Podcasts about morphologic

Újra itt a Mérlegen, a hvg.hu üzleti podcastja: ezen a héten Dzindzisz Sztefant nélkülözzük, Csatári Flóra Dóra azonban egy rendkívül izgalmas (és tudományos) interjút hozott Prószéky Gáborral, a Nyelvtudományi Kutatóközpont főigazgatójával és a MorphoLogic cég alapítójával, aki a Természetes Nyelvek Feldolgozásáról, avagy a ChatGPT tudományos alapját is adó technológiáról mesélt, illetve arról, hogyan tudjuk a gépi nyelveszközöket hasznosítani vállalkozóként és átlagemberként. Fizess elő a hvg360-ra, az első hónapban csupán 360 forintért! https://hvg.hu/360/elofizetes Mondd el véleményedet az adásról Spotify-on a műsor alatt, vagy a podcastok@hvg.hu email címen! • 1:20: Mi az a Natural Language Processing? • 6:24: Mit tud a deep learning, amit ezt megelőzően nem tudtak a nyelvi modellek? • 13:23: A Mesterséges Intelligencia csak kiegészíti a valódit? • 14:15: Miért jó nekünk, ha egy gép tud egy emberi nyelvet? • 18:29: A ChatGPT tud magyarul? Ezt ki lehet jelenteni? Miért? • 32:33: Miben tér el a mesterséges intelligencia tudományos és üzleti alkalmazása? Mi motiválja ezt? • 40:25: Az üzleti világban – akár vállalkozóként, akár befektetőként – milyen ajtókat nyitnak jelenleg a nyelvi modellek? • 43:38: Mi az a prompt engineering, ami alig fél éve született, mint szakma? • 48:02: Prószéky Gábor a Morphologic alapítója és 25 évig ügyvezetője volt – hogyan egyensúlyozik a kutatói és vállalkozói pálya között valaki?

In this episode of the IJGC podcast, Editor-in-Chief, Dr. Pedro Ramirez, is joined by Dr. Anil K. Sood to discuss novel high-grade serous ovarian morphologic classification. Dr. Sood is a Professor in the Department of Gynecologic Oncology and Reproductive Medicine at the UT MD Anderson Cancer Center. He is Co-Director of the multi-disciplinary Blanton-Davis Ovarian Cancer Research Program and co-leads the Ovarian Cancer Moonshot Program. He is an elected member of the American Society for Clinical Investigation (ASCI), the Association of American Physicians (AAP), and the National Academy of Medicine (NAM). Highlights: - High grade serous ovarian cancer (HGSOC) could be classified into two gross morphologic subtypes. - Type I and type II morphologic subtypes differed with respect to clinical outcomes. - The two morphologic subtypes also differed with regard to transcriptomic, proteomic, and metabolomic profiles.

Adenoid Ameloblastoma is a very rare benign odontogenic tumor characterized microscopically by epithelium resembling conventional ameloblastoma, with additional duct-like structures, epithelial whorls, and cribriform architecture. Dentinoid deposits, clusters of clear cells, and ghost-cell keratinization may also be present.These tumors do not harbor BRAF or KRAS mutations and their molecular basis appears distinct from conventional ameloblastoma but remains unknown. Dr. Carolina Cavalieri Gomes from the Universidade Federal de Minas Gerais in Brazil, discusses her team's discovery of CTNNB1 (beta-catenin) exon 3 mutations in 4 of 9 primary cases and 2 additional recurrences. While the occasional presence of ghost cells keratinization was the feature that led the team to initially investigate beta-catinin, this feature was present in only 2/6. Furthermore, nuclear beta-catenin immunoexpression (IHC) was found in 7 of 8 tested samples including some with wild type CTNNB1. The findings support the classification of adenoid ameloblastoma as a separate entity, and not as a subtype of ameloblastoma. The use of beta-catenin IHC could help in establishing the diagnosis in challenging cases. Hosted on Acast. See acast.com/privacy for more information.

A subset of clinically benign uterine polyps shows atypical morphologic features worrisome for, but not diagnostic of, Mullerian adenosarcoma. The guest, Dr Marisa Nucci discusses her team's finding in their recently published study in Modern Pathology. The authors propose the term “atypical uterine polyps” for these lesions, which show biologic overlap with early Mullerian adenosarcoma but lack molecular alterations characteristic of clinically aggressive adenosarcoma. Study by Nucci et al. Atypical uterine polyps show morphologic and molecular overlap with mullerian adenosarcoma but follow a benign clinical course. Modern Pathology, 35, 106-116. See acast.com/privacy for privacy and opt-out information.

