新型冠状病毒肺炎诊断试验设计的思考

新型冠状病毒肺炎(COVID-19)疫情来势汹汹,准确、快速的诊断和筛检方法显得尤为重要。国内诸多科技工作者都在积极开展相关的诊断试验。本文回顾了目前正在开展的10余项诊断试验的注册信息,针对其中存在的共性问题进行讨论,重点阐述了如何使用患者干预比较结果(PICOS)原则构造COVID-19诊断试验的研究问题,对金标准的设置、受试者的代表性、样本量计算和同步、独立、盲法的测定等诊断试验的设计要点进行了详细说明,旨在为广大研究者提供COVID-19诊断试验设计的建议,帮助研究者在顶层设计阶段减少、避免偏倚,完成高质量的临床研究,为临床诊疗提供循证医学证据。     

基于文献计量学的中国新型冠状病毒感染影像学研究分析

目的 通过文献计量学的方法分析我国新型冠状病毒感染（coronavirus disease 2019, COVID-19影像研究者发表于国内外的论文,探讨COVID-19影像学研究热点,为应对长期COVID-19带来的挑战提供参考和指引。方法 在Web of Science和中国科学引文数据库检索2020年1月1日-2022年12月31日中国作者发表的COVID-19影像相关文献。应用CiteSpace软件进行文献计量及可视化分析。结果 共纳入文献2 229篇（Web of Science数据库1 771篇,中国科学引文数据库458篇）。月度发文量呈显著增长再缓慢下降,而后维持在稳定水平的趋势。关键词及共被引聚类分析显示COVID-19影像领域的研究热点是COVID-19临床特征、影像鉴别诊断以及人工智能的应用。关键词突现分析显示爆发强度最强的关键词是“deep learning”（深度学习）,爆发时长最长的关键词是“attention mechanism”（注意力机制）。结论 近年来COVID-19影像领域研究稳中有进,医学影像人工智能领域最受关注。研究者们的关注重点是COVID-1...     

新型冠状病毒肺炎诊断的相关实验室检测技术应用

2019年底暴发的新型冠状病毒肺炎(COVID-19)已成全球蔓延态势,对世界公共卫生安全造成了重大威胁。实验室检测在COVID-19诊治全过程都具有重要价值。目前,严重急性呼吸综合征冠状病毒2(SARSCoV-2)核酸检测仍是诊断COVID-19的金标准。文章对COVID-19的确诊、鉴别诊断及辅助诊断相关实验室检测技术的原理、优缺点、现有研究情况、结果判读等问题进行综述,以期为临床医生及检验工作者提供参考。     

2例新型冠状病毒肺炎粪便SARS-CoV-2核酸阳性临床启示

目前中国新型冠状病毒肺炎(COVID-19)疫情仍十分严峻,及时、早期地识别COVID-19患者是控制疫情的关键步骤。而新型冠状病毒(SARS-CoV-2)核酸检测作为诊断COVID-19的金标准,不但对检测技术提出更高的要求,同时对不同病程患者如何选择标本类型也是一个挑战。本文报道2例COVID-19确诊病例的诊疗经过,并分析采用不同类型标本检测SARS-CoV-2核酸的检出情况,为临床诊断COVID-19时如何选择标本类型和如何提高核酸检出率提供参考。     

新型冠状病毒肺炎痰标本2019-nCoV核酸阳性3例报道:对咽拭子检测假阴性问题的思考及建议

由新型冠状病毒2019-nCoV引起的肺炎(COVID-19)目前疫情仍十分严峻。2019-nCoV的核酸检测是COVID-19确诊的必要指标之一。临床对标本类型的选择影响2019-nCoV核酸检出率。本文报道3例COVID-19患者的确诊经过,分析采用咽拭子和痰标本检测2019-nCoV的效果,为临床诊断COVID-19时如何选择标本类型和如何提高核酸检出率提供参考。     

