can get molluscum contagiosum; warts with papillomavirus; you can get atypical mycobacteria; and even Kaposi sarcoma, which is HHV8. So, there's a huge variety of infections with the sphingosine one phosphate receptor modulators.  Dr Berkowitz: And any- aside from screening for JC virus before initiating these, any- and then continuing to monitor for JC antibody index, any other considerations as far as labs to send, monitoring before or on the drug or vaccine considerations for patients on fingolimod and the others in this category, siponimod, etcetera?  Dr Nath: Yeah, there are a lot of things to consider. All the details are really available in the chapter if you look at them. But briefly, all the things that one could potentially vaccinate patients for, all these infections I mentioned, one should do so. The timing is critical so that if you can do it before treatment, I think, before starting treatment, that is absolutely important. And you got to give them at least, you know, two to three weeks for these vaccines to take effect before starting your medication. If your patient already arrives on a medication, then you got to play this game of you know, before the next dose, give them again two to three weeks before the next dose and start vaccinating them and get all the vaccines in. Broadly, about the things to worry about the vaccines are you have live vaccines, and you've got the inactivated vaccines or the subunit vaccines. You have to be careful with live vaccines, because if your patient is immunocompromised, that virus can sometimes itself cause harm. For example, you know, yellow fever is one, and there you can develop encephalitis from it. Measles, mumps, rubella, these are all live vaccines. Now, the good thing is that a lot of us have been immunized very early in childhood, but that may not be the case any longer. And so, these things, one has to be very careful with when you're giving live vaccines, that we want to avoid them as much as possible, and individuals are gonna be immune-compromised. But all the others, meningococcus, for example, you should- the HPV vaccines, the varicella zoster vaccines, all these things, you've got to pre-vaccinate and make sure that they have an antibody response to them before starting immunocompromising therapy. Dr Berkowitz: Perfect. Okay, moving on to some of the other orals. What infectious and/or vaccine considerations do we have with teriflunomide?  Dr Nath: Okay, yeah. Teriflunomide is a very interesting drug. It's relatively safe. There is concern about the possibility of varicella zoster infection, people have reported that, and also tuberculosis. But PML is extremely rare, if not at all, and we haven't seen herpes encephalitis quite yet.  Dr Berkowitz: Got it. How about dimethyl fumarate? Dr Nath: Yeah. So dimethyl fumarate is... as I mentioned earlier, it's interesting because it causes this neutropenia. It's transient, but it occurs early on, and these patients can be at risk of PML, although small. They can develop varicella zoster virus infection, herpes encephalitis, and also fungal infections. For example, cryptococcal infection has been reported with dimethyl fumarate. Dr Berkowitz: Okay. We've spoken a bit about natalizumab and PML, and you have extensive information on this in your article, and I'll defer the reader to that. But for natalizumab, what are the key points every neurologist should know about natalizumab and PML as far as from the practical perspective, screening, frequency of screening, when to worry, when to not use natalizumab at all in the first place based on what you find in your screening for JC virus? What are the key points every neurologist should know?  Dr Nath: Uh, yes. You bring up an important point, and that is all patients should be monitored for JC virus. If they're JC virus-negative, so that's your most ideal patient to go on natalizumab, but that doesn't mean they cannot get infected with the virus. In fact, there's an interesting study claiming that, you know, patients, when they get these infusions, they're all sitting in the same room getting infused. Some have JC virus, some don't have JC virus, and so there's the potential that we may be aiding the transmission here in some way or another. The virus is an interesting one. It comes out in urine, and then it's spread through oral contamination, gets into the tonsils, and then spreads from there to your marrow and resides in the kidney and the marrow, as well as the lymph nodes, forever. So, you, you have to monitor these patients to see that during the course, even if they're negative, they could turn out positive. So, every six months or a year, an antibody test should be done on all patients irrespective. If a patient already has antibodies, that's not an absolute contraindication. It just means you've got to monitor them closely for development of new symptoms, and if, whenever there are new symptoms, don't just assume this is due to MS, but just make sure the MRI is done with and without contrast. The- and if there's still a suspicion, that you do a CSF evaluation for JC virus. Just detecting, looking for JC virus in the blood, a rising titer is another thing that can help you. And so, the titer is also important. And the reason you have rising titers is it means that there's an infection that's already occurred in the brain, and the immune system is reacting to that infection by increasing titers. But that alone is not sufficient to make the diagnosis. You still- that gives you an index of suspicion. You've got to then do the MRI and the spinal tap to, you know, be absolutely certain. So, each patient is a little bit different, so the way you monitor them is going to depend on where they are. You know, if they've had prior immunomodulatory therapy before starting natalizumab, or if they're on natalizumab for more than two years, then the chances of PML are much greater, so you may want to monitor them more closely. Uh, they never had any prior immunomodulatory therapy, you're just starting natalizumab, maybe once a year is sufficient. So, I think you've got to tailor it depending on what your risks are for each patient. Dr Berkowitz: Perfect. That's very helpful. And again, you write extensively about PML and natalizumab and PML considerations in your article. So, for a more detailed and in-depth discussion of what we just discussed, definitely hope readers will take a look at your article. Okay. Last but not least---certainly not least, 'cause we're using these probably, it seems, the most commonly in many places I've worked---rituximab, ocrelizumab are B-cell therapies for MS. What are some of the infectious and vaccine considerations related to these infusion medications?  Dr Nath: So, there's concern for PML with anti-B-cell therapies also, maybe not to the same degree as natalizumab, but the same principles should be applied. A lot of people think that these are relatively safe. I don't think so. I think we see enough number of patients on B-cell therapies with PML. So, I would use the same caution because these infections are... you know, can be fatal. So, one should be very careful, even with anti-B-cell therapies. And just with natalizumab, you also have the risk of VZV infection causing shingles. HSV1 has been reported, but there's another interesting complication that has been reported with anti-B-cell therapies, and that is severe West Nile encephalitis. And as mosquitoes-borne diseases are getting more and more prevalent, and we're seeing West Nile cases erupting every summer, I think one's got to be, you know, very cognizant of the fact that this can occur. These patients should take precautions to prevent mosquito bites from occurring and not expose themselves to areas where they could be at risk for it. Unfortunately, there is no vaccine for it and no specific treatment for West Nile. So, all one can do is use prevention strategies for mosquito bites.  Dr Berkowitz: Yeah, I'm glad you mentioned that. I think the only really truly severe neuroinvasive cases I've seen of West Nile virus have indeed been in patients who were being treated with B-cell therapy. Not, if I'm remembering correctly, for immune-mediated disease, but for a lymphoma, so probably other confounding factors there. But yeah, it's a disease we learn about and think about, but I've only seen the most severe cases in patients who had abnormal immune systems, so I'm glad you flagged that. This has been a very helpful discussion, and I've learned a lot from you. I learned a lot from your article, just as I did when you were my attending some 20-something years ago on the wards when I was a medical student. So, it's good to continue learning from you through your writing and research, and today from getting to talk to you again. I encourage our readers to read your article and to bookmark those tables for when these considerations come up for your patients on these immunomodulatory therapies and you're wondering which infections to worry about and how to manage vaccines in this patient population. So again, today I've been interviewing Dr. Avi Nath about his article on vaccine considerations and infection risk in multiple sclerosis and related disorders, which he wrote with Dr. Amit Bar-Or. This article appears in the April 2026 Continuum issue on multiple sclerosis. Be sure to check out Continuum Audio episodes from this and other issues, and thank you again to our listeners for joining today.  Dr Nath: Thank you so much, Aaron, for that wonderful interview, and I'm extremely proud of all your accomplishments over the last 20 years. You've done an amazing job, and it was such a pleasure to see you and to be able to do this interview with you. Thank you again.  Dr Berkowitz: Thanks. That means a lot. I never would have imagined- we won't say 20, how many, but 20-something years ago as the medical student looking up to you and all your expertise on these infections and all of your research that led to so much of our understanding on these, that I would find myself interviewing you two decades later. So, for all the students listening, you never know where you'll end up, but I appreciate your very kind words.  Dr Nath: That's what we hope for all our students. Thank you so much.  Dr Berkowitz: Thanks again.  Dr Monteith: This is Dr. Teshamae Monteith, Associate Editor of Continuum Audio. If you've enjoyed this episode, you'll love the journal, which is full of in-depth and clinically relevant information important for neurology practitioners. Use the link in the episode notes to learn more and subscribe. AAN members, you can get CME for listening to this interview by completing the evaluation at continpub.com/audioCME. Thank you for listening to Continuum Audio.

In the ER, seconds matter when identifying the "Dangerous Tetrad": rigidity, fever, autonomic instability, and altered mentation. This episode breaks down the clinical markers and pathophysiology of these life-threatening syndromes to help clinicians differentiate between NMS and Serotonin Syndrome. A must-listen for healthcare professionals looking to sharpen their diagnostic skills and improve patient outcomes in high-acuity settings.

少見但可能致命的抗精神病藥物惡性症候群（Neuroleptic Malignant Syndrome，簡稱NMS）的臨床表現是如何？ NMS 典型是在暴露於多巴胺阻斷藥物之後出現（最常見是抗精神病藥物，第一代與第二代抗精神病藥物階可能）；從用藥到出現症狀的中位數約 4 天，但也可能快到 1 天內就發作，或延後到 30 天以上才出現。整體病程常在第 2–3 天達到高峰；而在最早期的臨床症狀，常是血壓變動與肌張力增高。 NMS 的核心臨床特徵是：（1）高體溫（嚴重時可到 40°C 以上，大多也會有脫水的症狀）以及（2）自律神經不穩（dysautonomia）、（3）嚴重肌肉僵硬（muscular rigidity）。 ►NMS 的病理機制可能與交感神經—腎上腺素系統過度亢奮有關，這種亢奮會讓肌肉細胞內的鈣離子濃度（intracellular calcium ions）上升，使肌肉張力更高、更僵硬，同時也會出現心跳快、血壓波動、出汗等自律神經症狀。進一步來說，下視丘的多巴胺受體被阻斷，身體的散熱能力會變差；而當掌管運動中樞的基底核系統裡的多巴胺受體被阻斷，會更直接導致明顯的肌肉僵硬（rigidity）。肌肉強直本身會增加產熱，再加上散熱變差，兩者疊加成 NMS最關鍵的臨床指標——高體溫（hyperthermia）。 ►自律神經不穩的表現常包含：心跳加快、血壓快速波動（可高可低）、大量出汗、呼吸變快等；意識狀態改變也很常見，從譫妄、混亂、嗜睡到嚴重時接近僵直症（catatonia）都可能出現。 ►肌肉僵硬檢查時常被形容為「鉛管樣僵硬」（lead-pipe rigidity）：醫師幫病人把手腳關節慢慢彎直時，會覺得從頭到尾都卡卡的、阻力很平均；有時也會出現像巴金森症「齒輪樣」（cogwheel）那樣「一格一格卡住」的感覺。 -最新一集Podcast將分享在新版的《會談地圖》當中，整理自權威醫學期刊 NEJM 關於抗精神病藥物惡性症候群相關的診斷與治療，除了讓大家更加認識抗精神病藥物惡性症候群，也希望將龐雜的醫學資訊拆解，整理出貼近台灣臨床實務的內容！ 本集的Podcast，將會深入的探討： 1️⃣NMS臨床特徵與病理機制：如何將高體溫（Hyperthermia）、自律神經不穩、肌肉僵硬（Rigidity）等病理生理學症狀串聯起來，更全面的理解NMS的病徵及發病原理？ 2️⃣實驗室檢查：會利用甚麼實驗室檢查的數值來診斷NMS？ 3️⃣治療原則： NMS 需要視為重症（ICU 等級）來處理嗎？根據風險大小有哪些治療的方法呢？ 4️⃣預後與醫病溝通：預後也是不可掉以輕心的階段，停藥後的恢復期大約需要多久？有哪些後期併發症是需要留意的呢？ - 如果您對抗精神病藥物惡性症候群的議題充滿興趣，或是想進一步了解更多《新版會談地圖》的內容，歡迎加入

Jak se ženy cítí, když se po setmění pohybují po městě? Dokument Čau šťávo, kam tečeš? přináší osobní svědectví o tom, jaké drobné i zásadní volby činí, aby se cítily bezpečně. Devět z deseti mladých žen se podle výzkumu agentury NMS ocitlo v nepříjemné situaci s muži. Autorka Anna Váchová, studentka sociální antropologie na Masarykově univerzitě v Brně, dává hlas těm, jejichž obavy často zůstávají skryté.