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.14.331199v1?rss=1 Authors: Wu, Y., Besson, P., Azcona, E. A., Bandt, S. K., Parrish, T. B., Breiter, H. C., Katsaggelos, A. K. Abstract: Brain structure is tightly coupled with brain functions, but it remains unclear how cognition is related to brain morphology, and what is consistent across neurodevelopment. In this work, we developed graph convolutional neural networks (gCNNs) to predict Fluid Intelligence (Gf) from shapes of cortical ribbons and subcortical structures. T1-weighted MRIs from two independent cohorts, the Human Connectome Project (HCP; age: 28.81 {+/-} 3.70) and the Adolescent Brain Cognitive Development Study (ABCD; age: 9.93 {+/-} 0.62) were independently analyzed. Cortical and subcortical surfaces were extracted and modeled as surface meshes. Three gCNNs were trained and evaluated using six-fold nested cross-validation. Overall, combining cortical and subcortical surfaces yielded the best predictions on both HCP (R=0.454) and ABCD datasets (R=0.314), and outperformed the current literature. Across both datasets, the morphometry of the amygdala and hippocampus, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a novel reframing of the morphometry underlying Gf. Copy rights belong to original authors. Visit the link for more info

Careful morphologic review of blood smears is a crucial first step in the diagnostic workup of an abnormal WBC differential, explains Dr. Olga Pozdnyakova in this CAPcast interview. Dr. Pozdnyakova, along with Dr. Kyle Bradley, will be teaching a course on this topic at CAP20, which will be held virtually Oct. 10-14. For more information and to register: https://www.capannualmeeting.org.

This podcast is part of the 2019 NSH Symposium/Convention Poster Podcast Series. To read the full abstract and download a PDF copy of the poster (should it be made available by the presenter) visit the Block.   Authors: E. Grace Van Dyke, DVM Candidate, Washington State University School of Veterinary Medicine, Pullman, WA; Rachel Silvestri, HTL, Tulane National Primate Research Center, Covington, LA; Carol Coyne, BS, Tulane National Primate Research Center, Covington, LA; Heather Frye, Tulane National Primate Research Center, Covington, LA; Robert V. Blair, DVM, PhD, Tulane National Primate Research Center, Covington, LA; Peter J. Didier, DVM, PhD, Tulane National Primate Research Center, Covington, LA

Morphologic examination by light microscopy remains an integral part of initial histopathologic assessment for hematolymphoid neoplasms; however, a variety of laboratory techniques are now being used to optimize diagnostic information in the appropriate clinical context. For diagnosing myeloid disorders and acute myeloid leukemia, one approach is to utilize an next gen sequencing panel and DNA-based cytogenomic microarray, according to Dr. Pranil Chandra, Chief Medical Officer of Genomic and Clinical Pathology with PathGroup. In this CAPcast, Dr. Chandra discusses why his laboratory, in conjunction with pathologist and oncologist colleagues, developed this technology and how they’ve implemented it into clinical practice. Dr. Chandra currently serves on the Personalized Healthcare Committee of the College of American Pathologists, and he’s written a short article now posted on CAP.org (https://capatholo.gy/2RO4h96) outlining the reasoning his team used to make this panel their standard of care.

Diagnostically challenging lesions comprise both foci (small lesions) and non-mass-like enhancing lesions and pose a challenge to current computer-aided diagnosis systems. Motion-based artifacts lead in dynamic contrast-enhanced breast magnetic resonance to diagnostic misinterpretation; therefore, motion compensation represents an important prerequisite to automatic lesion detection and diagnosis. In addition, the extraction of pertinent kinetic and morphologic features as lesion descriptors is an equally important task. In the present paper, we evaluate the performance of a computer-aided diagnosis system consisting of motion correction, lesion segmentation, and feature extraction and classification. We develop a new feature extractor, the radial Krawtchouk moment, which guarantees rotation invariance. Many novel feature extraction techniques are proposed and tested in conjunction with lesion detection. Our simulation results have shown that motion compensation combined with Minkowski functionals and Bayesian classifier can improve lesion detection and classification.