用于新型冠状病毒检测的纳米材料及生物传感技术

新型冠状病毒肺炎(Corona Virus Disease 2019, COVID-19)疫情大流行引起全球对此重大突发公共卫生事件的高度关注。新型冠状病毒(SARS-CoV-2)经过多次突变,出现传染速度加快、免疫逃逸、隐匿性传播等特性,令防控形势至今仍异常严峻。对患者的早发现、早隔离仍然是目前最有效的防控措施。因此,迫切需要快速、高灵敏的检测手段来甄别此病毒,以便及早识别感染者。本文简要介绍了SARS-CoV-2的一般特征,并针对核酸、抗体、抗原及病原体作为检测靶标的不同检测手段及最新进展进行分类概述;对一些光学、电学、磁学以及可视化的新型纳米传感器在SARS-CoV-2检测技术上的应用进行了分析。鉴于纳米技术的应用在提高检测灵敏度、特异性以及准确率上具有优势,本文详细介绍了新型纳米传感器在SARS-CoV-2检测中的研究进展,包括表面增强拉曼基生物传感器、电化学生物传感器、磁纳米生物传感器以及比色生物传感器等,并探讨了纳米材料在新型生物传感器构建中的作用和挑战,为纳米材料研究人员开发各种类型的冠状病毒传感技术提供思路。     

生物安全柜关键计量参数校准

0引言2020年春节之际,新型冠状病毒肺炎的疫情日益严峻,感染人数不断攀升,为切断病毒的传染路径,急需进行分级诊疗,即对确诊人员、疑似人员、密切接触人员等有针对性地实施不同的收治、隔离方案,避免交叉感染,防止疫情扩大。实施分级诊疗的当务之急是加快诊断速度,给确诊发热病人、排查高危人群、甄别疑似病例、隔离阳性患者、保护阴性健康人群提供医学判断,并为一线员工和疫区人群重返工作岗位提供科学依据。各地政府指定了大量有能力和资质进行病原核酸检测的机构,如疾控中心、医院检验科、第三方医学检测实验室等进行核酸检测工作,处于疫情中心的武汉市还组建了符合标准且每日可检测万人份样本的应急检测“火眼”实验室,使得对新型冠状病毒的检测能力和速度得到大幅提升。     

疫情可视化设计中的数据分析与表达方法研究——以“重庆市新型冠状病毒肺炎疫情数据可视化分析”为例

围绕疫情数据可视化这一主题,研究如何针对突发事件的海量数据进行数据分析和可视化表达。在数据量大、内容繁杂的背景下,基于可视化设计的方法论,探讨数据可视化在突发公共卫生事件数据报道中的优势,并以“重庆市新型冠状病毒肺炎疫情数据可视化分析”设计为案例,具体分析数据可视化从设计方法到结论的过程。分析、整理适用于突发公共卫生事件下的疫情可视化设计数据分析和表达方法,为突发公共事件的设计介入、数据可视化的设计方法提供一定的补充。     

从SARS到新型冠状病毒的学术研究回顾性分析

在回顾新型冠状病毒的学术研究历程的基础上,回溯2003年至今SARS的学术研究成果对2019新型冠状病毒的疫情控制的学术影响力,从SARS学术研究的作者、发表年、研究机构、被引频次等多数据统计来分析SARS后我国疫情研究的情况,指出针对2019新型冠状病毒,应建立疾病回顾性研究和建立"2019-nCoV"病毒典藏库,尽快从学术研究的角度对2019-nCoV疾病相关的科学资料进行集中典藏,彰显科技工作对疫情控制的作用。     

中国计量院成功研制新型冠状病毒核衣壳蛋白（N蛋白）人源IgM单克隆抗体溶液标准物质

正本刊讯新型冠状病毒疫情爆发以来,核酸检测方法以其反应及时、检测较快的优势一度作为临床诊断及大规模高危人群筛查的"金标准"。随着研究的深入,科学家发现人体内新型冠状病毒特异性IgM抗体多在发病3～5天后开始出现阳性,是核酸检测方法的重要补充。因此,国家卫健委在《新型冠状病毒肺炎     

核酸检测技术在新型冠状病毒肺炎诊断中的应用分析

自新型冠状病毒肺炎爆发以来,疫情迅速蔓延至全国各地区。准确并及时诊断新型冠状病毒感染对患者的治疗以及疫情的防控至关重要。基于实时荧光RT-PCR技术的病毒核酸检测是新型冠状病毒肺炎诊断的主要手段。该文简要介绍新型冠状病毒核酸检测技术的原理及流程,并从疾病特点、检测试剂盒的性能、临床采样过程、实验操作等方面分析可能影响检测结果准确性的因素,为进一步提高新型冠状病毒核酸检测效能,降低漏诊率提供依据。     