In this episode, Lauren Bookbinder, DVM, DACVIM-LA, joined us to discuss neonatal maladjustment syndrome (NMS) and foal sepsis. She described the causes and clinical signs of NMS, management options, risk of sepsis in these foals, and prognosis. She also discussed other risk factors for foal sepsis, strategies for identifying septic foals, treatment options, and more.This episode of Disease Du Jour is brought to you by Equithrive. Do you have a problem mare? Learn more about the science behind Equithrive Mare Pellets at https://equithrive.com/products/equithrive-mare-pellets.This episode of Disease Du Jour is brought to you by Equithrive.Use promo code DUJOUR to get 20% off your first order, plus free shipping at Equithrive.com.GUESTS AND LINKS - EPISODE 177:Host: Carly Sisson (Digital Content Manager) of EquiManagement | Email Carly (CSisson@equinenetwork.com)Guest: Dr. Lauren BookbinderPodcast Website: Disease Du JourThis episode of Disease Du Jour podcast is brought to you by Equithrive.Connect with the Host: Carly Sisson (Digital Content Manager) of EquiManagement | Email Carly (CSisson@equinenetwork.com)

In this episode, Lauren Bookbinder, DVM, DACVIM-LA, joined us to discuss neonatal maladjustment syndrome (NMS) and foal sepsis. She described the causes and clinical signs of NMS, management options, risk of sepsis in these foals, and prognosis. She also discussed other risk factors for foal sepsis, strategies for identifying septic foals, treatment options, and more.This episode of Disease Du Jour is brought to you by Equithrive. Do you have a problem mare? Learn more about the science behind Equithrive Mare Pellets at https://equithrive.com/products/equithrive-mare-pellets.This episode of Disease Du Jour is brought to you by Equithrive.Use promo code DUJOUR to get 20% off your first order, plus free shipping at Equithrive.com.GUESTS AND LINKS - EPISODE 177:Host: Carly Sisson (Digital Content Manager) of EquiManagement | Email Carly (CSisson@equinenetwork.com)Guest: Dr. Lauren BookbinderPodcast Website: Disease Du JourThis episode of Disease Du Jour podcast is brought to you by Equithrive.Connect with the Host: Carly Sisson (Digital Content Manager) of EquiManagement | Email Carly (CSisson@equinenetwork.com)

Čo hovoria preferencie o našej politike? Zdá sa, že namiesto veľkých posunov vidíme skôr presuny medzi blokmi. Smer ja ako-tak stabilný, opozičný líder klesá, no rastú extrémisti aj Matovičovo hnutie. Do toho na Slovensku udrela ekonomická kríza, ktorú sa vláda snaží prekryť aj kauzou s faktúrami. Ako sa teda momentálne darí politickým stranám, prečo niektoré rastú a iné strácajú a aké to môže mať dôsledky? Tomáš Prokopčák sa v podcaste Dobré ráno pýta politického analytika Mikuláša Hanesa z NMS. Zdroj zvukov: TASR Odporúčanie Mojím dnešným odporúčaním je skvelý seriál Shrinking na Apple TV. Je to vtipné, je to smutné, je to láskavé a z celej tejto série o terapii, vzťahoch a prežívaní straty ide predovšetkým ľudskosť. Skúste. – Všetky podcasty denníka SME nájdete na⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ sme.sk/podcasty⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ – Odoberajte aj audio verziu denného newslettra⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ SME.sk⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ s najdôležitejšími správami na⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ sme.sk/brifingSee omnystudio.com/listener for privacy information.

Hnutí Naše Česko vyrazilo do českého politického dostihu opravdu zostra. V průzkumu NMS, což je vůbec první průzkum, který novou formaci jihočeského hejtmana Martina Kuby změřil, dosáhlo bezmála pěti procent, což je hranice pro vstup do Poslanecké sněmovny.

Hnutí Naše Česko vyrazilo do českého politického dostihu opravdu zostra. V průzkumu NMS, což je vůbec první průzkum, který novou formaci jihočeského hejtmana Martina Kuby změřil, dosáhlo bezmála pěti procent, což je hranice pro vstup do Poslanecké sněmovny.Všechny díly podcastu Názory a argumenty můžete pohodlně poslouchat v mobilní aplikaci mujRozhlas pro Android a iOS nebo na webu mujRozhlas.cz.

Episode 210: Heat Stroke BasicsWritten by Jacob Dunn, MS4, American University of the Caribbean. Edits and comments by Hector Arreaza, MD.You are listening to Rio Bravo qWeek Podcast, your weekly dose of knowledge brought to you by the Rio Bravo Family Medicine Residency Program from Bakersfield, California, a UCLA-affiliated program sponsored by Clinica Sierra Vista, Let Us Be Your Healthcare Home. This podcast was created for educational purposes only. Visit your primary care provider for additional medical advice. Definition:Heat stroke represents the most severe form of heat-related illness, characterized by a core body temperature exceeding 40°C (104°F) accompanied by central nervous system (CNS) dysfunction. Arreaza: Key element is the body temperature and altered mental status. Jacob: This life-threatening condition arises from the body's failure to dissipate heat effectively, often in the context of excessive environmental heat load or strenuous physical activity. Arreaza: You mentioned, it is a spectrum. What is the difference between heat exhaustion and heat stroke? Jacob: Unlike milder heat illnesses such as heat exhaustion, heat stroke involves multisystem organ dysfunction driven by direct thermal injury, systemic inflammation, and cytokine release. You can think of it as the body's thermostat breaking under extreme stress — leading to rapid, cascading failures if not addressed immediately. Arreaza: Tell us what you found out about the pathophysiology of heat stroke?Jacob: Pathophysiology: Under normal conditions, the body keeps its core temperature tightly controlled through sweating, vasodilation of skin blood vessels, and behavioral responses like seeking shade or drinking water. But in extreme heat or prolonged exertion, those mechanisms get overwhelmed.Once core temperature rises above about 40°C (104°F), the hypothalamus—the brain's thermostat—can't keep up. The body shifts from controlled thermoregulation to uncontrolled, passive heating. Heat stroke isn't just someone getting too hot—it's a full-blown failure of the body's heat-regulating system. Arreaza: So, it's interesting. the cell functions get affected at this point, several dangerous processes start happening at the same time.Jacob: Yes: Cellular Heat InjuryHigh temperatures disrupt proteins, enzymes, and cell membranes. Mitochondria start to fail, ATP production drops, and cells become leaky. This leads to direct tissue injury in vital organs like the brain, liver, kidneys, and heart.Arreaza: Yikes. Cytokines play a big role in the pathophysiology of heat stroke too. Jacob: Systemic Inflammatory ResponseHeat damages the gut barrier, allowing endotoxins to enter the bloodstream. This triggers a massive cytokine release—similar to sepsis. The result is widespread inflammation, endothelial injury, and microvascular collapse.Arreaza: What other systems are affected?Coagulation AbnormalitiesEndothelial damage activates the clotting cascade. Patients may develop a DIC-like picture: microthrombi forming in some areas while clotting factors get consumed in others. This contributes to organ dysfunction and bleeding.Circulatory CollapseAs the body shunts blood to the skin for cooling, perfusion to vital organs drops. Combine that with dehydration from sweating and fluid loss, and you get hypotension, decreased cardiac output, and worsening ischemia.Arreaza: And one of the key features is neurologic dysfunction.Jacob: Neurologic DysfunctionThe brain is extremely sensitive to heat. Encephalopathy, confusion, seizures, and coma occur because neurons malfunction at high temperatures. This is why altered mental status is the hallmark of true heat stroke.Arreaza: Cell injury, inflammation, coagulopathy, circulatory collapse and neurologic dysfunction. Jacob: Ultimately, heat stroke is a multisystem catastrophic event—a combination of thermal injury, inflammatory storm, coagulopathy, and circulatory collapse. Without rapid cooling and aggressive supportive care, these processes spiral into irreversible organ failure.Background and Types:Arreaza: Heat stroke is part of a spectrum of heat-related disorders—it is a true medical emergency. Mortality rate reaches 30%, even with optimal treatment. This mortality correlates directly with the duration of core hyperthermia. I'm reminded of the first time I heard about heat stroke in a baby who was left inside a car in the summer 2005. Jacob: There are two primary types: -nonexertional (classic) heat stroke, which develops insidiously over days and predominantly affects vulnerable populations like children, the elderly, and those with chronic illnesses during heat waves; -exertional heat stroke, which strikes rapidly in young, otherwise healthy individuals, often during intense exercise in hot, humid conditions. Arreaza: In our community, farm workers are especially at risk of heat stroke, but any person living in the Central Valley is basically at risk.Jacob: Risk factors amplify vulnerability across both types, including dehydration, cardiovascular disease, medications that impair sweating (e.g., anticholinergics), and acclimatization deficits. Notably, anhidrosis (lack of sweating) is common but not required for diagnosis. Hot, dry skin can signal the shift from heat exhaustion to stroke. Arreaza: What other conditions look like heat stroke?Differential Diagnosis:Jacob: Presenting with altered mental status and hyperthermia, heat stroke demands a broad differential to avoid missing mimics. -Environmental: heat exhaustion, syncope, or cramps. -Infectious etiologies like sepsis or meningitis must be ruled out. -Endocrine emergencies such as thyroid storm, pheochromocytoma, or diabetic ketoacidosis (DKA) can overlap. -Neurologic insults include cerebrovascular accident (CVA), hypothalamic lesions (bleeding or infarct), or status epilepticus. -Toxicologic culprits are plentiful—sympathomimetic or anticholinergic toxidromes, salicylate poisoning, serotonin syndrome, malignant hyperthermia, neuroleptic malignant syndrome (NMS), or even alcohol/benzodiazepine withdrawal. When it comes to differentials, it is always best to cast a wide net and think about what we could be missing if this is not heat stroke. Arreaza: Let's say we have a patient with hyperthermia and we have to assess him in the ER. What should we do to diagnose it?Jacob: Workup:Diagnosis is primarily clinical, hinging on documented hyperthermia (>40°C) plus CNS changes (e.g., confusion, delirium, seizures, coma) in a hot environment. Arreaza: No single lab confirms it, but targeted testing allows us to detect complications and rule out alternative diagnosis. Jacob: -Start with ECG to assess for dysrhythmias or ischemic changes (sinus tachycardia is classic; ST depressions or T-wave inversions may hint at myocardial strain). -Labs include complete blood count (CBC), comprehensive metabolic panel (electrolytes, renal function, liver enzymes), glucose, arterial blood gas, lactate (elevated in shock), coagulation studies (for disseminated intravascular coagulation, or DIC), creatine kinase (CK) and myoglobin (for rhabdomyolysis), and urinalysis. Toxicology screen if history suggests. Arreaza: I can imagine doing all this while trying to cool down the patient. What about imaging?-Imaging: chest X-ray for pulmonary issues, non-contrast head CT if neurologic concerns suggest edema or bleed (consider lumbar puncture if infection suspected). It is important to note that continuous core temperature monitoring—via rectal, esophageal, or bladder probe—is essential, not just peripheral skin checks. Arreaza: TreatmentManagement:Time is tissue here—initiate cooling en route, if possible, as delays skyrocket morbidity. ABCs first: secure airway (intubate if needed, favoring rocuronium over succinylcholine to avoid hyperkalemia risk), support breathing, and stabilize circulation. -Remove the patient from the heat source, strip clothing, and launch aggressive cooling to target 38-39°C (102-102°F) before halting to prevent rebound hypothermia. -For exertional cases, ice-water immersion reigns supreme—it's the fastest method, with immersion in cold water resulting in near-100% survival if started within 30 minutes. -Nonexertional benefits from evaporative cooling: mist with tepid water (15-25°C) plus fans for convective airflow. -Adjuncts include ice packs to neck, axillae, and groin; -room-temperature IV fluids (avoid cold initially to prevent shivering); -refractory cases, invasive options like peritoneal lavage, endovascular cooling catheters, or even ECMO. -Fluid resuscitation with lactated Ringer's or normal saline (250-500 mL boluses) protects kidneys and counters rhabdomyolysis—aim for urine output of 2-3 mL/kg/hour. Arreaza: What about medications?Jacob: Benzodiazepines (e.g., lorazepam) control agitation, seizures, or shivering; propofol or fentanyl if intubated. Avoid antipyretics like acetaminophen. For intubation, etomidate or ketamine as induction agents. Hypotension often resolves with cooling and fluids; if not, use dopamine or dobutamine over norepinephrine to avoid vasoconstriction. Jacob: What IV fluid is recommended/best for patients with heat stroke?Both lactated Ringer's solution and normal saline are recommended as initial IV fluids for rehydration, but balanced crystalloids such as LR are increasingly favored due to their lower risk of hyperchloremic metabolic acidosis and AKI. However, direct evidence comparing the two specifically in the setting of heat stroke is limited. Arreaza: Are cold IV fluids better/preferred over room temperature fluids?Cold IV fluids are recommended as an adjunctive therapy to help lower core temperature in heat stroke, but they should not delay or replace primary cooling methods such as cold-water immersion. Cold IV fluids can decrease core temperature more rapidly than room temperature fluids. For example, 30mL/kg bolus of chilled isotonic fluids at 4 degrees Celsius over 30 minutes can decrease core temperature by about 1 degree Celsius, compared to 0.5 degree Celsius with room temperature fluids. Arreaza: Getting cold IV sounds uncomfortable but necessary for those patients. Our favorite topic.Screening and Prevention:-Heat stroke prevention focuses on public health and individual awareness rather than routine testing. -High-risk groups—elderly, children, athletes, laborers, or those on impairing meds—should acclimatize gradually (7-14 days), hydrate preemptively (electrolyte solutions over plain water), and monitor temperature in exertional settings. -Communities during heat waves need cooling centers and alerts. -For clinicians, educate patients with CVD or obesity about early signs like dizziness or nausea. -No formal "screening" exists, but vigilance in EDs during summer surges saves lives. -Arreaza: I think awareness is a key element in prevention, so education of the public through traditional media like TV, and even social media can contribute to the prevention of this catastrophic condition.Jacob: Ya so heat stroke is something that should be on every physician's radar in the central valley especially in the summer time given the hot temperatures. Rapid recognition is key. Arreaza: Thanks, Jacob for this topic, and until next time, this is Dr. Arreaza, signing off.Even without trying, every night you go to bed a little wiser. Thanks for listening to Rio Bravo qWeek Podcast. We want to hear from you, send us an email at RioBravoqWeek@clinicasierravista.org, or visit our website riobravofmrp.org/qweek. See you next week! References:Gaudio FG, Grissom CK. Cooling Methods in Heat Stroke. J Emerg Med. 2016 Apr;50(4):607-16. doi: 10.1016/j.jemermed.2015.09.014. Epub 2015 Oct 31. PMID: 26525947. https://pubmed.ncbi.nlm.nih.gov/26525947/.Platt, M. A., & LoVecchio, F. (n.d.). Nonexertional classic heat stroke in adults. In UpToDate. Retrieved September 7, 2025, from https://www.uptodate.com/contents/nonexertional-classic-heat-stroke-in-adults. (Key addition: Emphasizes insidious onset in at-risk populations and the role of urban heat islands in exacerbating classic cases.) Heat Stroke. WikEM. Retrieved December 3, 2025, from https://wikem.org/wiki/Heat_stroke. (Key additions: Details on cooling rates for immersion therapy, confirmation that anhidrosis is not diagnostic, and fluid titration to urine output for rhabdomyolysis prevention.)Theme song, Works All The Time by Dominik Schwarzer, YouTube ID: CUBDNERZU8HXUHBS, purchased from https://www.premiumbeat.com/. 

