Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

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The National Home-Based Primary Care Learning Network: A Practice-Based Quality Improvement and Research Network

Home-based primary care (HBPC) provides interdisciplinary, comprehensive care at home for homebound older adults and has been largely excluded from the national conversation on care quality and quality improvement. In this Pragmatic Innovations article, we describe the work of the National HBPC Learning Network (LN), which focuses on fostering a continuous learning culture among HBPC practices to improve practice quality, elevate the field of HBPC, and create a community of continuous growth and quality of care accountability. The LN recruits HBPC practices in waves of 9 to 10 practices. It currently comprises 38 HBPC practices that care for 58,000 patients across 25 states (approximately 26% of all patients receiving HBPC in the United States). In a Kickoff meeting, the HBPC practices in each wave are instructed in the basics of quality improvement, develop project aim statements and their first plan-do-study-act cycle, receive an introduction to the LN quality improvement software platform, and review plans for LN engagement. Each month, practices submit updates and receive real-time feedback on their quality improvement work. Monthly virtual workshops are held with all practices that include sharing results of a “1-minute survey” (a monthly 1-to 3-question survey sent to all LN participants on a topic relevant to HBPC practices), a didactic and discussion related to the 1-minute survey topic, and interactive progress updates from LN participants regarding their quality improvement work. Each wave ends with “Moving-up Day,” where practices report on their overall project and reflect on how their practice has changed as a result of the LN. LN practices have addressed and improved performance in multiple HBPC-related quality areas including assessment of functional status and cognitive impairment, falls prevention, advanced care planning, COVID-19 vaccination, and others. We present case studies of 3 LN practices and how LN participation strengthened their practices.     

Assessment of the Benefits of Targeted Interventions for Pandemic Control in China Based on Machine Learning Method and Web Service for COVID-19 Policy Simulation

Taking the Chinese city of Xiamen as an example, simulation and quantitative analysis were performed on the transmissions of the Coronavirus Disease 2019(COVID-19) and the influence of intervention combinations to assist policymakers in the preparation of targeted response measures. A machine learning model was built to estimate the effectiveness of interventions and simulate transmission in different scenarios. The comparison was conducted between simulated and real cases in Xiamen. A web inter...     

435例原发性高血压患者脉图参数与心血管危险分层的相关性分析

目的 本研究旨在探讨原发性高血压患者脉图参数与心血管危险分层的相关性，为原发性高血压患者心血管风险事件预警提供中医特色的依据。方法 采集435例原发性高血压患者的症状体征、脉象信息、生化指标等，分为低中危组、高危组、很高危组，采用单因素方差分析、秩和检验、有序logistic回归分析等方法，探讨原发性高血压患者脉图参数与心血管危险分层的相关性。结果 ①高血压低中危组→高危组→很高危组患者病程逐渐延长，两两组间比较有统计学差异；②高血压低中危组→高危组→很高危组患者的脉图参数H3/H1、H4/H1逐渐上升；高血压患者很高危组H5/H1、T1/T低于低中危组；③有序logistic回归分析结果显示，脉图参数T1、T4、T5、T为影响因素。结论 病程、血脂、肾功能是高血压患者心血管危险分层的影响因素之一。心血管危险分层等级的升高与脉图参数H3/H1、H4/H1、T5、T的升高及H5/H1、T4、T1的降低相关，说明脉图能一定程度上反映原发性高血压患者血管壁增厚、血管内壁受损、血管硬化及左心室收缩功能的减退程度。     

Active learning in head and neck trauma: outcomes after an innovative educational course

Excellence in teaching and learning is fundamental to the provision of quality training for doctors and for this a sound knowledge of the principles of adult learning theory is important. We present an educational course in head and neck trauma skills for emergency medicine (EM) registrars, using the pedagogical principle of active learning. Our aim was to provide trainees with skills in the evaluation and management of hard and soft tissue injuries to the head, neck, and mouth. Active learning has been shown to provide a superior experience in classrooms, creating a ‘deep’ understanding of the material. To maximise learning, we used these principles to develop small group seminars led by a diverse range of teachers that allowed trainees to acquire hands-on skills. Questionnaires that were completed before and after the course showed an appreciable educational impact. We conclude that innovative teaching methods facilitate the sharing of knowledge that will benefit patient care.     

Assessment of a novel deep learning-based marker-less motion capture system for gait study

BackgroundMarker-less systems based on digital video cameras and deep learning for gait analysis could have a deep impact in clinical routine. A recently developed system has shown promising results in terms of joint center position but has not been yet evaluated in terms of gait outcomes.Research questionHow does this novel marker-less system compare to a marker-based reference system in terms of clinically relevant gait parameters?MethodsThe deep learning method behind the developed marker-less system was trained on a dedicated dataset consisting of forty-one asymptomatic and pathological subjects each performing ten walking trials. The system could estimate the three-dimensional position of seventeen joint centers or keypoints (e.g., neck, shoulders, hip, knee, and ankles). We evaluated the marker-less system against a marker-based system in terms of differences in joint position (Euclidean distance), detection of gait events (e.g., heel strike and toe-off), spatiotemporal parameters (e.g., step length, time), kinematic parameters (e.g., hip and knee extension-flexion), and inter-trial reliability for kinematic parameters.ResultsThe marker-less system was able to estimate the three-dimensional position of joint centers with a mean difference of 13.1 mm (SD = 10.2 mm). 99% of the estimated gait events were estimated within 10 ms of the corresponding reference values. Estimated spatiotemporal parameters showed zero bias. The mean and standard deviation of the differences of the estimated kinematic parameters varied by parameter (for example, the mean and standard deviation for knee extension flexion angle were −3.0° and 2.7°). Inter-trial reliability of the measured parameters was similar to that of the marker-based references.SignificanceThe developed marker-less system can measure the spatiotemporal parameters within the range of the minimum detectable changes obtained using the marker-based reference system. Moreover, except for hip extension flexion, the system showed promising results in terms of several kinematic parameters.