Jack Stanley,1,2,6 Emmett Rabot,3,4,6 Siva Reddy,1 Eugene Belilovsky,1,5 Laurent Mottron,3,4,7 and Danilo Bzdok1,2,7,8,*
1 Mila - Que´ bec Artificial Intelligence Institute, Montre´ al, QC H2S3H1, Canada
2 The Neuro - Montre´ al Neurological Institute (MNI), McConnell Brain Imaging Centre, Department of Biomedical Engineering, Faculty of Medicine, School of Computer Science, McGill University, Montre´ al, QC H3A2B4, Canada
3 Research Center, Centre Inte´ gre´ Universitaire de Sante´ et de Services Sociaux du Nord-de-lIle-de-Montre´ al (CIUSSS-NIM), Montre´ al, QC H4K1B3, Canada
4 Universite´ de Montre´ al, Montre´ al, QC H3C3J7, Canada
5 Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
6 These authors contributed equally
7 These authors contributed equally
8 Lead contact
*Correspondence: danilo.bzdok@mcgill.ca
https://doi.org/10.1016/j.cell.2025.02.025
SUMMARY
Efforts to use genome-wide assays or brain scans to diagnose autism have seen diminishing returns. Yet the clinical intuition of healthcare professionals, based on longstanding first-hand experience, remains the gold standard for diagnosis of autism. We leveraged deep learning to deconstruct and interrogate the logic of expert clinician intuition from clinical reports to inform our understanding of autism. After pre-training on hundreds of millions of general sentences, we finessed large language models (LLMs) on >4,000 free-form health records from healthcare professionals to distinguish confirmed versus suspected autism cases. By introducing an explainability strategy, our extended language model architecture could pin down the most salient single sentences in what drives clinical thinking toward correct diagnoses. Our framework flagged the most autism-critical DSM-5 criteria to be stereotyped repetitive behaviors, special interests, and perception-based behaviors, which challenges today,s focus on deficits in social interplay, suggesting necessary revision of long-trusted diagnostic criteria in gold-standard instruments.
Li Yuping,1,7,* Linlin Guan,2 Isabelle Becher,3 Kira S. Makarova,4 Xueli Cao,2 Surabhi Hareendranath,1 Jingwen Guan,1 Frank Stein,3 Siqi Yang,2 Arne Boergel,3 Karine Lapouge,3 Kim Remans,3 David Agard,5 Mikhail Savitski,3 Athanasios Typas,3 Eugene V. Koonin,4 Yue Feng,2,* and Joseph Bondy-Denomy1,6,8,*
1 Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94403, USA
2 State Key Laboratory of Green Biomanufacturing, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
3 European Molecular Biology Laboratory (EMBL), Meyerhofstraße 1, 69117 Heidelberg, Germany
4 Computational Biology Branch, Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
5 The Chan-Zuckerberg Institute for Advanced Biological Imaging and the Department of Biochemistry, University of California, San Francisco, San Francisco, CA 94143, USA
6 Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94403, USA
7 Present address: Biozentrum, University of Basel, Basel 4056, Switzerland
*Correspondence: yuping.li@unibas.ch (L.Y.), fengyue@mail.buct.edu.cn (Y.F.), joseph.bondy-denomy@ucsf.edu (J.B.-D.)
https://doi.org/10.1016/j.cell.2025.02.016
Jumbo bacteriophages of the fKZ-like family assemble a lipid-based early phage infection (EPI) vesicle and a proteinaceous nucleus-like structure during infection. These structures protect the phage from nucleases and may create selective pressure for immunity mechanisms targeting this specific phage family. Here, we identify ‘‘jumbo phage killer’’ (Juk), a two-component immune system that terminates infection of fKZ-like phages, suppressing the expression of early phage genes and preventing phage DNA replication and phage nucleus assembly while saving the cell. JukA (formerly YaaW) rapidly senses the EPI vesicle by binding to an early-expressed phage protein, gp241, and then directly recruits JukB. The JukB effector structurally resembles a pore-forming toxin and destabilizes the EPI vesicle. Functional anti-fKZ JukA homologs are found across bacterial phyla, associated with diverse effectors. These findings reveal a widespread defense system that specifically targets early events executed by fKZ-like jumbo phages prior to phage nucleus assembly.
原创: 十六点五 中山二院糖尿病足中心
多学科协作诊疗模式(MDT)是21世纪以来国内外新出现的最重要的医学模式之一。目的是使传统的个体式、经验式的医疗模式转变为小组协作共同决策的模式,其可以针对特定疾病,整合医疗资源,依托多学科团队,以求能够为患者制定最佳的诊断和治疗方案,并在深入交流的过程中提高各个专科的水平。
中山二院糖尿病足中心
一位援疆医生讲述了自己的故事:一天夜里,他接诊了一名遭遇车祸的病人,肝脏破裂,生命垂危。虽经全力抢救,病人终因失血过多而死亡。当医生告诉家属这个坏消息后,家属不仅没有责怪医生,反而向医生道谢,然后要求把切下的破碎肝脏带回去,和死者一起埋葬。丧事办完后,家属又来到医院结清所有费用。此举令这位医生十分感动。从此,每当遇到危重患者,他都没有后顾之忧,总是愿意冒险一搏。
伤口世界平台生态圈,以“关爱人间所有伤口患者”为愿景,连接、整合和拓展线上和线下的管理慢性伤口的资源,倡导远程、就近和居家管理慢性伤口,解决伤口专家的碎片化时间的价值创造、诊疗经验的裂变复制、和患者的就近、居家和低成本管理慢性伤口的问题。
2019广东省医疗行业协会伤口管理分会年会
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