Tři čtvrtiny lidí by si přáli, aby Andrej Babiš (ANO) veřejně představil řešení svého střetu zájmů, jak požaduje prezident Petr Pavel, zjistil průzkum pro Český rozhlas od agentury NMS. „Prezident se nechce stát spolupachatelem porušení právního řádu,“ říká v pořadu Pro a proti poslankyně Pirátské strany Kateřina Stojanová. „Nemůže podmiňovat jmenování premiéra velmi extenzivním výkladem zákona,“ nesouhlasí poslanec hnutí ANO Radek Vondráček.Všechny díly podcastu Pro a proti můžete pohodlně poslouchat v mobilní aplikaci mujRozhlas pro Android a iOS nebo na webu mujRozhlas.cz.

In Episode 30 of Queer Storytime, Stevie sits down with Valentina “Vale” Mendoza — a Chicana trans woman, former Wall Street transactional attorney, founder of MeVale, P.C., and a 2026 grassroots congressional candidate running in New Jersey's 7th District. With a bold platform rooted in economic, racial, and social justice, Vale represents a new generation of political leadership: unapologetically queer, deeply community-grounded, and unwilling to compromise people's humanity for political convenience.Stevie opens the episode with a grounding invitation — a reminder to breathe, to reset, and to reconnect with our bodies in a moment when the nation is gripped by fear, fascism, and political instability. From there, this conversation moves between the personal and the political with intention: Vale speaks candidly about growing up the daughter of working-class immigrants, navigating transition, overcoming corporate disillusionment, and finding her purpose through survival, recovery, and community.Together, Stevie and Vale unpack major questions facing the country today:What does liberation actually mean — spiritually, politically, and materially?Is the U.S. Constitution a living document meant to evolve?How do we move people out of fight-or-flight long enough to talk about policy?Where has the Democratic Party abandoned its values — and why is performative allyship a threat?Why is democratic socialism resonating with new generations of voters?How can economic, racial, and social justice be pursued as interconnected goals?What qualifications should be required for elected office?What does genuine representation look like for queer, trans, disabled, working-class, and immigrant communities?Vale brings forward a vision for systemic change: worker empowerment, class consciousness, dismantling billionaire influence, addressing disability inequities, and building economic systems that honor human dignity rather than extract from it. She also offers sharp reflections on the political center drifting rightward — and why rhetoric from so-called allies harms vulnerable communities as much as overt hate.As the episode moves into its reflective closing, Vale and Stevie explore purpose, identity, joy, community, and the spiritual dimensions of political work. Vale shares a message for queer and trans youth, a warning for legislators targeting LGBTQ+ lives, and a truth many of us know intimately: trans people become experts at transformation because our survival demands it.This episode is essential listening for anyone navigating despair in today's political climate — and for those seeking grounded, intersectional, justice-driven leadership directly from the communities most impacted.

Welcome, dolls, gays, & theys; thank you for tuning in to Queer Story Time. In this episode, Stevie sits down with Dakarai Larriett, a proud son of Alabama, entrepreneur, community volunteer, and U.S. Senate candidate whose campaign was born from a traumatic false arrest. Drawing from a lifetime of lived experience, growing up a Jehovah's Witness, navigating body dysphoria as a teen with gynecomastia, and building a career before returning home. Dakarai shares how his journey through faith, shame, and resilience shaped his mission to fight for justice, healthcare, and dignity for marginalized communities.Together, Stevie and Dakarai explore how personal harm transforms into political action. Dakarai recounts the racial and homophobic profiling that led to his arrest and how that experience inspired his Motorist Bill of Rights, calling for greater transparency, access to bodycam footage, and fair policing. The conversation also dives into Alabama's pressing issues from rural hospital closures and maternal health disparities to education, economic opportunity, and voter suppression, all through the lens of lived experience.This episode blends intimate truth-telling with bold policy vision, asking what it truly takes to represent people who've been pushed to the margins, and how compassion, courage, and representation can change the system from within.What you'll hear in this episodeDakarai's childhood in Alabama and faith journey away from Jehovah's Witnesses toward a welcoming UMC community.The physical and emotional impact of gynecomastia and why body-related shame matters to public health conversations.The false arrest: dashcam/bodycam evidence, dehumanizing language by officers, and the broken accountability system.Why Dakarai turned trauma into activism and a Senate run, and what justice means to him.Concrete policy proposals: Motorist Bill of Rights, healthcare access (Medicare/Medicaid expansion), maternal health, education investment, and voter access.A candid conversation about representation, the politics of the South, and how to build coalitions that protect the most vulnerable.

Mládí vpřed a více žen. Jak se změní Sněmovna? Kdo volil koho? Proč dominoval Babiš? Kvůli čemu vyhořeli komunisté? A kdo to hodil Motoristům? Velká analýza voleb s Terezou Friedrichovou z agentury NMS a Michalem Kormaňákem z Ipsosu. Ptá se Matěj Skalický.

Mládí vpřed a více žen. Jak se změní Sněmovna? Kdo volil koho? Proč dominoval Babiš? Kvůli čemu vyhořeli komunisté? A kdo to hodil Motoristům? Velká analýza voleb s Terezou Friedrichovou z agentury NMS a Michalem Kormaňákem z Ipsosu. Ptá se Matěj Skalický. Všechny díly podcastu Vinohradská 12 můžete pohodlně poslouchat v mobilní aplikaci mujRozhlas pro Android a iOS nebo na webu mujRozhlas.cz.

In this powerful and deeply moving episode, I'm joined by Renee Thibeau, a devoted mother, passionate advocate, and fierce ally to the transgender community. Renee is the proud mom of three incredible children: her oldest, a 25-year-old cisgender daughter; her 17-year-old son, who is autistic and full of love and light; and her courageous transgender daughter — the reason she has stepped so fully into public advocacy.Renee's journey began when her ten-year-old child shared that she was a girl living in a boy's body and, heartbreakingly, had contemplated ending her own life. Faced with this reality, Renee chose love, acceptance, and action. By affirming her daughter's truth, advocating for her well-being, and surrounding her with support, Renee helped transform despair into hope and fear into thriving.Now, Renee speaks out publicly to share their story — not for recognition, but because she knows the stakes. Every child deserves to be seen and accepted for who they truly are, and every parent has the power to save a life with love. Through her writing, speaking, and tireless advocacy, Renee proves that empathy and understanding are life-saving acts, and that true success is measured not by accolades but by the lives we protect.Together, we dive deep into:The myths and false narratives fueling anti-trans rhetoric in politics and religionRenee's personal journey as a parent and the unconditional love that guided her through her daughter's transitionStories of support, including her late Aunt Sylvia—a nun whose unwavering affirmation reminds us of the true meaning of faithThe dangerous hypocrisy of lawmakers who legislate against the LGBTQ+ community while hiding their own truthsHow complacency and societal blind spots have contributed to the rise of harmful cultural narrativesWhy hormone therapy is reversible, but suicide is not — a stark reality we cannot ignoreWhat it means to raise trans kids with joy, love, and dignity, while preparing them for futures full of hopeRenee shares her heart, her advocacy, and her unapologetic truth, reminding us that being an ally isn't passive — it's about showing up, speaking out, and protecting the most vulnerable among us.This is a deeply moving, fiery, and inspiring conversation you won't want to miss.

0:00 Hey Hey it's MonJay, Scrubbing Bubbles2:52 Intro/Housekeeping6:05 AJ Hidden Gems Video9:06 No Mans Sky Update15:30 Tips, Spooky Season Is On!21:30 Bryans Spooky Season Plans23:45 Tips, State Of Play27:43 Zombie Army VR33:05 Tips, NMS, Aces Of Thunder45:00 More Tips55:45 Games On Sale1:22:45 Tips1:25:30 Four Minute Challenge1:33:15 Tips 1:36:05 Thank You!1:37:05 Outro, AJ Shoutouts1:40:00 Poll Results

0:00 Hey Hey it's WesDay1:13 Intro/Housekeeping4:00 Reviewing Into Black8:13 Tips, NMS, Into Black21:05 NMS Update with PSSR!39:30 Tips, PSSR43:45 Grimlord50:45 Aces Of Thunder New Release Date? 59:05 Tips, Cats1:07:30 Four Minute Challenge1:17:30 Virtual Strangers Upcoming Stuff1:18:40 Thank You! 1:19:56 Outro1:21:25 Flashback Aug 7, 2021

Welcome back to Queer Story Time The Podcast where we honor the lived experiences, wisdom, and resilience of LGBTQ+ individuals across generations. I'm your host, Stevie, and today's episode features a very special guest: Wendy Cole (she/her), a 77-year-old transgender woman whose life and voice are a beacon for so many in our community—especially those navigating transition later in life.Wendy is a transition mentor, public speaker, podcast host, and truth-teller whose work is rooted in compassion, candor, and fierce advocacy. After beginning her transition in her late 60s, Wendy has made it her mission to support and guide other trans people through their own journeys—offering not only practical tools and emotional accountability, but also the kind of lived wisdom that only comes from doing the hard work of becoming your full self.Wendy co-hosts Demystifying The Transgender Journey Podcast and is a visible presence across social media and public speaking platforms, using her voice to humanize trans experiences, challenge harmful narratives, and affirm that authenticity has no expiration date. Whether she's speaking to a room full of allies, mentoring one-on-one, or pushing back against legislative attacks, Wendy's message is clear: we've always been here, and we are not going back into hiding.

*Let go of past failures, pain, or even past victories that no longer serve you. Stay spiritually alertGod is working something new, even in places you least expect.Setmix 122 Tracklist:1. Kelvin Momo & Tycoon - Modimo (feat. Sir Trill & Murumba Pitch)2. H-Tee & Nms deep - Bavumile feat. Nganiie3. Kabza De Small, Soa Matrix - Amakhosi ft Zawadi Yamungu, Tshego AMG, Tribe Franko4. DJ Maphorisa, Xduppy & Kabza De Small - Abantwana Bakho (feat. Thatohatsi, Young Stunna & Nkosazana Daughter)5. Mdu aka TRP - Mabebuza (Revisit)6. Sfarzo Rtee, Jazzworx & Thukuthela - Asiyeni (feat. DBN Gogo)7. Mkeyz - Mntase Vuka (feat. MDU aka TRP, Djy Vino & Da Ish)8. Mas Musiq & Daliwonga - iMali yami (feat. DBN Gogo, Xolani Guitars & Lawd Weezy)9. Sam Deep, Stixx, Nvcho - Bhari10. Tycoon & MDU aka TRP - Shamba Nabo (feat. Cowboii)11. OUT OF ORDER, Kelvin Momo - Sunset12. Kelvin Momo - Peak Hour (feat. Maremo Violin)13. Kelvin Momo - 583 (feat. Nvcho & Stixx)14. Simplekeyz - Summer Daze (Tribute to Dukesoul)15. De Mthuda ft. Acatears - Morena16. Mkeyz - Siyibonile (feat. De Mthuda & Sam Deep)17. Kelvin Momo - Izintho (feat. Mkeyz & Mzizi)18. Mdu aka TRP - Ngimtholile (feat. Zee_Nhle, Tracy, Thatohatsi & Toby Franco)19. Sfarzo Rtee - Be My Somebody (feat. Jinger Stone)20. Tycoon - The World of 2 (feat. MDU a.k.a TRP)21. Kabza De Small ft Djy Vino, Nkulee501 - Wela MarnIsaiah 43:18-19

In this deeply moving Pride Month episode of Queer Storytime, we center community voices and the power of storytelling to uplift, reflect, and affirm our shared humanity. Host Stevie, shares five personal messages from queer and trans individuals, and their loved ones. Highlighting the resilience, growth, and connection that define our community. 

Fluphenazine is a high-potency typical antipsychotic that primarily acts as a dopamine D2 receptor antagonist in the mesolimbic pathway, reducing positive symptoms of schizophrenia. Extrapyramidal symptoms (EPS), such as dystonia, akathisia, and parkinsonism, are common due to potent D2 blockade in the nigrostriatal pathway. Neuroleptic malignant syndrome (NMS), though rare, is a life-threatening adverse effect characterized by rigidity, hyperthermia, altered mental status, and autonomic instability. CYP2D6 inhibitors (e.g., fluoxetine, paroxetine) can increase fluphenazine plasma concentrations, potentially raising the risk of toxicity and side effects. Concomitant use of fluphenazine with CNS depressants (e.g., alcohol, benzodiazepines) can enhance sedation and respiratory depression.

