BACKGROUND:Reduced left ventricular ejection fraction (LVEF) initiates heart failure, and promptly identifying low ejection fraction is crucial for managing progression and averting mortality. In this study we developed an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm to identify patients with low ejection fraction and predict LVEF values. METHODS:The electrocardiogram data were used as input, and the algorithm generated the probability of the patient suffering a low ejection fraction and estimated the LVEF value. A 5-year follow-up study on a group of individuals who initially had normal LVEF values was also performed. Furthermore, external validation of the algorithm performance was conducted using the Medical Information Mart for Intensive Care-IV database. RESULTS:The algorithm's performance on the test set yielded an area under the curve value of 0.965 for detecting LVEF ≤ 50%. The algorithm had an accuracy of 92.8%, sensitivity of 88.8%, and specificity of 92.9%. For LVEF regression, the method showed a mean absolute error of 5.28 (95% confidence interval, 5.23-5.33) for the testing set. Additionally, the algorithm obtained an area under the curve value of 0.848 and a mean absolute error value of 9.56 during external validation. Patients with false positive results had a significantly greater likelihood of developing a low ejection fraction compared with patients who received true negative results (26.2% vs 2.0%; P < 0.0001). CONCLUSIONS:The AI-ECG algorithm is capable of identifying low ejection fraction in patients with high accuracy. The AI-ECG algorithm is an efficient, prompt, and cost-effective screening tool for early heart failure.

The performance of deep learning models for medical image segmentation is often limited in scenarios where training data or annotations are limited. Self-Supervised Learning (SSL) is an appealing solution for this dilemma due to its feature learning ability from a large amount of unannotated images. Existing SSL methods have focused on pretraining either an encoder for global feature representation or an encoder-decoder structure for image restoration, where the gap between pretext and downstream tasks limits the usefulness of pretrained decoders in downstream segmentation. In this work, we propose a novel SSL strategy named Volume Fusion (VolF) for pretraining 3D segmentation models. It minimizes the gap between pretext and downstream tasks by introducing a pseudo-segmentation pretext task, where two sub-volumes are fused by a discretized block-wise fusion coefficient map. The model takes the fused result as input and predicts the category of fusion coefficient for each voxel, which can be trained with standard supervised segmentation loss functions without manual annotations. Experiments with an abdominal CT dataset for pretraining and both in-domain and out-domain downstream datasets showed that VolF led to large performance gain from training from scratch with faster convergence speed, and outperformed several state-of-the-art SSL methods. In addition, it is general to different network structures, and the learned features have high generalizability to different body parts and modalities.

Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcome the difficulty of identifying specific cell types. In assessing data obtained from fine needle biopsies, experiments revealed that there was a higher level of agreement in the quantitative results between Artificial Intelligence (AI) algorithms and pathologists, as well as among pathologists themselves, in comparison to the data obtained from surgical specimens. We highlight that the AI-enabled analytics enhance the objectivity and interpretability of PD-L1 quantification to improve the targeted immunotherapy development in DLBCL patients.

Backgrounds The widespread coronavirus disease 2019 (COVID-19) outbreak impacted the mental health of infected patients admitted to Fangcang shelter hospital a large-scale, temporary structure converted from existing public venues to isolate patients with mild or moderate symptoms of COVID-19 infection. Objective This study aimed to investigate the risk factors of the infected patients from a new pharmacological perspective based on psychiatric drug consumption rather than questionnaires for the first time. Methods We summarised the medical information and analysed the prevalence proportion, characteristics, and the related risk factors of omicron variants infected patients in the Fangcang Shelter Hospital of the National Exhibition and Convention Center (Shanghai) from 9 April 2022 to 31 May 2022. Results In this study, 6,218 individuals at 3.57% of all admitted patients in the Fangcang shelter were collected suffering from mental health problems in severe conditions including schizophrenia, depression, insomnia, and anxiety who needed psychiatric drug intervention. In the group, 97.44% experienced their first prescription of psychiatric drugs and had no diagnosed historical psychiatric diseases. Further analysis indicated that female sex, no vaccination, older age, longer hospitalization time, and more comorbidities were independent risk factors for the drug-intervened patients. Conclusion This is the first study to analyse the mental health problems of omicron variants infected patients hospitalised in Fangcang shelter hospitals. The research demonstrated the necessity of potential mental and psychological service development in Fangcang shelters during the COVID-19 pandemic and other public emergency responses.

