糖尿病足的诊断（接上篇）

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糖尿病足

压疮

造口

溃疡

烧伤
蔡道章院长

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

中德骨科伤口管理学校校长

广东省骨科研究院运动医学研究所所长

广东省内运动医学专业唯一的博士研究生导师

美国哈弗大学医学院骨科访问学者

专业特长处于省内领先、国内或国际先进水平以上

2018年获得“国之名医卓越建树”荣誉称号

2017年被评为全国卫生计生系统先进工作者、广东省医学领军人才

中国医师协会运动医师分会副会长

STCOT中国部运动医学分会副主任委员

广东省医学会关节外科分会主任委员

广东省医学会运动医学会分会名誉主任委员

独立承担过国家“863”课题，主持过10余项省、部级科研项目

多份专业杂志编委
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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.
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糖尿病足的诊断（接上篇）

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18 10月 2019

Author : 伤口世界

原创：胡海涛负压引流与创面修复

世界卫生组织(WHO)定义:糖尿病足是指糖尿病患者由于合并神经病变及各种不同程度末梢血管病变而导致下肢感染、溃疡形成和(或)深部组织的破坏。

三个必须条件，缺一不可：

糖尿病患者；
合并神经病变及不同程度末梢血管病变；
下肢感染、溃疡形成或深部组织坏死；

该患者疼痛明显、足背动脉博动好，不符合糖尿病足诊断标准2、3，所以排除糖尿病足诊断。伤口内见到痛风石、CT看到切口区域关节周围痛风结节较多，按痛风结节诊治方案处理，伤口愈合。

糖尿病足鉴别诊断：糖尿病患者合并痛风结节破溃、下肢动脉硬化闭塞、血栓闭塞性脉管炎等。

最后提醒：遇到糖尿病患者足溃疡时，第一步是诊断，第二步是鉴别诊断，尤其当治疗效果不好时，得再次思考这两个问题。

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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.

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2019广东省医疗行业协会伤口管理分会年会

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2019年6月15日中国广州
主办单位：广东省医疗行业协会伤口管理分会

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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