Happy Pride to all the dolls, gays, theys, and beyond! We're celebrating two major milestones in this episode: Pride 2025 coincides with our 25th episode! Host Stevie Inghram takes the mic solo in this heartfelt reflection, offering eight profound truths learned from over a year of powerful conversations with queer and trans guests from around the world.Based on a widely loved blog post from her new site www.futuredrstevie.com, this episode explores the resilience, wisdom, and beauty within the LGBTQIA+ community and the healing power of storytelling, science, and chosen family.From our century old fight for survival and the impact of white supremacy on queer existence to the truth that not all queer people can - or should - come out, Stevie's words are an invitation to deepen our understanding, reframe pride, and commit to a shared vision of healing and liberation.✨ Key themes include:Why queer and trans people simply want to grow old in peaceThe myth that politicians can erase our existenceHow community transforms traumaWhat respect actually looks likeThe importance of allowing queer folks to choose not to come outWhy your own inner work supports the liberation of othersThe universal possibility of embodying your true self at any age

Pre väčšinu Slovákov je rodina ešte stále hlavným zdrojom bezpečia, blízkosti i dôvery. Tento základný pocit však dnes ohrozujú obavy z nedostatku peňazí, ale aj politika, ktorá dokáže rodiny generačne štiepiť. Zásadnou obavou je i strach, že sa nedokážeme postarať o nemohúceho príbuzného. Pre väčšinu Slovákov je doteraz rodina hlavným zdrojom bezpečia, blízkosti i dôvery. Aktuálne však existujú minimálne dva, presnejšie tri kľúčové faktory, ktorý tento pocit silne nahlodávajú. Okrem už tradičného problému nedostatku financií, je to aktuálne tak silná polarizácia na našej politickej scéne, že svojou intenzitou zasahuje už aj do úzkych rodinných väzieb. Nemenej prekvapivé môžu byť aj naše obavy o to, ako sa dokážeme postarať o rodinných príslušníkov, ktorí majú nejaký vážny zdravotný problém či trpia nejakým zdravotným znevýhodnením. To toľko proklamované bezpečie rodinného krbu - kam sa však väčšina z nás tradične utieka, tak môže byť dnes oveľa krehkejšie, než ako by sme chceli. To všetko vyplýva z prieskumu realizovaného agentúrou NMS pre finančný dom Uniqa.Väčšia časť našej populácie dokáže mať (i v rámci rodiny) nejaký zhodný názor, to čo to štiepi a polarizuje sú vyjadrenia a činy politikov, ktorí sa medzi sebou radi vymedzujú, hovorí Michal Mislovič z agentúry NMS.A ešte jedna zaujímavosť. Slováci vidia rodinu hlavne tam, kde sú deti Každé spolužitie s deťmi je väčšinovo označované za rodinu a to bez ohľadu na to, akí dospelí sa o deti starajú.O čom tieto naše obavy vypovedajú a sú i dôsledkom zlyhávajúceho štátu v jeho sociálnych záväzkoch či nedostatku empatie a sebastrednosti našich politických elít? Čo s tým vieme urobiť a aký dosah to má na súdržnosť celej našej spoločnosti?No a napokon, nakoľko sme ešte stále tradičnou spoločnosťou koreniacou vo vzorcoch z minulého či dokonca predminulého storočia?Ráno Nahlas, dnes s analytikom Michalom Mislovičom z výskumnej agentúry NMS. Pekný deň a pokoj v duši praje Braňo Dobšinský.

Pre väčšinu Slovákov je rodina ešte stále hlavným zdrojom bezpečia, blízkosti i dôvery. Tento základný pocit však dnes ohrozujú obavy z nedostatku peňazí, ale aj politika, ktorá dokáže rodiny generačne štiepiť. Zásadnou obavou je i strach, že sa nedokážeme postarať o nemohúceho príbuzného. Pre väčšinu Slovákov je doteraz rodina hlavným zdrojom bezpečia, blízkosti i dôvery. Aktuálne však existujú minimálne dva, presnejšie tri kľúčové faktory, ktorý tento pocit silne nahlodávajú. Okrem už tradičného problému nedostatku financií, je to aktuálne tak silná polarizácia na našej politickej scéne, že svojou intenzitou zasahuje už aj do úzkych rodinných väzieb. Nemenej prekvapivé môžu byť aj naše obavy o to, ako sa dokážeme postarať o rodinných príslušníkov, ktorí majú nejaký vážny zdravotný problém či trpia nejakým zdravotným znevýhodnením. To toľko proklamované bezpečie rodinného krbu - kam sa však väčšina z nás tradične utieka, tak môže byť dnes oveľa krehkejšie, než ako by sme chceli. To všetko vyplýva z prieskumu realizovaného agentúrou NMS pre finančný dom Uniqa.Väčšia časť našej populácie dokáže mať (i v rámci rodiny) nejaký zhodný názor, to čo to štiepi a polarizuje sú vyjadrenia a činy politikov, ktorí sa medzi sebou radi vymedzujú, hovorí Michal Mislovič z agentúry NMS.A ešte jedna zaujímavosť. Slováci vidia rodinu hlavne tam, kde sú deti Každé spolužitie s deťmi je väčšinovo označované za rodinu a to bez ohľadu na to, akí dospelí sa o deti starajú.O čom tieto naše obavy vypovedajú a sú i dôsledkom zlyhávajúceho štátu v jeho sociálnych záväzkoch či nedostatku empatie a sebastrednosti našich politických elít? Čo s tým vieme urobiť a aký dosah to má na súdržnosť celej našej spoločnosti?No a napokon, nakoľko sme ešte stále tradičnou spoločnosťou koreniacou vo vzorcoch z minulého či dokonca predminulého storočia?Ráno Nahlas, dnes s analytikom Michalom Mislovičom z výskumnej agentúry NMS. Pekný deň a pokoj v duši praje Braňo Dobšinský.

„To není žádná demokracie,“ prohlásil Andrej Babiš v rozhovoru pro Deník.cz v reakci na radikální celní politiku Donalda Trumpa. Také dodal, že má s americkým prezidentem společnou hlavně červenou barvu kampaně. Přitom ještě v listopadu minulého roku prohlásil, že de facto mají totožné programy. „Jsem přesvědčen, že Donald Trump je nejlepším řešením pro Evropu a také pro celý svět,“ pronesl na CNN Prima News.  Před čtrnácti dny ovšem zveřejnila agentura NMS průzkum, z něhož vyplynulo, že Trumpovu politiku v Česku podporuje jen 20 procent lidí a celých 40 procent jako hlavního představitele trumpismu u nás vidí Andreje Babiše. Začal velký obrat a předstírání, že s Trumpem nemá on ani hnutí ANO vlastně nic společného.  Zároveň zaregistroval, že mu volební preference nerostou, takže začala zákulisní jednání se Socdem o účasti některých špičkových politiků na kandidátce hnutí ANO. Babiš na to jde tak, že jakoby nechává jednání na krajských organizacích, aby pak mohl říct: je to rozhodnutí regionů. Jednání však stále ještě nejsou u konce, protože Socdem chce vytvořit s ANO formální koalici.  Co může být černou labutí říjnových sněmovních voleb? Jak si Lubomír Zaorálek představuje akciový trh? A co je smyslem Trumpovy těkavé ekonomické politiky?

Today we're going to break down three life-threatening syndromes that every CRNA, anesthesia provider, and healthcare professional should know: Serotonin Syndrome, Neuroleptic Malignant Syndrome (NMS), and Malignant Hyperthermia (MH). Though these conditions are rare, their symptoms often overlap—making quick, accurate diagnosis and intervention critical. Garry and Terry take a deep dive into the causes, symptoms, physiology, and treatment protocols for each condition, while also sprinkling in their signature humor and real-world insights. Here's some of what we discuss in this episode:

According to the latest data from research agency NMS, 16% of women in Slovakia suffer from period poverty. This term is desribed as lack of access to sanitary and menstrual products. We talked to Natália Blahová, a representative from the initiative Dôstojná menštruácia, who explains what this alarming problem means from women and how to fight it.

In this powerful episode of Queer Storytime, we dive deep into the urgent and historical topic of mutual aid within the LGBTQIA+ community. From the Stonewall era to the HIV/AIDS crisis and today's escalating attacks on gender and sexually affirming health care, mutual aid has been a lifeline for queer and trans folks.Stevie lays out the distinction between charity and solidarity, highlighting why institutional approaches often fall short. As mainstream LGBTQIA+ organizations amass wealth, the most vulnerable in our community — Black and Brown trans folks, unhoused queer youth, disabled queer individuals, and those living under oppressive legal systems — are left behind.This episode announces a groundbreaking new initiative: A Mutual Aid Collective for LGBTQIA+ Health & Wellbeing. This network will provide access to holistic health resources, guidance, and sliding-scale services for queer and trans individuals, especially those living in states where affirming healthcare is under attack.Listen now to learn about our community's resilience, why we need to reclaim the true spirit of mutual aid, and how you can support this collective effort. Remember, our survival is the ultimate act of resistance.Key Topics Covered:The historical precedent of mutual aid within the LGBTQIA+ community.The distinction between charity and solidarity.Criticism of mainstream LGBTQIA+ organizations hoarding wealth.Introduction of the Mutual Aid Collective for LGBTQIA+ Health & Wellbeing.How listeners can support or access this new initiative.Calls for community-based, nonhierarchical, direct aid over bureaucratic charity models.Call to Action:✨ Get Involved: If you're in a position to contribute financially, share skills, or simply spread the word, support the Mutual Aid Collective by visiting here - https://opencollective.com/queer-trans-thriving 

Now that the dust has settled it turns out that Assassin’s Creed Shadows is a really really good game. We also talk about some NMS new update news, Dun Awakening pricing, and a bit of WoW Hardcord DDOS drama. That and more on this episode of the New Overlords Podcast with Sema and @MaxTheGrey. MP3 … New Overlords Podcast 552: Assassin’s Creed Shadows Read More » The post New Overlords Podcast 552: Assassin's Creed Shadows first appeared on NEW OVERLORDS.

This week we talk about the NMS expedition and the new planet tech Hello Games has added. Then we catch up on what’s know and not known about Light No Fire, how the NMS stuff figures in, and what we hope for. That and more on this episode of the New Overlords Podcast with Sema … New Overlords Podcast 547: No Man’s Sky and Light No Fire Read More » The post New Overlords Podcast 547: No Man's Sky and Light No Fire first appeared on NEW OVERLORDS.

Trans On The Road In The Deep SouthContent Warning:This episode includes discussions of sensitive topics, including sexual abuse, especially between time stamps 13:30-19:00. Listener discretion is advised.Description: In this heartfelt episode of Queer Story Time, we're joined by Terrance, a 71-year-old trans man, to explore what activism can look like at different stages of life. Terrance shares his unique perspective on creating "safe harbors" for others and the role this plays in fostering community and healing. His story is a reminder that activism isn't limited to marching in the streets—it can take many forms, all equally valid and impactful.We also dive into the importance of mindfulness, yoga, and other inward practices that allow us to break down societal norms and assumptions around gender and sexuality. By committing to personal growth, we can create positive change both within ourselves and in the world.Highlights of this episode include:Terrance's reflections on activism and why providing a safe space is his way of contributing.The need for all of us to do inward work to challenge societal expectations and heal from harmful norms.A discussion on the evolving nature of activism and holding space for queer and trans elders who've already fought many battles.A preview of upcoming episodes that will continue exploring conversion therapy and queer & trans healing.Big Announcements:Queer Story Time is transitioning off Meta platforms (Facebook, Instagram, Threads) and moving to decentralized social media like: Mastodon- @futuredrstevie@mastodon.socialPixelFed- @futuredoctorstevieLoops by PixelFed- @futuredoctorstevieCome get a FREE copy of “Your Complete Checklist to Achieving Optimal Health as an LGBTQIA+ Person” by joining the QST Newsletter/Mailing List Join the new Queer Story Time Community Hub on Patreon for early access to episodes, exclusive content, and monthly Zoom gatherings. Paid tiers start at just $5/month.Subscribe to the Queer Story Time YouTube channel for the new QST Reacts series, featuring Stevie's take on LGBTQIA+ topics in short, impactful videos.Ways to Support the Podcast:Subscribe and share this episode with friends and family.Follow us on decentralized social platforms and join the movement for safer, more inclusive online spaces.Tune in to Episode 21, where we continue the conversation on conversion therapy with two experts in the field. Until then, thank you for being part of the Queer Story Time community. Together, we're building a brave space for queer and trans stories to be heard.Connect with Your Host Stevie: QueerStorytimeThePodcast@gmail.com  Leave A Star Rating, Written Review, & Follow QST Podcast: Stevie encourages QST listeners to leave a star rating, and a written review on the podcast platform of your choice and to share the podcast with friends and family! This helps QST expand to an even bigger audience globally. Host: Stevie Inghram, M.S., YT, AWC, NMS-4 (She/They)Support this podcast at — https://redcircle.com/queer-story-time-the-podcast/donations

*Podporte podcast Dobré ráno v aplikácii Toldo na ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠sme.sk/extradobrerano⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Luxusná dovolenka, návšteva u Putina aj spor s Ukrajinou o tranzit plyn dokázali čosi nové: spojili opozíciu, a to vrátane hnutia Slovensko Igora Matoviča či Demokratov. Do toho sa objavil prieskum od NMS, ktorý naznačuje, že opozícia by dokázala poskladať koalíciu - ale tiež s Matovičom. Aké sú tam teda vzťahy a prečo? Tomáš Prokopčák sa v podcaste Dobré ráno pýta Petra Tkačenka. Zdroj zvukov: TA3, Facebook/Igor Matovič Odporúčanie: Dnes odporúčam text nášho kolegu Jána Krempaského Netúžila po pozornosti, hoci nás všetkých prevyšovala. Jedna z obetí štvrtkového útoku na gymnáziu v Spišskej Starej Vsi Mária Semančíková, zástupkyňa riaditeľky bola totiž Jankova spolužiačka. Vo veľmi úprimnom, osobnom a emotívnom texte tak spomína na to, akým bola človekom. – Všetky podcasty denníka SME nájdete na⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ sme.sk/podcasty⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ – Odoberajte aj audio verziu denného newslettra ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ SME.sk⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ s najdôležitejšími správami na⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ sme.sk/brifing⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