Shanghai has faced an unprecedented COVID-19 pandemic with the BA.2.2 strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron infection. Comprehensive insights into its epidemiology, clinical manifestations, and viral shedding dynamics are currently limited. This study encompasses 208373 COVID-19 patients that were infected with the Omicron BA.2.2 sub-lineage in Shanghai, China. Demographic information, clinical symptoms, vaccination status, isolation status, as well as viral shedding time (VST) were recorded. Among the COVID-19 patients included in this study, 187124 were asymptomatic and 21249 exhibited mild symptoms. The median VST was 8.3 days. The common clinical symptoms included fever, persistent cough, phlegm, sore throat, and gastrointestinal symptoms. Factors such as advanced age, presence of comorbidities, mild symptomatology, and delayed isolation correlated with extended VST. Conversely, female gender and administration of two or three vaccine doses correlated with a reduction in VST. This investigation offers an in-depth characterization and analytical perspective on Shanghai's recent COVID-19 surge. Prolonged viral shedding of SARS-CoV-2 was observed in elderly, male, symptomatic patients, and those with comorbidity. Female, individuals with two or three vaccine doses, as well as those isolated early, shows an effective reduced VST.

目的 通过搭建基于多模态智能对话机器人的糖尿病健康管理服务平台,解决糖尿病病程管理的痛点问题.方法 通过构建先进的专病知识图谱系统、智能对话机器人平台、大数据与深度学习模块、物联网基础平台,设计功能完善、使用方便的多模态智能对话机器人糖尿病健康管理服务平台.结果 基于多模态智能对话机器人的糖尿病健康管理服务平台使用多域模型后,深度对话会话语言理解的准确率和召回率的调和平均值提高了0.4.结论 将人工智能引入糖尿病病程管理,从糖尿病专病防治领域出发,通过深度学习+对话机器人+大数据+物联网技术与病程管理的结合,一对一个性化地对患者药物、饮食、运动、胰岛素使用、并发症的预防、应急情况的处理等进行指导,纠正患者不良习惯、改变重"医"轻"防"的观念,建立糖尿病防治新模式.

Artificial intelligence is increasingly being used on the clinical electrocardiogram workflows. Few electrocardiograms based on artificial intelligence algorithms have focused on detecting myocardial ischemia using long-term electrocardiogram data. A main reason for this is that interference signals generated from daily activities while wearing the Holter monitor lowered the ability of artificial intelligence to detect myocardial ischemia. In this study, an automatic system combining denoising and segmentation modules was developed to detect the deviation of the ST-segment and J point. We proposed a ECG Bidirectional Transformer network that applied in both denoising and segmentation tasks. The denoising model achieved RMSEde, SNRimp, and PRD values of 0.074, 10.006, and 16.327, respectively. The segmentation model achieved precision, sensitivity (recall), and F1-score of 96.00, 93.06, and 94.51%, respectively. The system's ability to distinguish the depression and elevation of the ST-segment and J point was also verified by cardiologists as well. From our ECG dataset, 103 patients with ST-segment depression and 10 patients with ST-segment elevation were detected with positive predictive values of 80.6 and 60% respectively. Using Holter ECG and transformer-based deep neural networks, we can detect subtle ST-segment changes in noisy ECG signals. This system has the potential to improve the efficacy of daily medicine and to provide a broader population-level screening for asymptomatic myocardial ischemia.

探索在新型冠状病毒（新冠）感染疫情防控中的大型方舱医院药品保障管理模式。根据方舱医院规模及收治新冠感染者特征，成立应急药品管理团队，搭建药品管理组织架构、建立规章制度；根据方舱医院的运行特点，制定和调整药品目录，规范申领、调拨流程，并加强特殊药品的管理；依据感染者的需求，开展多元化药学服务，指导患者用药。本团队建立了针对大型方舱医院运行的特色药品保障体系，分为中心药库及二级药库进行药品保障管理，在做到保障基本医疗药品供应的同时，提供药学服务以确保患者的用药安全。本团队建立的方舱药品保障管理模式，可为应对突发公共卫生事件时药品保障及完善服务患者提供参考。

Hospital culture plays an important role in the orderly operation of large shelter hospitals as well as epidemic prevention and control.From April to May 2022, the shelter hospital of the National Convention and Exhibition Center(Shanghai) had created the large shelter hospital culture co-built by doctors and patients with a greater sense of belonging by taking measures such as joint party building between doctors and patients, giving play to the vanguard force of party members, carrying out various forms of cultural, sports and science popularization activities, encouraging enthusiastic patients to participate in activity planning, focusing on key groups, formulating shelter " residents convention", and so on. These measures ultimately formed cultural adaptation, cultural synchronization and cultural shaping, which were conducive to enhancing the empathy of doctors and patients, improving the effectiveness of medical implementation, and promoting the standardization of shelter management system. This harmonious, warm and autonomous culture co-built by doctors and patients effectively ensures the safe and orderly operation of the shelter hospital, and provides reference for the construction of the cultural system of large shelter hospitals in China.