基于迁移学习和多参数融合优化的COVID-19辅助诊断模型

  目的   针对传统CT影像诊断准确性不高和效率低下问题,探讨深度学习技术在影像学中辅助诊断COVID-19的模型研究。   方法   首先构建早期、进展期和重症期三类别的COVID-19影像学数据集,然后构建一个基于VGG-16迁移学习的诊断COVID-19的初始模型,最后通过逐步对全连接层网络结构、激活函数、损失函数、优化算法、学习率和样本批次大小的多参数融合优化,设计出一个COVID-19辅助诊断模型。   结果   在COVID-19影像学测试集上COVID-19辅助诊断模型的准确率为98.10%,其中早期、进展期和重症期样本的敏感度分别为0.97、1.00、0.97,F1-score分别为0.98、0.97、0.99。   结论   通过迁移学习和多参数融合优化策略,设计的COVID-19辅助诊断模型在测试集上有较高的准确率。在防控疫情时,辅助诊断模型能帮助医务工作者提高工作效率。      

基于CT影像的COVID-19智能辅助诊断方法

目的结合COVID-19患者肺部CT影像学特征,探讨深度学习技术在COVID-19辅助诊断上的价值。方法搜集武汉大学中南医院和华中科技大学同济医学院确诊为COVID-19患者的部分CT影像资料构建小样本COVID-19数据集,将VGG-16具有提取高层抽象特征部分与设计的全连接层共同构成初步的基于迁移学习的COVID-19智能辅助诊断模型,使用COVID-19训练集迭代训练诊断模型,不断优化全连接层网络参数,最后训练出一个基于VGG-16卷积神经网络迁移学习的COVID-19智能辅助诊断模型。结果在COVID-19测试集中早期、进展期和重症期3个类别的样本上,COVID-19智能辅助诊断模型测试的敏感度分别为0.95、0.93和0.96,F1 Score分别为0.98、0.95和0.92,综合的诊断准确率达到94.59%。结论小样本数据集上采用迁移学习技术训练的COVID-19辅助诊断模型具有较高的可靠性,在防控疫情的关键时期,能快速地为医生提供诊断的参考意见,提高医生的工作效率。      

新型冠状病毒肺炎诊疗:从方案更新到临床实践

2019年年末新型冠状病毒肺炎(COVID-19)疫情出现,全国上下同心进行了全民防疫阻击战。全国军队、地方医疗队分批驰援湖北,其他省市地区强化防控管理、对确诊及疑似病例进行集中收治。该文对国家卫生健康委员会COVID-19诊疗方案更新、诊断流程图、治疗实践体会进行了解读述评,以期为COVID-19的进一步临床诊断治疗提供参考。     

1例COVID-19患者的实验室指标变化及意义

新型冠状病毒(SARS-CoV-2)是单链正义RNA病毒,属于β类冠状病毒,与严重急性呼吸综合征冠状病毒(SARS-CoV)和中东呼吸综合症冠状病毒(MERS-CoV)同种属^[1-2]。现有的研究结果显示,新型冠状病毒肺炎(COVID-19)的传染性和病死率都非常高,围绕该病毒的诊断和治疗亟待突破。尤其是对病毒感染免疫的研究将在实验室诊断和疫苗的研发中发挥至关重要的作用。本文就上海首例COVID-19患者整个诊治过程中的实验室指标变化进行报道,为COVID-19的诊断治疗提供信息。     

特异性IgM/IgG抗体检测筛查无症状新型冠状病毒感染者1例

正日前境外输入病例和无症状感染者引起的新型冠状病毒肺炎(COVID-19)聚集性传播备受关注。如何尽早筛查出无症状感染者是目前COVID-19防控的关键。虽核酸检测仍是COVID-19确诊的金标准,但受样本采集部位、时机、方法以及样品保存、运输、核酸提取、检测试剂检出能力等多因素影响,不少病例多次核酸阴性而延误诊断[1]。《新型冠状病毒肺炎诊疗方案(试行第七版)》[2]提出SARS-Co V-2特异性Ig M/Ig G抗体检测可作为COVID-19诊断指标之一,但不同方法之间检测结果存在较大差异。本研究报道1例核酸连续多次检测阴性、7种方法抗体检测连续多次阳性而最终确诊的无症状感染者,旨在为制定COVID-19筛查和诊疗方案提供依据。     