In this episode of Queer Story Time, Prince Manvendra Singh Gohil and HH Prince DeAndre, Duke of Hanumanteshwar, share their personal journeys as passionate advocates for LGBTQ+ rights. They discuss their struggles with conversion therapy, the fight for marriage equality in India, and the creation of an LGBTQ+ community campus to support and empower our community. They both offer powerful insights into the harm of conversion practices and emphasize the need for change in both medical and legal systems. They also highlight the ongoing progress in understanding gender and sexuality, and how their work through retreats, activism, and community-building helps queer and trans youth find their voice and power.Prince Manvendra Singh Gohil, the Crown Prince of Rajpipla, hails from the 650-year-old Gohil Dynasty and made history as the first Indian royal to publicly come out as gay. A global icon in the fight for LGBTQ+ rights, Prince Manvendra is the chairperson and co-founder of Lakshya Trust, which serves to empower the LGBTQ+ community in India. He has been featured on platforms such as Oprah Winfrey and Keeping Up with the Kardashians. A passionate advocate for HIV awareness, he serves as the Brand Ambassador for the AIDS Healthcare Foundation India Cares. Currently, Prince Manvendra is spearheading the development of an LGBTQIA+ community campus in India, a revolutionary project aimed at social and financial empowerment for LGBTQ+ individuals.Joining him is HH Prince DeAndre, the Duke of Hanumanteshwar. An esteemed author and LGBTQ+ activist, Prince DeAndre is the Creative Director of H1927LLC, blending fashion with philanthropy through the "Fashion for a Cause" initiative. He co-authored the memoir A Royal Commitment: Ten Years of Marriage and Activism with Prince Manvendra, documenting their powerful journey of love and advocacy. Prince DeAndre's leadership in wellness is evident through his exclusive retreats, which merge yoga, cultural experiences, and direct engagement with royalty. His personal story of resilience, especially as he navigates life with Spondyloarthritis and hidden disabilities, serves as an inspiring reminder of the strength found in transformation and activism.Together, Prince Manvendra and Prince DeAndre continue to break boundaries and advocate for equality, sharing their insights and experiences in this inspiring episode.Key Topics Covered: • The lasting impacts of conversion therapy and the ongoing fight for marriage equality in India• Advocacy work and the creation of an LGBTQ+ community campus in India• Stories of strength, resilience, and the importance of living authentically• How societal pressures and religious beliefs influence family dynamics and harm LGBTQ+ individuals• The role of spiritual practices like yoga in healing and self-discovery for the LGBTQ+ community• The intersection of personal and political journeys: fighting for rights while living authentically• Activism in both the U.S. and India, and how the two worlds intersectSpecial Mentions:A new line of gender-fluid swimsuits and underwearUpcoming yoga retreats and spiritual gatherings that focus on queer wellnessThe importance of listening to queer and trans elders for wisdom and guidanceGuest Info: Our guests include activists and creators of significant change within the LGBTQ+ community. Stay connected with them on social media:Instagram:@princemanvendragohil@duke.hanumanteshwar@haumanteshwar1927tmConnect with Your Host Stevie: QueerStorytimeThePodcast@gmail.com  Join the QST Community Facebook Group: Come connect with our vibrant community here, it's free to join!  Facebook Group: https://www.facebook.com/share/JCiyGgCnpX7gPbfU/?mibextid=K35XfQueer Story Time Email List: Stay updated with QST episodes, and special news, events, and future opportunities Email List Sign-Up: http://eepurl.com/iSc-HQLeave A Star Rating, Written Review, & Follow QST Podcast: I encourage QST listeners to leave a star rating, and a written review on the podcast platform of your choice and to share the podcast with friends and family! This helps QST expand to an even bigger audience globally.Be sure to follow your host Stevie on Instagram @queertransthriving and the QST YouTube Channel: https://www.youtube.com/channel/UCsV_UVohIXCZkSXExp8aYkA  Support QST & Buy Me A Coffee:If you'd like to support Stevis as your QST host, please consider buying me a coffee at this link and check-out my additional offerings: https://buymeacoffee.com/queertransthriving  Get In-Touch with Stevie via E-Mail: queerstorytimethepodcast@gmail.comHost: Stevie Inghram, M.S., YT, AWC, NMS-4 (they/them or she/her)Support this podcast at — https://redcircle.com/queer-story-time-the-podcast/donations

In this deeply heartfelt and insightful episode, we sit down with Dr. Lulu a queer Nigerian-born pediatrician, former Lt. Col. in the U.S. Air Force, and a mom of a transgender daughter. Her mission centers on youth suicide prevention, particularly among Black gender-diverse youth, and she offers gender-affirming coaching through her practice, Dr. Lulu's PRIDE Corner. Recognized for her advocacy, she has received multiple awards, including the 2021 San Antonio LGBT Chamber Youth Advocate of the Year and the Atlanta Trans Life Award's Pioneer of the Year. In this conversation we explore themes of radical self-love, parenting queer & trans children, and the interconnectedness of community in supporting gender and sexually expansive individuals. Dr. Lulu shares her personal journey, the inspiration behind her work, and actionable insights for parents, educators, and allies alike.Dr. Lulu emphasizes why the future is queer and why inward work is essential to building a more inclusive and affirming world. From asking, "What if my child is queer?" to the transformative power of radical self-belief, this episode is a must-listen for anyone seeking to expand their understanding and support for the LGBTQIA+ community.Key Highlights:Dr. Lulu's journey to radical self-love and acceptance.The importance of unlearning biases and relearning acceptance.Addressing societal fears around queerness: "What if my child is queer?"The significance of creating affirming spaces for queer/trans youth.The power of the village: collective responsibility in raising and saving children.Dr. Lulu's work with her nonprofit, Lulu's Angels Haven Inc, providing safe spaces for Black queer youth.Insights for parents and allies on building supportive environments.Dr. Lulu's upcoming books and ongoing advocacy work.Where to Find Dr. Lulu:Website: www.dr-lulu.comInstagram: @themomatricianFacebook: Dr. Lulu Angels HavenNonprofit: Dr. Lulu's Angels Haven Inc.Podcast: Moms 4 Trans KidsConnect with Your Host Stevie: QueerStorytimeThePodcast@gmail.com  Join the QST Community Facebook Group: Come connect with our vibrant community here, it's free to join!  Facebook Group: https://www.facebook.com/share/JCiyGgCnpX7gPbfU/?mibextid=K35XfQueer Story Time Email List: Stay updated with QST episodes, and special news, events, and future opportunities Email List Sign-Up: http://eepurl.com/iSc-HQLeave A Star Rating, Written Review, & Follow QST Podcast: I encourage QST listeners to leave a star rating, and a written review on the podcast platform of your choice and to share the podcast with friends and family! This helps QST expand to an even bigger audience globally.Be sure to follow your host Stevie on Instagram @queertransthriving and the QST YouTube Channel: https://www.youtube.com/channel/UCsV_UVohIXCZkSXExp8aYkA  Support QST & Buy Me A Coffee:If you'd like to support Stevis as your QST host, please consider buying me a coffee at this link and check-out my additional offerings: https://buymeacoffee.com/queertransthriving  Get In-Touch with Stevie via E-Mail: queerstorytimethepodcast@gmail.comHost: Stevie Inghram, M.S., YT, AWC, NMS-4 (they/them or she/her)Support this podcast at — https://redcircle.com/queer-story-time-the-podcast/donations