Vascularization is vital for the survival and functionality of complex tissue-engineered organs, and immune microenvironment is pivotal for effective vascularization. 3D bioprinting is a powerful technique for manufacturing engineered tissues. However, the reconstruction of functionalized vascular scaffolds with immunomodulatory properties through 3D bioprinting has rarely been reported. In this study, we fabricated scaffolds with immunomodulatory properties by incorporating INF-γ loaded laponite into the mixtures of gelatin methacrylate (GelMA)/alginate/4-arm poly(ethylene glycol) acrylate (PEG) (GAP) through coaxial bioprinting method with a sequential cross-linking mechanism that allows for stable production of 3D microfibrous scaffolds. Laponite addition optimized the hydrogel's physical and chemical performance, improved the rheological properties and printing feasibility while enhancing mechanical stress, making the direct fabrication of scaffolds with increased porosity and decreased filament diameter possible. Furthermore, new scaffolds facilitated the expression of chemotactic factors and accelerated EPC migration toward the microfiber peripheries to form a layer of confluent endothelium. Meanwhile, the scaffolds were capable of releasing IFN-γ in the early stage to stimulate macrophage M1 polarization, followed by induction of M2 polarization via the release of Si4+, Mg2+ as the degradation of laponite occurred, which successfully improved the sprouting and mature of newly formed vasculature, as well as vascularized bone regeneration. Our results suggested that a combination of GAP-IFN-γ@Lap bioink with a dual-step cross-linking procedure could regulate the local immune microenvironment, aiding the formation of a confluent endothelium, promoting angiogenesis and tissue regeneration, which potentially provides an efficient and simple strategy for developing complex vascularized tissues.

大型方舱医院作为一个突发公共事件或灾害救援中的特殊医疗空间,受到社会广泛关注.探索方舱医院文化体系建设,通过党建文化、精神文化、制度文化、行为文化和社群文化建设,形成文化合力,营造和谐医院氛围,提升医疗执行效能,为方舱医院管理目标的实现,提供文化保障.

为做好新型冠状病毒（新冠）肺炎疫情防控工作，根据《上海市新型冠状病毒肺炎防控方案（第五版）》《区域新型冠状病毒核酸检测组织实施指南（第三版）》精神，坚持科学精准、动态清零，以快制快、抓细抓实开展疫情防控各项工作；因时因势因情施策，调整和优化防控策略，扎实推进网格化筛查，及时发现潜在的隐匿传播风险，

目的:探索和寻求以大型方舱医院为代表的新型公共卫生防控与医疗设施信息系统建设方案与实践.方法:从上海3家大型方舱医院的实际业务需求出发,对其建设和运行特点,从基础设施、终端硬件、应用改造、运行维护等方面逐一进行分析,并结合本团队在实际工作中的经验总结系统建设思路和建设方案.结果:根据方舱医院的建设特点和其信息化需求,按照方舱医院应用功能规范要求,采用基于云平台的简化HIS及EMR作为核心业务系统,集成住院、护理、医技、药品管理等子模块,完成方舱医院信息系统的建设并实现医护业务的闭环管理.结论:5G、一体式医护终端、云平台、微服务等新技术的组合应用能够解决大型方舱医院建设周期短、规模大,信息化基础设施匮乏、用户多样性等问题.本研究为日后此类新型公共卫生防控与医疗设施信息化建设提供了新的建设思路和方法.

BACKGROUND:As the omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surges amid the coronavirus disease 2019 (COVID-19) pandemic, there is limited comorbidities data associated with viral shedding time (VST). We aimed to investigate the effect of comorbidities on VST in asymptomatic and mild patients with omicron. METHODS:A multi-center, retrospective, observational study was conducted from March 12, 2022 to May 24, 2022 in Shanghai. The analysis was adjusted for patients' baseline demographic, using log-rank test and logistic regression model. RESULTS:The study enrolled 198,262 subjects. The median duration of viral shedding time (VST) was 8.29 days. The number of cumulative viral shedding events was significantly lower in the chronic obstructive pulmonary disease (COPD), hyperlipidemia, diabetes, urinary system disease, and cardiocerebrovascular disease than in the no corresponding comorbidities group. Patients with comorbidities had a lower incidence of viral shedding, and the most significant independent risk factor is COPD (aOR 1.78, 95% CI: 1.53-2.08, p < 0.001). Across different age ranges, the comorbidities affecting viral shedding also differ, with the greatest risk factors for viral shedding being hyperlipidemia (aOR 2.23, 95% CI: 1.50-3.31, p < 0.001) and COPD (aOR 1.85, 95% CI: 1.50-2.28, p < 0.001) between ages of 18-39 and 40-64, and thyroid dysfunction (aOR 2.36, 95% CI: 1.60-3.47, p < 0.001) above age 64. CONCLUSIONS:Omicron-infected patients with comorbidities might prolong the VST. The independent risk factors also differ across age ranges, suggesting that providing targeted effective prevention and control guidance and allocating appropriate resources to different populations should be a crucial strategy.