新型冠状病毒肺炎(COVID-19)临床研究的启示与国家临床试验网络构想

新型冠状病毒肺炎(COVID-19)疫情爆发以来,从2020年1月23日至2020年3月5日由中国临床研究人员在中国临床试验注册中心(www.chictr.org.cn)和美国临床试验注册网站(www.clinicaltrials.gov)两个平台上登记注册并已经或将要在中国境内进行的针对新型冠状病毒肺炎的干预性临床试验项目总数已超过200个。这一现象引起了社会各界的关注。本文在对已登记注册的249个在中国开展的COVID-19临床试验项目的主要特征作描述性分析的基础上,借鉴美国国家卫生研究院(National Institutes of Health)组建的专项疾病领域国家临床试验网络的实践,思考在突发流行病情况下提高临床试验质量与效率的对策。     

Flare of the silent pandemic in the era of the COVID-19 pandemic: Obstacles and opportunities

A noteworthy public health problem, antimicrobial resistance (AMR) has been impeded in many ways by the coronavirus disease 2019 (COVID-19) pandemic. This narrative review discusses the two-sided impact of COVID-19 on the magnitude of AMR. The pandemic has put tremendous strain on healthcare systems, diverting resources, personnel, and attention away from AMR diagnosis and management toward COVID-19 diagnosis and contact tracking and tracing. AMR research has been severely hampered, and surveillance and antimicrobial stewardship (AMS) programs have been de-emphasized, delayed, or halted. Antibiotics, particularly broad-spectrum, were prescribed more frequently without diagnostic confirmation of bacterial infection than before the pandemic. Nonetheless, the COVID-19 pandemic has highlighted the vulnerability of healthcare systems in controlling infectious disease threats and raised awareness of the importance of infection prevention and control. Yet, the pandemic has created opportunities to capitalize on positive effects on AMR management. The review concludes that it is now more important than ever to focus on AMR and strengthen AMS programs to ensure appropriate antibiotic use and other AMR prevention measures in healthcare. We must ensure that one of the COVID-19 legacies is increased support for AMR research, diagnostic implementation, appropriate diagnostic stewardship, and the strengthening of our health systems. The COVID-19 pandemic has demonstrated that prevention is better than cure. Countries will need to step up their efforts to combat AMR as a multidisciplinary community. We must prepare our public health systems to combat multiple threats at the same time.     

新型冠状病毒肺炎的国内研究现状可视化分析与解读

目的可视化分析和解读新型冠状病毒肺炎(COVID-19)的国内研究现状,为疫情防控策略与方案提供线索和指引。方法以“新型冠状病毒”为主题词检索中国知网数据库文献,发表时间限定为2019年12月1日至2020年3月1日。利用VOSviewer软件分析文献的作者和关键词共现网络,解读共现图谱聚类揭示的COVID-19研究现状与热点。结果检索到有效文献664篇,文献作者形成5大团体,但各团体间的合作较少。“疫情防控”和“中药”等10个高频关键词及其他关键词共同聚类成7个研究主题,每个主题所揭示的内容均真实反映了当前的研究现状与热点。结论国内关于COVID-19的研究热点主要集中在疫情防控、医护人员的管理与防护、儿童感染及防护、COVID-19临床特征和诊疗方案、中医药在COVID-19防治中的应用。     

Management of Diabetes During a Dual Pandemic

The dual pandemics of coronavirus disease-19 (COVID-19) and diabetes among patients are associated with 2- to 3-times higher intensive care admissions and higher mortality rates. Whether sheltering at home, quarantined with a positive COVID-19 test, or hospitalized, the person living with diabetes needs special considerations for successful management. Having diabetes and being COVID-19–positive increases the risk of poor outcomes and death. Providers need to give anticipatory pharmacologic guidance to patients with diabetes during COVID-19 lockdown. Patients with diabetes need to be more observant than others and to use self-protective actions. This review (1) discusses the clinical observations of COVID-19, diabetes and underlying mechanisms, (2) describes special considerations in caring for patients with diabetes in a COVID-19 environment, and (3) reviews clinical implications for the health care provider. This review highlights current evidenced-based knowledge. Additional research regarding clinical management is warranted     

Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19

In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public health crisis of the novel coronavirus disease 2019 (COVID-19) pandemic. Chest radiograph (CXR) has been playing a crucial role in COVID-19 patient triaging, diagnosing and monitoring, particularly in the United States. Considering the mixed and unspecific signals in CXR, an image retrieval model of CXR that provides both similar images and associated clinical information can be more clinically meaningful than a direct image diagnostic model. In this work we develop a novel CXR image retrieval model based on deep metric learning. Unlike traditional diagnostic models which aim at learning the direct mapping from images to labels, the proposed model aims at learning the optimized embedding space of images, where images with the same labels and similar contents are pulled together. The proposed model utilizes multi-similarity loss with hard-mining sampling strategy and attention mechanism to learn the optimized embedding space, and provides similar images, the visualizations of disease-related attention maps and useful clinical information to assist clinical decisions. The model is trained and validated on an international multi-site COVID-19 dataset collected from 3 different sources. Experimental results of COVID-19 image retrieval and diagnosis tasks show that the proposed model can serve as a robust solution for CXR analysis and patient management for COVID-19. The model is also tested on its transferability on a different clinical decision support task for COVID-19, where the pre-trained model is applied to extract image features from a new dataset without any further training. The extracted features are then combined with COVID-19 patient's vitals, lab tests and medical histories to predict the possibility of airway intubation in 72 hours, which is strongly associated with patient prognosis, and is crucial for patient care and hospital resource planning. These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.     

Clinical application of SARS-CoV-2 antibody detection and monoclonal antibody therapies against COVID-19

The purpose of this study was to investigate the clinical application of severe acute respiratory distress syndrome coronavirus-2 (SARS-CoV-2) specific antibody detection and anti-SARS-CoV-2 specific monoclonal antibodies (mAbs) in the treatment of coronavirus infectious disease 2019 (COVID-19). The dynamic changes of SARS-CoV-2 specific antibodies during COVID-19 were studied. Immunoglobulin M (IgM) appeared earlier and lasted for a short time, while immunoglobulin G (IgG) appeared later and lasted longer. IgM tests can be used for early diagnosis of COVID-19, and IgG tests can be used for late diagnosis of COVID-19 and identification of asymptomatic infected persons. The combination of antibody testing and nucleic acid testing, which complement each other, can improve the diagnosis rate of COVID-19. Monoclonal anti-SARS-CoV-2 specific antibodies can be used to treat hospitalized severe and critically ill patients and non-hospitalized mild to moderate COVID-19 patients. COVID-19 convalescent plasma, highly concentrated immunoglobulin, and anti-SARS-CoV-2 specific mAbs are examples of anti-SARS-CoV-2 antibody products. Due to the continuous emergence of mutated strains of the novel coronavirus, especially omicron, its immune escape ability and infectivity are enhanced, making the effects of authorized products reduced or invalid. Therefore, the optimal application of anti-SARS-CoV-2 antibody products (especially anti-SARS-CoV-2 specific mAbs) is more effective in the treatment of COVID-19 and more conducive to patient recovery.     

The designs of exploratory biomarker study

With the progress of molecular biology and other disciplines, biomarkers are becoming increasingly important in both drug discovery and development. Several critical issues, including scientific rationale, study design, marker assessment, cost and feasibility should be carefully considered in the validation of biomarkers through clinical research. Here, we highlight several important aspects related to the design for clinical researches which incorporate biomarkers. First, we define the term "biomarker" and illustrate the difference between prognostic and predictive markers. Second, in exploratory clinical studies, we focus on issues related to study design for screening and evaluating biomarkers. Finally, we review the design of confirmatory clinical trials for new treatments and companion biomarker diagnostic tests.