Happy holidays! We'll be sharing snippets from Latent Space LIVE! through the break bringing you the best of 2024! We want to express our deepest appreciation to event sponsors AWS, Daylight Computer, Thoth.ai, StrongCompute, Notable Capital, and most of all all our LS supporters who helped fund the gorgeous venue and A/V production!For NeurIPS last year we did our standard conference podcast coverage interviewing selected papers (that we have now also done for ICLR and ICML), however we felt that we could be doing more to help AI Engineers 1) get more industry-relevant content, and 2) recap 2024 year in review from experts. As a result, we organized the first Latent Space LIVE!, our first in person miniconference, at NeurIPS 2024 in Vancouver.The single most requested domain was computer vision, and we could think of no one better to help us recap 2024 than our friends at Roboflow, who was one of our earliest guests in 2023 and had one of this year's top episodes in 2024 again. Roboflow has since raised a $40m Series B!LinksTheir slides are here:All the trends and papers they picked:* Isaac Robinson* Sora (see our Video Diffusion pod) - extending diffusion from images to video* SAM 2: Segment Anything in Images and Videos (see our SAM2 pod) - extending prompted masks to full video object segmentation* DETR Dominancy: DETRs show Pareto improvement over YOLOs* RT-DETR: DETRs Beat YOLOs on Real-time Object Detection* LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection* D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement* Peter Robicheaux* MMVP (Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs)* * Florence 2 (Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks) * PalíGemma / PaliGemma 2* PaliGemma: A versatile 3B VLM for transfer* PaliGemma 2: A Family of Versatile VLMs for Transfer* AlMv2 (Multimodal Autoregressive Pre-training of Large Vision Encoders) * Vik Korrapati - MoondreamFull Talk on YouTubeWant more content like this? Like and subscribe to stay updated on our latest talks, interviews, and podcasts.Transcript/Timestamps[00:00:00] Intro[00:00:05] AI Charlie: welcome to Latent Space Live, our first mini conference held at NeurIPS 2024 in Vancouver. This is Charlie, your AI co host. When we were thinking of ways to add value to our academic conference coverage, we realized that there was a lack of good talks, just recapping the best of 2024, going domain by domain.[00:00:36] AI Charlie: We sent out a survey to the over 900 of you. who told us what you wanted, and then invited the best speakers in the Latent Space Network to cover each field. 200 of you joined us in person throughout the day, with over 2, 200 watching live online. Our second featured keynote is The Best of Vision 2024, with Peter Robichaud and Isaac [00:01:00] Robinson of Roboflow, with a special appearance from Vic Corrapati of Moondream.[00:01:05] AI Charlie: When we did a poll of our attendees, the highest interest domain of the year was vision. And so our first port of call was our friends at Roboflow. Joseph Nelson helped us kickstart our vision coverage in episode 7 last year, and this year came back as a guest host with Nikki Ravey of Meta to cover segment Anything 2.[00:01:25] AI Charlie: Roboflow have consistently been the leaders in open source vision models and tooling. With their SuperVision library recently eclipsing PyTorch's Vision library. And Roboflow Universe hosting hundreds of thousands of open source vision datasets and models. They have since announced a 40 million Series B led by Google Ventures.[00:01:46] AI Charlie: Woohoo.[00:01:48] Isaac's picks[00:01:48] Isaac Robinson: Hi, we're Isaac and Peter from Roboflow, and we're going to talk about the best papers of 2024 in computer vision. So, for us, we defined best as what made [00:02:00] the biggest shifts in the space. And to determine that, we looked at what are some major trends that happened and what papers most contributed to those trends.[00:02:09] Isaac Robinson: So I'm going to talk about a couple trends, Peter's going to talk about a trend, And then we're going to hand it off to Moondream. So, the trends that I'm interested in talking about are These are a major transition from models that run on per image basis to models that run using the same basic ideas on video.[00:02:28] Isaac Robinson: And then also how debtors are starting to take over the real time object detection scene from the YOLOs, which have been dominant for years.[00:02:37] Sora, OpenSora and Video Vision vs Generation[00:02:37] Isaac Robinson: So as a highlight we're going to talk about Sora, which from my perspective is the biggest paper of 2024, even though it came out in February. Is the what?[00:02:48] Isaac Robinson: Yeah. Yeah. So just it's a, SORA is just a a post. So I'm going to fill it in with details from replication efforts, including open SORA and related work, such as a stable [00:03:00] diffusion video. And then we're also going to talk about SAM2, which applies the SAM strategy to video. And then how debtors, These are the improvements in 2024 to debtors that are making them a Pareto improvement to YOLO based models.[00:03:15] Isaac Robinson: So to start this off, we're going to talk about the state of the art of video generation at the end of 2023, MagVIT MagVIT is a discrete token, video tokenizer akin to VQ, GAN, but applied to video sequences. And it actually outperforms state of the art handcrafted video compression frameworks.[00:03:38] Isaac Robinson: In terms of the bit rate versus human preference for quality and videos generated by autoregressing on these discrete tokens generate some pretty nice stuff, but up to like five seconds length and, you know, not super detailed. And then suddenly a few months later we have this, which when I saw it, it was totally mind blowing to me.[00:03:59] Isaac Robinson: 1080p, [00:04:00] a whole minute long. We've got light reflecting in puddles. That's reflective. Reminds me of those RTX demonstrations for next generation video games, such as Cyberpunk, but with better graphics. You can see some issues in the background if you look closely, but they're kind of, as with a lot of these models, the issues tend to be things that people aren't going to pay attention to unless they're looking for.[00:04:24] Isaac Robinson: In the same way that like six fingers on a hand. You're not going to notice is a giveaway unless you're looking for it. So yeah, as we said, SORA does not have a paper. So we're going to be filling it in with context from the rest of the computer vision scene attempting to replicate these efforts. So the first step, you have an LLM caption, a huge amount of videos.[00:04:48] Isaac Robinson: This, this is a trick that they introduced in Dolly 3, where they train a image captioning model to just generate very high quality captions for a huge corpus and then train a diffusion model [00:05:00] on that. Their Sora and their application efforts also show a bunch of other steps that are necessary for good video generation.[00:05:09] Isaac Robinson: Including filtering by aesthetic score and filtering by making sure the videos have enough motion. So they're not just like kind of the generators not learning to just generate static frames. So. Then we encode our video into a series of space time latents. Once again, SORA, very sparse in details.[00:05:29] Isaac Robinson: So the replication related works, OpenSORA actually uses a MAG VIT V2 itself to do this, but swapping out the discretization step with a classic VAE autoencoder framework. They show that there's a lot of benefit from getting the temporal compression, which makes a lot of sense as the Each sequential frames and videos have mostly redundant information.[00:05:53] Isaac Robinson: So by compressing against, compressing in the temporal space, you allow the latent to hold [00:06:00] a lot more semantic information while avoiding that duplicate. So, we've got our spacetime latents. Possibly via, there's some 3D VAE, presumably a MAG VATV2 and then you throw it into a diffusion transformer.[00:06:19] Isaac Robinson: So I think it's personally interesting to note that OpenSORA is using a MAG VATV2, which originally used an autoregressive transformer decoder to model the latent space, but is now using a diffusion diffusion transformer. So it's still a transformer happening. Just the question is like, is it?[00:06:37] Isaac Robinson: Parameterizing the stochastic differential equation is, or parameterizing a conditional distribution via autoregression. It's also it's also worth noting that most diffusion models today, the, the very high performance ones are switching away from the classic, like DDPM denoising diffusion probability modeling framework to rectified flows.[00:06:57] Isaac Robinson: Rectified flows have a very interesting property that as [00:07:00] they converge, they actually get closer to being able to be sampled with a single step. Which means that in practice, you can actually generate high quality samples much faster. Major problem of DDPM and related models for the past four years is just that they require many, many steps to generate high quality samples.[00:07:22] Isaac Robinson: So, and naturally, the third step is throwing lots of compute at the problem. So I didn't, I never figured out how to manage to get this video to loop, but we see very little compute, medium compute, lots of compute. This is so interesting because the the original diffusion transformer paper from Facebook actually showed that, in fact, the specific hyperparameters of the transformer didn't really matter that much.[00:07:48] Isaac Robinson: What mattered was that you were just increasing the amount of compute that the model had. So, I love how in the, once again, little blog posts, they don't even talk about [00:08:00] like the specific hyperparameters. They say, we're using a diffusion transformer, and we're just throwing more compute at it, and this is what happens.[00:08:08] Isaac Robinson: OpenSora shows similar results. The primary issue I think here is that no one else has 32x compute budget. So we end up with these we end up in the middle of the domain and most of the related work, which is still super, super cool. It's just a little disappointing considering the context. So I think this is a beautiful extension of the framework that was introduced in 22 and 23 for these very high quality per image generation and then extending that to videos.[00:08:39] Isaac Robinson: It's awesome. And it's GA as of Monday, except no one can seem to get access to it because they keep shutting down the login.[00:08:46] SAM and SAM2[00:08:46] Isaac Robinson: The next, so next paper I wanted to talk about is SAM. So we at Roboflow allow users to label data and train models on that data. Sam, for us, has saved our users 75 years of [00:09:00] labeling time.[00:09:00] Isaac Robinson: We are the, to the best of my knowledge, the largest SAM API that exists. We also, SAM also allows us to have our users train just pure bounding box regression models and use those to generate high quality masks which has the great side effect of requiring less training data to have a meaningful convergence.[00:09:20] Isaac Robinson: So most people are data limited in the real world. So anything that requires less data to get to a useful thing is that super useful. Most of our users actually run their object per frame object detectors on every frame in a video, or maybe not most, but many, many. And so Sam follows into this category of taking, Sam 2 falls into this category of taking something that really really works and applying it to a video which has the wonderful benefit of being plug and play with most of our Many of our users use cases.[00:09:53] Isaac Robinson: We're, we're still building out a sufficiently mature pipeline to take advantage of that, but it's, it's in the works. [00:10:00] So here we've got a great example. We can click on cells and then follow them. You even notice the cell goes away and comes back and we can still keep track of it which is very challenging for existing object trackers.[00:10:14] Isaac Robinson: High level overview of how SAM2 works. We there's a simple pipeline here where we can give, provide some type of prompt and it fills out the rest of the likely masks for that object throughout the rest of the video. So here we're giving a bounding box in the first frame, a set of positive negative points, or even just a simple mask.[00:10:36] Isaac Robinson: I'm going to assume people are somewhat familiar with SAM. So I'm going to just give a high level overview of how SAM works. You have an image encoder that runs on every frame. SAM two can be used on a single image, in which case the only difference between SAM two and SAM is that image encoder, which Sam used a standard VIT [00:11:00] Sam two replaced that with a hara hierarchical encoder, which gets approximately the same results, but leads to a six times faster inference, which is.[00:11:11] Isaac Robinson: Excellent, especially considering how in a trend of 23 was replacing the VAT with more efficient backbones. In the case where you're doing video segmentation, the difference is that you actually create a memory bank and you cross attend the features from the image encoder based on the memory bank.[00:11:31] Isaac Robinson: So the feature set that is created is essentially well, I'll go more into it in a couple of slides, but we take the features from the past couple frames, plus a set of object pointers and the set of prompts and use that to generate our new masks. Then we then fuse the new masks for this frame with the.[00:11:57] Isaac Robinson: Image features and add that to the memory bank. [00:12:00] It's, well, I'll say more in a minute. The just like SAM, the SAM2 actually uses a data engine to create its data set in that people are, they assembled a huge amount of reference data, used people to label some of it and train the model used the model to label more of it and asked people to refine the predictions of the model.[00:12:20] Isaac Robinson: And then ultimately the data set is just created from the engine Final output of the model on the reference data. It's very interesting. This paradigm is so interesting to me because it unifies a model in a dataset in a way that is very unique. It seems unlikely that another model could come in and have such a tight.[00:12:37] Isaac Robinson: So brief overview of how the memory bank works, the paper did not have a great visual, so I'm just, I'm going to fill in a bit more. So we take the last couple of frames from our video. And we take the last couple of frames from our video attend that, along with the set of prompts that we provided, they could come from the future, [00:13:00] they could come from anywhere in the video, as well as reference object pointers, saying, by the way, here's what we've found so far attending to the last few frames has the interesting benefit of allowing it to model complex object motion without actually[00:13:18] Isaac Robinson: By limiting the amount of frames that you attend to, you manage to keep the model running in real time. This is such an interesting topic for me because one would assume that attending to all of the frames is super essential, or having some type of summarization of all the frames is super essential for high performance.[00:13:35] Isaac Robinson: But we see in their later ablation that that actually is not the case. So here, just to make sure that there is some benchmarking happening, we just compared to some of the stuff that's came out prior, and indeed the SAM2 strategy does improve on the state of the art. This ablation deep in their dependencies was super interesting to me.[00:13:59] Isaac Robinson: [00:14:00] We see in section C, the number of memories. One would assume that increasing the count of memories would meaningfully increase performance. And we see that it has some impact, but not the type that you'd expect. And that it meaningfully decreases speed, which justifies, in my mind, just having this FIFO queue of memories.[00:14:20] Isaac Robinson: Although in the future, I'm super interested to see A more dedicated summarization of all of the last video, not just a stacking of the last frames. So that another extension of beautiful per frame work into the video domain.[00:14:42] Realtime detection: DETRs > YOLO[00:14:42] Isaac Robinson: The next trend I'm interested in talking about is this interesting at RoboFlow, we're super interested in training real time object detectors.[00:14:50] Isaac Robinson: Those are bread and butter. And so we're doing a lot to keep track of what is actually happening in that space. We are finally starting to see something change. So, [00:15:00] for years, YOLOs have been the dominant way of doing real time object detection, and we can see here that they've essentially stagnated.[00:15:08] Isaac Robinson: The performance between 10 and 11 is not meaningfully different, at least, you know, in this type of high level chart. And even from the last couple series, there's not. A major change so YOLOs have hit a plateau, debtors have not. So we can look here and see the YOLO series has this plateau. And then these RT debtor, LW debtor, and Define have meaningfully changed that plateau so that in fact, the best Define models are plus 4.[00:15:43] Isaac Robinson: 6 AP on Cocoa at the same latency. So three major steps to accomplish this. The first RT deditor, which is technically a 2023 paper preprint, but published officially in 24, so I'm going to include that. I hope that's okay. [00:16:00] That is showed that RT deditor showed that we could actually match or out speed YOLOs.[00:16:04] Isaac Robinson: And then LWdebtor showed that pre training is hugely effective on debtors and much less so on YOLOs. And then DeFine added the types of bells and whistles that we expect from these types, this, this arena. So the major improvements that RTdebtor shows was Taking the multi scale features that debtors typically pass into their encoder and decoupling them into a much more efficient transformer encoder.[00:16:30] Isaac Robinson: The transformer is of course, quadratic complexity. So decreasing the amount of stuff that you pass in at once is super helpful for increasing your runtime or increasing your throughput. So that change basically brought us up to yellow speed and then they do a hardcore analysis on. Benchmarking YOLOs, including the NMS step.[00:16:54] Isaac Robinson: Once you once you include the NMS in the latency calculation, you see that in fact, these debtors [00:17:00] are outperforming, at least this time, the the, the YOLOs that existed. Then LW debtor goes in and suggests that in fact, the frame, the huge boost here is from pre training. So, this is the define line, and this is the define line without pre training.[00:17:19] Isaac Robinson: It's within range, it's still an improvement over the YOLOs, but Really huge boost comes from the benefit of pre training. When YOLOx came out in 2021, they showed that they got much better results by having a much, much longer training time, but they found that when they did that, they actually did not benefit from pre training.[00:17:40] Isaac Robinson: So, you see in this graph from LWdebtor, in fact, YOLOs do have a real benefit from pre training, but it goes away as we increase the training time. Then, the debtors converge much faster. LWdebtor trains for only 50 epochs, RTdebtor is 60 epochs. So, one could assume that, in fact, [00:18:00] the entire extra gain from pre training is that you're not destroying your original weights.[00:18:06] Isaac Robinson: By relying on this long training cycle. And then LWdebtor also shows superior performance to our favorite data set, Roboflow 100 which means that they do better on the real world, not just on Cocoa. Then Define throws all the bells and whistles at it. Yellow models tend to have a lot of very specific complicated loss functions.[00:18:26] Isaac Robinson: This Define brings that into the debtor world and shows consistent improvement on a variety of debtor based frameworks. So bring these all together and we see that suddenly we have almost 60 AP on Cocoa while running in like 10 milliseconds. Huge, huge stuff. So we're spending a lot of time trying to build models that work better with less data and debtors are clearly becoming a promising step in that direction.[00:18:56] Isaac Robinson: The, what we're interested in seeing [00:19:00] from the debtors in this, this trend to next is. Codetter and the models that are currently sitting on the top of the leaderboard for large scale inference scale really well as you switch out the backbone. We're very interested in seeing and having people publish a paper, potentially us, on what happens if you take these real time ones and then throw a Swingy at it.[00:19:23] Isaac Robinson: Like, do we have a Pareto curve that extends from the real time domain all the way up to the super, super slow but high performance domain? We also want to see people benchmarking in RF100 more, because that type of data is what's relevant for most users. And we want to see more pre training, because pre training works now.[00:19:43] Isaac Robinson: It's super cool.[00:19:48] Peter's Picks[00:19:48] Peter Robicheaux: Alright, so, yeah, so in that theme one of the big things that we're focusing on is how do we get more out of our pre trained models. And one of the lenses to look at this is through sort of [00:20:00] this, this new requirement for like, how Fine grained visual details and your representations that are extracted from your foundation model.[00:20:08] Peter Robicheaux: So it's sort of a hook for this Oh, yeah, this is just a list of all the the papers that I'm going to mention I just want to make sure I set an actual paper so you can find it later[00:20:18] MMVP (Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs)[00:20:18] Peter Robicheaux: Yeah, so sort of the big hook here is that I make the claim that LLMs can't see if you go to if you go to Claude or ChatGPT you ask it to see this Watch and tell me what time it is, it fails, right?[00:20:34] Peter Robicheaux: And so you could say, like, maybe, maybe the Like, this is, like, a very classic test of an LLM, but you could say, Okay, maybe this, this image is, like, too zoomed out, And it just, like, it'll do better if we increase the resolution, And it has easier time finding these fine grained features, Like, where the watch hands are pointing.[00:20:53] Peter Robicheaux: Nodice. And you can say, okay, well, maybe the model just doesn't know how to tell time from knowing the position of the hands. But if you actually prompt [00:21:00] it textually, it's very easy for it to tell the time. So this to me is proof that these LLMs literally cannot see the position of the watch hands and it can't see those details.[00:21:08] Peter Robicheaux: So the question is sort of why? And for you anthropic heads out there, cloud fails too. So the, the, my first pick for best paper of 2024 Envision is this MMVP paper, which tries to investigate the Why do LLMs not have the ability to see fine grained details? And so, for instance, it comes up with a lot of images like this, where you ask it a question that seems very visually apparent to us, like, which way is the school bus facing?[00:21:32] Peter Robicheaux: And it gets it wrong, and then, of course, it makes up details to support its wrong claim. And so, the process by which it finds these images is sort of contained in its hypothesis for why it can't. See these details. So it hypothesizes that models that have been initialized with, with Clip as their vision encoder, they don't have fine grained details and the, the features extracted using Clip because Clip sort of doesn't need to find these fine grained [00:22:00] details to do its job correctly, which is just to match captions and images, right?[00:22:04] Peter Robicheaux: And sort of at a high level, even if ChatGPT wasn't initialized with Clip and wasn't trained contrastively at all. The vision encoder wasn't trained contrastively at all. Still, in order to do its job of capturing the image it could do a pretty good job without actually finding the exact position of all the objects and visual features in the image, right?[00:22:21] Peter Robicheaux: So This paper finds a set of difficult images for these types of models. And the way it does it is it looks for embeddings that are similar in clip space, but far in DynaV2 space. So DynaV2 is a foundation model that was trained self supervised purely on image data. And it kind of uses like some complex student teacher framework, but essentially, and like, it patches out like certain areas of the image or like crops with certain areas of the image and tries to make sure that those have consistent representations, which is a way for it to learn very fine grained visual features.[00:22:54] Peter Robicheaux: And so if you take things that are very close in clip space and very far in DynaV2 space, you get a set of images [00:23:00] that Basically, pairs of images that are hard for a chat GPT and other big language models to distinguish. So, if you then ask it questions about this image, well, as you can see from this chart, it's going to answer the same way for both images, right?[00:23:14] Peter Robicheaux: Because to, to, from the perspective of the vision encoder, they're the same image. And so if you ask a question like, how many eyes does this animal have? It answers the same for both. And like all these other models, including Lava do the same thing, right? And so this is the benchmark that they create, which is like finding clip, like clip line pairs, which is pairs of images that are similar in clip space and creating a data set of multiple choice questions based off of those.[00:23:39] Peter Robicheaux: And so how do these models do? Well, really bad. Lava, I think, So, so, chat2BT and Jim and I do a little bit better than random guessing, but, like, half of the performance of humans who find these problems to be very easy. Lava is, interestingly, extremely negatively correlated with this dataset. It does much, much, much, much worse [00:24:00] than random guessing, which means that this process has done a very good job of identifying hard images for, for Lava, specifically.[00:24:07] Peter Robicheaux: And that's because Lava is basically not trained for very long and is initialized from Clip, and so You would expect it to do poorly on this dataset. So, one of the proposed solutions that this paper attempts is by basically saying, Okay, well if clip features aren't enough, What if we train the visual encoder of the language model also on dyno features?[00:24:27] Peter Robicheaux: And so it, it proposes two different ways of doing this. One, additively which is basically interpolating between the two features, and then one is interleaving, which is just kind of like training one on the combination of both features. So there's this really interesting trend when you do the additive mixture of features.[00:24:45] Peter Robicheaux: So zero is all clip features and one is all DynaV2 features. So. It, as you, so I think it's helpful to look at the right most chart first, which is as you increase the number of DynaV2 features, your model does worse and worse and [00:25:00] worse on the actual language modeling task. And that's because DynaV2 features were trained completely from a self supervised manner and completely in image space.[00:25:08] Peter Robicheaux: It knows nothing about text. These features aren't really compatible with these text models. And so you can train an adapter all you want, but it seems that it's in such an alien language that it's like a very hard optimization for this. These models to solve. And so that kind of supports what's happening on the left, which is that, yeah, it gets better at answering these questions if as you include more dyna V two features up to a point, but then you, when you oversaturate, it completely loses its ability to like.