口腔医疗机构应对国家突发公共卫生事件的问题与思考

突发公共卫生事件应急管理是一个全球性的课题。本文就2019年12月以来发生在中国的新型冠状病毒肺炎这一突发公共卫生事件,口腔医疗机构及其从业人员面临的相关问题进行法律层面的分析与思考,以期对口腔医疗行业应对突发公共卫生事件提供帮助和指导。     

Automated classification of depression from structural brain measures across two independent community‐based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.     

Sliding mode control of time‐varying delay Markov jump with quantized output

This paper investigates the quantized sliding mode control of Markov jump systems with time‐varying delay. A dynamical adjustment law is explored to quantize the system output. By constructing an observer‐based integral sliding surface, a sliding mode controller is designed to take over the dynamical motion of state estimation and ensure the reachability of sliding surface. A new scaling manner is developed to build the bound between the system output and quantized error. With the help of separation strategies for controller synthesis and general transition probabilities and a lower bound theorem for nonlinear integral terms, a new synthesis method to ensure the required stability and meet the required performance is proposed in the form of linear matrix inequalities. The validity of the proposed control method is illustrated by a numerical example.     

MRI图像融合技术在肛瘘评估中的初步应用

目的采用图像融合技术获得T2WI与T2WI-FS的融合图像，评估其在肛瘘及肛周结构显示中的优势。 方法2016年6月至2018年6月，前瞻性选择中山大学附属第一医院29例肛瘘患者进行肛管磁共振（MR）检查，采用图像融合技术获取T2WI与T2WI-FS的融合图像T2WI-Fusion，利用Fisher score算法计算瘘管及肛门括约肌的组织间分辨力Fisher值、脂肪与肛门括约肌间的Fisher值，评估融合图像中瘘管及肛周结构的显示情况。采用改进的双刺激连续质量量表（DSCQS）对T2WI-FS、T2WI、增强3D-VIBE和T2WI-Fusion序列图像进行主观图像质量评价。 结果29例患者均成功获得T2WI与T2WI-FS的融合图像T2WI-Fusion。T2WI-Fusion、T2WI瘘管与括约肌间Fisher均值分别为6.46、3.31，T2WI-Fusion图像对瘘管的显示优于T2WI序列图像（P<0.001）。T2WI-Fusion、T2WI-FS脂肪与括约肌间Fisher均值分别为10.61、2.45，T2WI-Fusion图像对括约肌的显示优于T2WI-FS序列图像（P<0.001）。T2WI-Fusion对瘘管与括约肌的图像质量评价总评分均高于T2WI-FS、T2WI、增强3D-VIBE序列（P<0.001）。 结论MRI图像融合技术同时具备T2WI及T2WI-FS的优势，无需增加扫描序列及扫描时间，且操作简单，花费时间短，显著提高病变及肛周解剖结构的对比度和图像质量。