方舱医院具有设计、施工周期短，基础设施简单，床位数量大，收治患者密度高，运行周转快，医疗、护理和运行保障团队多为临时组建等特点。方舱护理工作强度大、部分患者存在病情变化的风险、患者个性化需求难以得到满足等，使方舱医院护理工作的开展面临巨大挑战。本团队根据方舱医院的运行特点，构建方舱医院护理管理体系，包括护理管理组织构架、人力资源管理、质量控制体系、运行管理体系、应急处置体系、服务保障体系、沟通联络体系和人文关怀体系，为方舱医院护理工作的开展提供指导，有效保障了方舱医院护理工作安全、有序、高效地运行。

2022年3月，以奥密克戎变异株为主要流行株的新型冠状病毒（新冠）肺炎疫情在上海蔓延。为彻底、快速阻断疫情传播，尽早实现社会面清零，在上海市各级领导统一领导与部署下，按照“及时发现、快速处置、精准管控、有效救治”的目标要求，严格落实“四早”（早发现、早报告、早隔离、早治疗）原则，本市着手开始建造市区两级方舱医院。目前总数已达110余所，床位达25万余张，主要用于隔离收治新冠轻症及无症状感染者。

为应对当前严峻复杂的新型冠状病毒(新冠)肺炎疫情,我院利用5G、物联网、可穿戴设备、机器人、小程序等新技术,探索和构建了智能方舱医院,实现了患者入舱、舱内护理和生活、出舱全流程的智能化;创建了新型方舱医院精细化管理,高效医疗护理和患者人文关怀的新模式.一方面提升了患者在舱内的生活质量,另一方面提高了方舱医院的智能化管理能力,使方舱医护人员节省了大量的患者提醒、环境消杀、物资运送等工作量.

上海交通大学医学院附属瑞金医院方舱医院管理团队先后参与了多家方舱医院的设计、建设和运行工作。结合方舱医院规模大、患者收治量大、医疗废物产生多、工作人员感染风险高等运行特点，本文从方舱医院设计布局、感染防控制度和操作流程的制定、全员培训、健康管理、医疗废物管理等方面，总结本团队在方舱医院感控预防和控制（感控）工作中的经验，以期为大型方舱医院的安全、平稳运行提供借鉴。

In view of the difficulties, including simple facilities, temporary team formation, high working intensity, medical safety hazards and difficult emergency disposal etc. in medical management when large-scale Fangcang hospitals admitted and treated large quantity of patients during the epidemic period of the COVID-19 Omicron variant. We explored the medical management system of large-scale Fangcang hospital from the aspects of establishing a medical management team, formulating medical management system and disease diagnosis and treatment standards, upgrading the safety precautions and control, refining quality control evaluation and strengthening hospital-wide training and supervision. Aiming at improving the safety and the medical quality as the core goal, strengthening the medical system and optimizing the workflow are optimized via summarized the practical experience, to make large-scale Fangcang hospitals respond well to public health emergencies.

构建大型方舱医院数据库体系,汇总新型冠状病毒(新冠)疫情流行期间的专病研究的全链路数据,为疫情期间管控政策的决策、多维度数据挖掘、疾病因果推断及研究结果的可重复性提供底层数据支撑.使用病案登记系统和搜索引擎大数据的挖掘,将来自于搜索引擎的互联网数据和病案登记系统数据进行对比融合;采用多终端自动化信息收集、结构化数据的录入和存储,针对各组数据上报的需求和课题组的研究方向相对独立,增加转运数据、健康设施数据、COVID-19科研动态监测数据和新冠状病毒国家科技资源服务系统;建立指标数据规范,构建符合新型冠状病毒临床研究数据库.构建大型方舱收治人群的全链路数据库,可用于方舱的运行管理页面保障,为患者中心、随访管理、人群探索、数据洞察、科研项目管理提供数据支持和今后数据安全的参考蓝图,也可用于指导不同新冠毒株的流行特点,为解决当前公共医疗卫生领域难题提供新的方法.