[00:25:36] Peter Robicheaux: Answer language and do language tasks. So you can also see with the interleaving, like they essentially double the number of tokens that are going into these models and just train on both, and it still doesn't really solve the MMVP task. It gets Lava 1. 5 above random guessing by a little bit, but it's still not close to ChachiPT or, you know, Any like human performance, obviously.[00:25:59] Peter Robicheaux: [00:26:00] So clearly this proposed solution of just using DynaV2 features directly, isn't going to work. And basically what that means is that as a as a vision foundation model, DynaV2 is going to be insufficient for language tasks, right?[00:26:14] Florence 2 (Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks)[00:26:14] Peter Robicheaux: So my next pick for best paper of 2024 would be Florence 2, which tries to solve this problem by incorporating not only This dimension of spatial hierarchy, which is to say pixel level understanding, but also in making sure to include what they call semantic granularity, which ends up, the goal is basically to have features that are sufficient for finding objects in the image, so they're, they're, they have enough pixel information, but also can be talked about and can be reasoned about.[00:26:44] Peter Robicheaux: And that's on the semantic granularity axis. So here's an example of basically three different paradigms of labeling that they do. So they, they create a big dataset. One is text, which is just captioning. And you would expect a model that's trained [00:27:00] only on captioning to have similar performance like chat2BT and like not have spatial hierarchy, not have features that are meaningful at the pixel level.[00:27:08] Peter Robicheaux: And so they add another type, which is region text pairs, which is essentially either classifying a region or You're doing object detection or doing instance segmentation on that region or captioning that region. And then they have text phrased region annotations, which is essentially a triple. And basically, not only do you have a region that you've described, you also find it's like, It's placed in a descriptive paragraph about the image, which is basically trying to introduce even more like semantic understanding of these regions.[00:27:39] Peter Robicheaux: And so like, for instance, if you're saying a woman riding on the road, right, you have to know what a woman is and what the road is and that she's on top of it. And that's, that's basically composing a bunch of objects in this visual space, but also thinking about it semantically, right? And so the way that they do this is they take basically they just dump Features from a vision encoder [00:28:00] straight into a encoder decoder transformer.[00:28:03] Peter Robicheaux: And then they train a bunch of different tasks like object detection and so on as a language task. And I think that's one of the big things that we saw in 2024 is these, these vision language models operating in, on pixel space linguistically. So they introduced a bunch of new tokens to point to locations and[00:28:22] Peter Robicheaux: So how does it work? How does it actually do? We can see if you look at the graph on the right, which is using the, the Dino, the the Dino framework your, your pre trained Florence 2 models transfer very, very well. They get 60%, 60 percent map on Cocoa, which is like approaching state of the art and they train[00:28:42] Vik Korrapati: with, and they[00:28:43] Peter Robicheaux: train with a much more more efficiently.[00:28:47] Peter Robicheaux: So they, they converge a lot faster, which both of these things are pointing to the fact that they're actually leveraging their pre trained weights effectively. So where is it falling short? So these models, I forgot to mention, Florence is a 0. 2 [00:29:00] billion and a 0. 7 billion parameter count. So they're very, very small in terms of being a language model.[00:29:05] Peter Robicheaux: And I think that. This framework, you can see saturation. So, what this graph is showing is that if you train a Florence 2 model purely on the image level and region level annotations and not including the pixel level annotations, like this, segmentation, it actually performs better as an object detector.[00:29:25] Peter Robicheaux: And what that means is that it's not able to actually learn all the visual tasks that it's trying to learn because it doesn't have enough capacity.[00:29:32] PalíGemma / PaliGemma 2[00:29:32] Peter Robicheaux: So I'd like to see this paper explore larger model sizes, which brings us to our next big paper of 2024 or two papers. So PolyGemma came out earlier this year.[00:29:42] Peter Robicheaux: PolyGemma 2 was released, I think like a week or two ago. Oh, I forgot to mention, you can actually train You can, like, label text datasets on RoboFlow and you can train a Florence 2 model and you can actually train a PolyGemma 2 model on RoboFlow, which we got into the platform within, like, 14 hours of release, which I was really excited about.[00:29:59] Peter Robicheaux: So, anyway, so [00:30:00] PolyGemma 2, so PolyGemma is essentially doing the same thing, but instead of doing an encoder decoder, it just dumps everything into a decoder only transformer model. But it also introduced the concept of location tokens to point to objects in pixel space. PolyGemma 2, so PolyGemma uses Gemma as the language encoder, and it uses Gemma2B.[00:30:17] Peter Robicheaux: PolyGemma 2 introduces using multiple different sizes of language encoders. So, the way that they sort of get around having to do encoder decoder is they use the concept of prefix loss. Which basically means that when it's generating, tokens autoregressively, it's all those tokens in the prefix, which is like the image that it's looking at and like a description of the task that it's trying to do.[00:30:41] Peter Robicheaux: They're attending to each other fully, full attention. Which means that, you know, it can sort of. Find high level it's easier for the, the prefix to color, to color the output of the suffix and also to just find like features easily. So this is sort of [00:31:00] an example of like one of the tasks that was trained on, which is like, you describe the task in English and then you give it all these, like, You're asking for it to segment these two classes of objects, and then it finds, like, their locations using these tokens, and it finds their masks using some encoding of the masks into tokens.[00:31:24] Peter Robicheaux: And, yeah, so, one of my critiques, I guess, of PolyGemma 1, at least, is that You find that performance saturates as a pre trained model after only 300 million examples seen. So, what this graph is representing is each blue dot is a performance on some downstream task. And you can see that after seeing 300 million examples, It sort of does equally well on all of the downtrend tasks that they tried it on, which was a lot as 1 billion examples, which to me also kind of suggests a lack of capacity for this model.[00:31:58] Peter Robicheaux: PolyGemma2, [00:32:00] you can see the results on object detection. So these were transferred to to Coco. And you can see that this sort of also points to an increase in capacity being helpful to the model. You can see as. Both the resolution increases, and the parameter count of the language model increases, performance increases.[00:32:16] Peter Robicheaux: So resolution makes sense, obviously, it helps to find small images, or small objects in the image. But it also makes sense for another reason, which is that it kind of gives the model a thinking register, and it gives it more tokens to, like, process when making its predictions. But yeah, you could, you could say, oh, 43.[00:32:30] Peter Robicheaux: 6, that's not that great, like Florence 2 got 60. But this is not Training a dino or a debtor on top of this language or this image encoder. It's doing the raw language modeling task on Cocoa. So it doesn't have any of the bells and whistles. It doesn't have any of the fancy losses. It doesn't even have bipartite graph matching or anything like that.[00:32:52] Peter Robicheaux: Okay, the big result and one of the reasons that I was really excited about this paper is that they blow everything else away [00:33:00] on MMVP. I mean, 47. 3, sure, that's nowhere near human accuracy, which, again, is 94%, but for a, you know, a 2 billion language, 2 billion parameter language model to be chat2BT, that's quite the achievement.[00:33:12] Peter Robicheaux: And that sort of brings us to our final pick for paper of the year, which is AIMV2. So, AIMV2 sort of says, okay, Maybe this language model, like, maybe coming up with all these specific annotations to find features and with high fidelity and pixel space isn't actually necessary. And we can come up with an even simpler, more beautiful idea for combining you know, image tokens and pixel tokens in a way that's interfaceable for language tasks.[00:33:44] Peter Robicheaux: And this is nice because it can scale, you can come up with lots more data if you don't have to come up with all these annotations, right? So the way that it works. is it does something very, very similar to PolyGemo, where you have a vision encoder that dumps image tokens into a decoder only transformer.[00:33:59] Peter Robicheaux: But [00:34:00] the interesting thing is that it also autoregressively tries to learn the mean squared error of the image tokens. So instead of having to come up with fancy object detection or semantic, or segment, or segmentation labels, you can just try to reconstruct the image and have it learn fine grained features that way.[00:34:16] Peter Robicheaux: And it does this in kind of, I think, a beautiful way that's kind of compatible with the PolyGemma line of thinking, which is randomly sampling a prefix line of thinking Prefix length and using only this number of image tokens as the prefix. And so doing a similar thing with the causal. So the causal with prefix is the, the attention mask on the right.[00:34:35] Peter Robicheaux: So it's doing full block attention with some randomly sampled number of image tokens to then reconstruct the rest of the image and the downstream caption for that image. And so, This is the dataset that they train on. It's image or internet scale data, very high quality data created by the data filtering networks paper, essentially which is maybe The best clip data that exists.[00:34:59] Peter Robicheaux: [00:35:00] And we can see that this is finally a model that doesn't saturate. It's even at the highest parameter count, it's, it appears to be, oh, at the highest parameter account, it appears to be improving in performance with more and more samples seen. And so you can sort of think that. You know, if we just keep bumping the parameter count and increasing the example scene, which is the, the, the line of thinking for language models, then it'll keep getting better.[00:35:27] Peter Robicheaux: So how does it actually do at finding, oh, it also improves with resolution, which you would expect for a model that This is the ImageNet classification accuracy, but yeah, it does better if you increase the resolution, which means that it's actually leveraging and finding fine grained visual features.[00:35:44] Peter Robicheaux: And so how does that actually do compared to CLIP on Cocoa? Well, you can see that if you slap a transformer detection head on it, Entry now in Cocoa, it's just 60. 2, which is also within spitting distance of Soda, which means that it does a very good job of [00:36:00] finding visual features, but you could say, okay, well, wait a second.[00:36:03] Peter Robicheaux: Clip got to 59. 1, so. Like, how does this prove your claim at all? Because doesn't that mean like clip, which is known to be clip blind and do badly on MMVP, it's able to achieve a very high performance on fine, on this fine grained visual features task of object detection, well, they train on like, Tons of data.[00:36:24] Peter Robicheaux: They train on like objects, 365, Cocoa, Flickr and everything else. And so I think that this benchmark doesn't do a great job of selling how good of a pre trained model MV2 is. And we would like to see the performance on fewer data as examples and not trained to convergence on object detection. So seeing it in the real world on like a dataset, like RoboFlow 100, I think would be quite interesting.[00:36:48] Peter Robicheaux: And our, our, I guess our final, final pick for paper of 2024 would be Moondream. So introducing Vic to talk about that.[00:36:54] swyx: But overall, that was exactly what I was looking for. Like best of 2024, an amazing job. Yeah, you can, [00:37:00] if there's any other questions while Vic gets set up, like vision stuff,[00:37:07] swyx: yeah,[00:37:11] swyx: Vic, go ahead. Hi,[00:37:13] Vik Korrapati / Moondream[00:37:13] question: well, while we're getting set up, hi, over here, thanks for the really awesome talk. One of the things that's been weird and surprising is that the foundation model companies Even these MLMs, they're just like worse than RT Tether at detection still. Like, if you wanted to pay a bunch of money to auto label your detection dataset, If you gave it to OpenAI or Cloud, that would be like a big waste.[00:37:37] question: So I'm curious, just like, even Pali Gemma 2, like is worse. So, so I'm curious to hear your thoughts on like, how come, Nobody's cracked the code on like a generalist that really you know, beats a specialist model in computer vision like they have in in LLM land.[00:38:00][00:38:01] Isaac Robinson: Okay. It's a very, very interesting question. I think it depends on the specific domain. For image classification, it's basically there. In the, in AIMv2 showed, a simple attentional probe on the pre trained features gets like 90%, which is as well as anyone does. The, the, the, the bigger question, like, why isn't it transferring to object detection, especially like real time object detection.[00:38:25] Isaac Robinson: I think, in my mind, there are two answers. One is, object detection is really, really, really the architectures are super domain specific. You know, we see these, all these super, super complicated things, and it's not super easy to, to, to build something that just transfers naturally like that, whereas image classification, you know, clip pre training transfers super, super quickly.[00:38:48] Isaac Robinson: And the other thing is, until recently, the real time object detectors didn't even really benefit from pre training. Like, you see the YOLOs that are like, essentially saturated, showing very little [00:39:00] difference with pre training improvements, with using pre trained model at all. It's not surprising, necessarily, that People aren't looking at the effects of better and better pre training on real time detection.[00:39:12] Isaac Robinson: Maybe that'll change in the next year. Does that answer your question?[00:39:17] Peter Robicheaux: Can you guys hear me? Yeah, one thing I want to add is just like, or just to summarize, basically, is that like, Until 2024, you know, we haven't really seen a combination of transformer based object detectors and fancy losses, and PolyGemma suffers from the same problem, which is basically to say that these ResNet, or like the convolutional models, they have all these, like, extreme optimizations for doing object detection, but essentially, I think it's kind of been shown now that convolution models like just don't benefit from pre training and just don't like have the level of intelligence of transformer models.[00:39:56] swyx: Awesome. Hi,[00:39:59] Vik Korrapati: can [00:40:00] you hear me?[00:40:01] swyx: Cool. I hear you. See you. Are you sharing your screen?[00:40:04] Vik Korrapati: Hi. Might have forgotten to do that. Let me do[00:40:07] swyx: that. Sorry, should have done[00:40:08] Vik Korrapati: that.[00:40:17] swyx: Here's your screen. Oh, classic. You might have to quit zoom and restart. What? It's fine. We have a capture of your screen.[00:40:34] swyx: So let's get to it.[00:40:35] Vik Korrapati: Okay, easy enough.[00:40:49] Vik Korrapati: All right. Hi, everyone. My name is Vic. I've been working on Moondream for almost a year now. Like Shawn mentioned, I just went and looked and it turns out the first version I released December [00:41:00] 29, 2023. It's been a fascinating journey. So Moonbeam started off as a tiny vision language model. Since then, we've expanded scope a little bit to also try and build some tooling, client libraries, et cetera, to help people really deploy it.[00:41:13] Vik Korrapati: Unlike traditional large models that are focused at assistant type use cases, we're laser focused on building capabilities that developers can, sorry, it's yeah, we're basically focused on building capabilities that developers can use to build vision applications that can run anywhere. So, in a lot of cases for vision more so than for text, you really care about being able to run on the edge, run in real time, etc.[00:41:40] Vik Korrapati: So That's really important. We have we have different output modalities that we support. There's query where you can ask general English questions about an image and get back human like answers. There's captioning, which a lot of our users use for generating synthetic datasets to then train diffusion models and whatnot.[00:41:57] Vik Korrapati: We've done a lot of work to minimize those sessions there. [00:42:00] So that's. Use lot. We have open vocabulary object detection built in similar to a couple of more recent models like Palagem, et cetera, where rather than having to train a dedicated model, you can just say show me soccer balls in this image or show me if there are any deer in this image, it'll detect it.[00:42:14] Vik Korrapati: More recently, earlier this month, we released pointing capability where if all you're interested in is the center of an object you can just ask it to point out where that is. This is very useful when you're doing, you know, I automation type stuff. Let's see, LA we, we have two models out right now.[00:42:33] Vik Korrapati: There's a general purpose to be para model, which runs fair. Like it's, it's it's fine if you're running on server. It's good for our local Amma desktop friends and it can run on flagship, flagship mobile phones, but it never. so much for joining us today, and we'll see you in the [00:43:00] next one. Less memory even with our not yet fully optimized inference client.[00:43:06] Vik Korrapati: So the way we built our 0. 5b model was to start with the 2 billion parameter model and prune it while doing continual training to retain performance. We, our objective during the pruning was to preserve accuracy across a broad set of benchmarks. So the way we went about it was to estimate the importance of different components of the model, like attention heads, channels MLP rows and whatnot using basically a technique based on the gradient.[00:43:37] Vik Korrapati: I'm not sure how much people want to know details. We'll be writing a paper about this, but feel free to grab me if you have more questions. Then we iteratively prune a small chunk that will minimize loss and performance retrain the model to recover performance and bring it back. The 0. 5b we released is more of a proof of concept that this is possible.[00:43:54] Vik Korrapati: I think the thing that's really exciting about this is it makes it possible for for developers to build using the 2B param [00:44:00] model and just explore, build their application, and then once they're ready to deploy figure out what exactly they need out of the model and prune those capabilities into a smaller form factor that makes sense for their deployment target.[00:44:12] Vik Korrapati: So yeah, very excited about that. Let me talk to you folks a little bit about another problem I've been working on recently, which is similar to the clocks example we've been talking about. We had a customer reach out who was talking about, like, who had a bunch of gauges out in the field. This is very common in manufacturing and oil and gas, where you have a bunch of analog devices that you need to monitor.[00:44:34] Vik Korrapati: It's expensive to. And I was like, okay, let's have humans look at that and monitor stuff and make sure that the system gets shut down when the temperature goes over 80 or something. So I was like, yeah, this seems easy enough. Happy to, happy to help you distill that. Let's, let's get it going. Turns out our model couldn't do it at all.[00:44:51] Vik Korrapati: I went and looked at other open source models to see if I could just generate a bunch of data and learn from that. Did not work either. So I was like, let's look at what the folks with [00:45:00] hundreds of billions of dollars in market cap have to offer. And yeah, that doesn't work either. My hypothesis is that like the, the way these models are trained are using a large amount of image text data scraped from the internet.[00:45:15] Vik Korrapati: And that can be biased. In the case of gauges, most gauge images aren't gauges in the wild, they're product images. Detail images like these, where it's always set to zero. It's paired with an alt text that says something like GIVTO, pressure sensor, PSI, zero to 30 or something. And so the models are fairly good at picking up those details.[00:45:35] Vik Korrapati: It'll tell you that it's a pressure gauge. It'll tell you what the brand is, but it doesn't really learn to pay attention to the needle over there. And so, yeah, that's a gap we need to address. So naturally my mind goes to like, let's use synthetic data to, Solve this problem. That works, but it's problematic because it turned out we needed millions of synthetic gauge images to get to reasonable performance.[00:45:57] Vik Korrapati: And thinking about it, reading a gauge is like [00:46:00] not a one, like it's not a zero short process in our minds, right? Like if you had to tell me the reading in Celsius for this, Real world gauge. There's two dials on there. So first you have to figure out which one you have to be paying attention to, like the inner one or the outer one.[00:46:14] Vik Korrapati: You look at the tip of the needle, you look at what labels it's between, and you count how many and do some math to figure out what that probably is. So what happens if we just add that as a Chain of thought to give the model better understanding of the different sub, to allow the model to better learn the subtasks it needs to perform to accomplish this goal.[00:46:37] Vik Korrapati: So you can see in this example, this was actually generated by the latest version of our model. It's like, okay, Celsius is the inner scale. It's between 50 and 60. There's 10 ticks. So the second tick, it's a little debatable here, like there's a weird shadow situation going on, the dial is off, so I don't know what the ground truth is, but it works okay.[00:46:57] Vik Korrapati: There's points on there that are, the points [00:47:00] over there are actually grounded. I don't know if this is easy to see, but when I click on those, there's a little red dot that moves around on the image. The model actually has to predict where this points are, I was already trying to do this with bounding boxes, but then Malmo came out with pointing capabilities.[00:47:15] Vik Korrapati: And it's like pointing is a much better paradigm to to represent this. We see pretty good results. This one's actually for clock reading. I couldn't find our chart for gauge reading at the last minute. So the light. Blue chart is with our rounded chain of thought. This measures, we have, we built a clock reading benchmark about 500 images.[00:47:37] Vik Korrapati: This measures accuracy on that. You can see it's a lot more sample efficient when you're using the chain of thought to model. Another big benefit from this approach is like, you can kind of understand how the model is. it and how it's failing. So in this example, the actual correct reading is 54 Celsius, the model output [00:48:00] 56, not too bad but you can actually go and see where it messed up. Like it got a lot of these right, except instead of saying it was on the 7th tick, it actually predicted that it was the 8th tick and that's why it went with 56.[00:48:14] Vik Korrapati: So now that you know that this. Failing in this way, you can adjust how you're doing the chain of thought to maybe say like, actually count out each tick from 40, instead of just trying to say it's the eighth tick. Or you might say like, okay, I see that there's that middle thing, I'll count from there instead of all the way from 40.[00:48:31] Vik Korrapati: So helps a ton. The other thing I'm excited about is a few short prompting or test time training with this. Like if a customer has a specific gauge that like we're seeing minor errors on, they can give us a couple of examples where like, if it's miss detecting the. Needle, they can go in and correct that in the chain of thought.[00:48:49] Vik Korrapati: And hopefully that works the next time. Now, exciting approach, we only apply it to clocks and gauges. The real question is, is it going to generalize? Probably, like, there's some science [00:49:00] from text models that when you train on a broad number of tasks, it does generalize. And I'm seeing some science with our model as well.[00:49:05] Vik Korrapati: So, in addition to the image based chain of thought stuff, I also added some spelling based chain of thought to help it understand better understand OCR, I guess. I don't understand why everyone doesn't do this, by the way. Like, it's trivial benchmark question. It's Very, very easy to nail. But I also wanted to support it for stuff like license plate, partial matching, like, hey, does any license plate in this image start with WHA or whatever?[00:49:29] Vik Korrapati: So yeah, that sort of worked. All right, that, that ends my story about the gauges. If you think about what's going on over here it's interesting that like LLMs are showing enormous. Progress in reasoning, especially with the latest set of models that we've seen, but we're not really seeing, I have a feeling that VLMs are lagging behind, as we can see with these tasks that should be very simple for a human to do [00:50:00] that are very easy to find VLMs failing at.[00:50:04] Vik Korrapati: My hypothesis on why this is the case is because On the internet, there's a ton of data that talks about how to reason. There's books about how to solve problems. There's books critiquing the books about how to solve problems. But humans are just so good at perception that we never really talk about it.[00:50:20] Vik Korrapati: Like, maybe in art books where it's like, hey, to show that that mountain is further away, you need to desaturate it a bit or whatever. But the actual data on how to, like, look at images is, isn't really present. Also, the Data we have is kind of sketched. The best source of data we have is like image all text pairs on the internet and that's pretty low quality.[00:50:40] Vik Korrapati: So yeah, I, I think our solution here is really just we need to teach them how to operate on individual tasks and figure out how to scale that out. All right. Yep. So conclusion. At Moondream we're trying to build amazing PLMs that run everywhere. Very hard problem. Much work ahead, but we're making a ton of progress and I'm really excited [00:51:00] about If anyone wants to chat about more technical details about how we're doing this or interest in collaborating, please, please hit me up.[00:51:08] Isaac Robinson: Yeah,[00:51:09] swyx: like, I always, when people say, when people say multi modality, like, you know, I always think about vision as the first among equals in all the modalities. So, I really appreciate having the experts in the room. Get full access to Latent Space at www.latent.space/subscribe

