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: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。
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: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (L.Y.), 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (Y.F.), 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (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.
创伤是指由于各种致伤因素导致的机体软组织、骨骼甚至内脏器官等等各个系统的损伤,创伤可以根据发生地点、受伤部位、受伤组织、致伤因素及皮肤完整程度进行分类。 按发生地点分为战争伤、工业伤、农业伤、交通伤、体育伤、生活伤等;按受伤部位分为颅脑创伤、胸部创伤、腹部创伤、各部位的骨折和关节脱位、手部伤等;按受伤类型分为骨折、脱位、脑震荡、器官破裂等;相邻部位同时受伤者称为联合伤(如胸腹联合伤);按受伤的组织或器官分类时,又可按受伤组织的深浅分为软组织创伤、骨关节创伤和内脏创伤。软组织创伤指皮肤、皮下组织和肌肉的损伤,也包括行于其中的血管和神经。单纯的软组织创伤一般较轻,但广泛的挤压伤可致挤压综合征。血管破裂大出血亦可致命。骨关节创伤包括骨折和脱位,并按受伤的骨或关节进一步分类并命名。如股骨骨折、肩关节脱位等。内脏创伤又可按受伤的具体内脏进行分类和命名。如脑挫裂伤、肺挫伤、肝破裂等。同一致伤原因引起两个以上部位或器官的创伤,称为多处伤或多发伤。按致伤因素,分为火器伤、切伤、刺伤、撕裂伤、挤压伤、扭伤、挫伤等。按皮肤完整程度,分为闭合性创伤、开放性创伤等。
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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