文献精选

糖尿病足的痛啊，我们用了这些招儿……

原创：十六点五中山二院糖尿病足中心

糖尿病足到底痛不痛，是一个很痛苦的问题。如果不痛，患者觉得“脚都不痛了，肯定死完全了，还能保吗？”。

糖尿病足病人的皮肤溃疡护理模式

原创：十六点五中山二院糖尿病足中心

根据我们的统计

wagner分级3-4级的糖尿病足中位数愈合时间

是6个月

很多患者甚至需要1年-1年半才能完全愈合

而50%以上的患者在2年左右就会复发或再发

“卢红斌”式伤口及其他

原创：十六点五中山二院糖尿病足中心

糖尿病足的伤口变化多端好像很难找到其中的规律但经过大量的临床实践我们似乎也找到了一些规律尤其是一个特殊部位有特殊规律的伤口其发病及治疗都有其一定固有的规律由于第一个给我们深刻印象的患者叫“卢红斌”。

糖尿病足的早期治疗

原创：十六点五中山二院糖尿病足中心

糖尿病足是一种可怕的疾病，但和所有的疾病一样，并不是一开始就这么可怕，也是从早期（比较轻的状态）发展成严重的情况，而为了引起大家的注意，网上可以看到的病例往往都是严重的状态的病人，或者是晚期的病人。

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K2 Content

Motor and vestibular signals in the visual cortex permit the separation of self versus externally generated visual motion 2025-07-04 00:00

Mateo Ve´ lez-Fort,1,3 Lee Cossell,1,3 Laura Porta,1 Claudia Clopath,1,2 and Troy W. Margrie1,4,*

1 Sainsbury Wellcome Centre, University College London, London, UK

2 Bioengineering Department, Imperial College London, London, UK

3 These authors contributed equally

4 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.2025.01.032

SUMMARY

Knowing whether we are moving or something in the world is moving around us is possibly the most critical sensory discrimination we need to perform. How the brain and, in particular, the visual system solves this motion-source separation problem is not known. Here, we find that motor, vestibular, and visual motion signals are used by the mouse primary visual cortex (VISp) to differentially represent the same visual flow information according to whether the head is stationary or experiencing passive versus active translation. During locomotion, we find that running suppresses running-congruent translation input and that translation signals dominate VISp activity when running and translation speed become incongruent. This cross-modal interaction between the motor and vestibular systems was found throughout the cortex, indicating that running and translation signals provide a brain-wide egocentric reference frame for computing the internally generated and actual speed of self when moving through and sensing the external world.
Mapping the landscape of social behavior 2025-07-03 00:00

Ugne Klibaite,1,4,* Tianqing Li,2,4 Diego Aldarondo,1,3 Jumana F. Akoad,1 Bence P. O¨ lveczky,1,* and Timothy W. Dunn2,5,*

1 Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA

2 Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA

3 Present address: Fauna Robotics, New York, NY 10003, USA

4 These authors contributed equally

5 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。 (U.K.), 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。 (B.P.O¨ .), 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。 (T.W.D.)

https://doi.org/10.1016/j.cell.2025.01.044

SUMMARY

Social interaction is integral to animal behavior. However, lacking tools to describe it in quantitative and rigorous ways has limited our understanding of its structure, underlying principles, and the neuropsychiatric disorders, like autism, that perturb it. Here, we present a technique for high-resolution 3D tracking of postural dynamics and social touch in freely interacting animals, solving the challenging subject occlusion and partassignment problems using 3D geometric reasoning, graph neural networks, and semi-supervised learning. We collected over 110 million 3D pose samples in interacting rats and mice, including seven monogenic autism rat lines. Using a multi-scale embedding approach, we identified a rich landscape of stereotyped actions, interactions, synchrony, and body contacts. This high-resolution phenotyping revealed a spectrum of changes in autism models and in response to amphetamine not resolved by conventional measurements. Our framework and large library of interactions will facilitate studies of social behaviors and their neurobiological underpinnings.
Large language models deconstruct the clinical intuition behind diagnosing autism 2025-06-26 00:00

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.

Custom Mod Mega1

主任医师、教授、博导，南方医科大学第三附属医院（广东省骨科医院）院长

文献精选

糖尿病足的痛啊，我们用了这些招儿……

糖尿病足病人的皮肤溃疡护理模式

“卢红斌”式伤口及其他

糖尿病足的早期治疗

学术会议

伤口世界提示

Custom Mod Mega1

主任医师、教授、博导，南方医科大学第三附属医院（广东省骨科医院）院长

文献精选

糖尿病足的痛啊，我们用了这些招儿……

糖尿病足病人的皮肤溃疡护理模式

“卢红斌”式伤口及其他

糖尿病足的早期治疗

消息订阅

学术会议

伤口世界提示