*Podporte podcast Dobré ráno v aplikácii Toldo na ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠sme.sk/extradobrerano⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. – Progresívne Slovensko, za ním už s väčším odstupom Smer. Ale aj Hlas potácajúci sa okolo desiatich percent, nárast extrémistov aj hnutia Igora Matoviča. Minulotýždňový model NMS ukázal viaceré zaujímavé veci. Takže kto, s kým, kedy a za akých podmienok? Tomáš Prokopčák sa v podcaste Dobré ráno pýta analytika Mikuláša Hanesa z NMS. Zdroj zvukov: TASR, YouTube/Republika, Matúš Šutaj Eštok, Facebook/Hnutie Slovensko, Andrej Danko – Všetky podcasty denníka SME nájdete na⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ sme.sk/podcasty⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ – Odoberajte aj audio verziu denného newslettra ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ SME.sk⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ s najdôležitejšími správami na⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ sme.sk/brifing⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

In this Monday show, Patka is going to walk you through two topics. Recent survey by research company NMS showed that over a quarter of Slovaks have faced sexual harassment in public transport. In the second part of the show we re going to talk about how tourism in Slovakia can be more ecological and sustainable.

Contributor: Taylor Lynch, MD Educational Pearls: What is NMS? Neuroleptic Malignant Syndrome Caused by anti-dopamine medication or rapid withdrawal of pro-dopamenergic medications Mechanism is poorly understood Life threatening What medications can cause it? Typical antipsychotics Haloperidol, chlorpromazine, prochlorperazine, fluphenazine, trifluoperazine Atypical antipsychotics Less risk Risperidone, clozapine, quetiapine, olanzapine, aripiprazole, ziprasidone Anti-emetic agents with anti dopamine activity Metoclopramide, promethazine, haloperidol Not ondansetron Abrupt withdrawal of levodopa How does it present? Slowly over 1-3 days (unlike serotonin syndrome which has a more acute onset) Altered mental status, 82% of patients, typically agitated delirium with confusion Peripheral muscle rigidity and decreased reflexes. AKA lead pipe rigidity. (As opposed to clonus and hyperreflexia in serotonin syndrome) Hyperthermia (>38C seen in 87% of patients) Can also have tachycardia, labile blood pressures, tachypnea, and tremor How is it diagnosed? Clinical diagnosis, focus on the timing of symptoms No confirmatory lab test but can see possible elevated CK levels and WBC of 10-40k with a left shift What else might be on the differential? Sepsis CNS infections Heat stroke Agitated delirium Status eptilepticus Drug induced extrapyramidal symptoms Serotonin syndrome Malignant hyperthermia What is the treatment? Start with ABC's Stop all anti-dopaminergic meds and restart pro-dopamine meds if recently stopped Maintain urine output with IV fluids if needed to avoid rhabdomyolysis Active or passive cooling if needed Benzodiazapines, such as lorazepam 1-2 mg IV q 4hrs What are active medical therapies? Controversial treatments Bromocriptine, dopamine agonist Dantrolene, classically used for malignant hyperthermia Amantadine, increases dopamine release Use as a last resort Dispo? Mortality is around 10% if not recognized and treated Most patients recover in 2-14 days Must wait 2 weeks before restarting any medications References Oruch, R., Pryme, I. F., Engelsen, B. A., & Lund, A. (2017). Neuroleptic malignant syndrome: an easily overlooked neurologic emergency. Neuropsychiatric disease and treatment, 13, 161–175. https://doi.org/10.2147/NDT.S118438 Tormoehlen, L. M., & Rusyniak, D. E. (2018). Neuroleptic malignant syndrome and serotonin syndrome. Handbook of clinical neurology, 157, 663–675. https://doi.org/10.1016/B978-0-444-64074-1.00039-2 Velamoor, V. R., Norman, R. M., Caroff, S. N., Mann, S. C., Sullivan, K. A., & Antelo, R. E. (1994). Progression of symptoms in neuroleptic malignant syndrome. The Journal of nervous and mental disease, 182(3), 168–173. https://doi.org/10.1097/00005053-199403000-00007 Ware, M. R., Feller, D. B., & Hall, K. L. (2018). Neuroleptic Malignant Syndrome: Diagnosis and Management. The primary care companion for CNS disorders, 20(1), 17r02185. https://doi.org/10.4088/PCC.17r02185 Summarized by Jeffrey Olson MS2 | Edited by Meg Joyce & Jorge Chalit, OMSIII  

Welcome to the CORE podcast, where we have thoughts on all things gaming, including new Comscore data showing gamers might be caring about ads in games differently than you think. Plus, the Deadpool bum controllers, Diablo and the new Spiritborn class, the new NMS update, and Concord getting some nice pre-release love from players. Plus a souls-like phone call, and more!GAMES PLAYEDSHAREDFF14Elden RingNo Man's Sky updateSCOTTCrab ChampionsBomb Rush CyberFunnkAce Combat 7: Skys UnknownJONSee Shared!BEAUMore ZZZ! (not sleep) Hosted on Acast. See acast.com/privacy for more information.