养老机构新冠肺炎疫情防控团体标准的探索

新型冠状病毒感染的肺炎是新发现的乙类传染病,实行甲类传染病预防、控制措施。老年人是病毒易感人群,养老机构老年人群聚集,新型冠状病毒极易发生大范围流行,必须高度重视养老机构新型冠状病毒肺炎疫情的预防和控制。本文以浙江省标准化协会团体标准为例,分析了养老机构疫情防控需求,以标准化助推防控工作,推动规范化管理,防止和降低感染发生,保障健康养老。同时构建了疫情期间养老机构标准化管理的框架结构及内容,并提出了标准国际化的设想。     

突发重大公共事件对中国建造国际化发展的冲击效应分析

以新型冠状病毒肺炎(COVID-19)疫情为代表的突发重大公共事件,在对各国经济社会发展产生重大影响的同时,也对伴随"一带一路"建设而稳步推进的中国建造国际化构成了明显冲击。本文首先梳理了代表性的突发重大公共事件及应急管理经验,以仍在进行中的COVID-19疫情为例,剖析其对中国建造企业国内市场和国际市场的影响;其次以我国某建筑企业海外工程项目实施为案例,通过管理人员深度访谈,深入分析COVID-19疫情对伊拉克泵站项目在材料与设备、复工与招聘、人员身心健康、项目绩效等方面的影响,对企业应对策略的有效性进行评价。研究表明,为了积极应对突发重大公共事件,政府和企业应当完善突发重大公共事件的应急机制,共享应急管理数据;明确工程材料设备需求量,提前安排运输,关注物流风险,确保项目按期交付;合理增加人员复工补贴,积极实施心理辅导,最大程度地保障项目参建人员的合法权益与身心健康;按需调整项目方案,建立风险缓释机制和长效应对机制,降低项目履约损失。     

中国计量院推出新冠病毒全序列假病毒标准物质

正本刊讯在"世界计量日"到来之际,中国计量科学研究院全新推出新型冠状病毒全序列假病毒标准物质(NIMRM5207)、新型冠状病毒刺突蛋白基因(S)核糖核酸标准物质(NIM-RM5208)和新型冠状病毒B.1.1.7突变株核衣壳蛋白基因(N)核糖核酸标准物质(NIM-RM5209),将更好满足新冠病毒核酸检测对计量标准的需求。     

防控口罩如何保存、清洗? 国家卫健委发布口罩选择和使用技术指引

在新型冠状病毒感染的肺炎流行期间,为指导不同人群科学合理地选择和使用口罩,严防新型冠状病毒感染的肺炎疫情蔓延和扩散,国家卫健委疾病预防控制局于2月4日制定发布了《不同人群预防新型冠状病毒感染口罩选择和使用技术指引》。     

2019新型冠状病毒的抗原抗体检测

2019新型冠状病毒(SARS-CoV-2)入侵人体引起急性呼吸道传染病,命名为2019冠状病毒病(COVID-19)。新冠病毒在全球范围传播对全球公共卫生安全构成了巨大的威胁,新冠病毒的检测对疫病诊断尤为重要。免疫分析技术作为核酸检测的辅助手段,主要用于对疑似病例和与感染者密切接触人员的筛查。免疫诊断技术包括抗体和抗原检测,通过检测感染部位或者血液样本中是否含有病毒本身的蛋白或是由病毒引起免疫反应而产生的抗体来判断是否携带病毒。对目前用于新冠病毒抗原和抗体检测的免疫分析方法着重进行了分析。     

快速核酸检测仪计量需求初探

快速核酸检测仪在新型冠状病毒防控中发挥了重要作用,但对其计量还处于较为空白状态,计量机构及厂家仅能参考PCR相关计量技术规范的部分条款对快速核酸检测仪的部分参数进行计量,规范性和统一性有待提高.本文结合日常工作中对快速核酸检测仪的核查,依据其计量特性提出了计量需求.     

The Efficacy and Safety of Triazavirin for COVID-19: A Trial Protocol

The coronavirus disease 2019 (COVID-19), a pneumonia caused by a novel coronavirus, was reported in December 2019. COVID-19 is highly contagious and has rapidly developed from a regional epidemic into a global pandemic. As yet, no effective drugs have been found to treat this virus. This study, an ongoing multicenter and blind randomized controlled trial (RCT), is being conducted at ten study sites in Heilongjiang Province, China, to investigate the efficacy and safety of Triazavirin (TZV) versus its placebo in COVID-19 patients. A total of 240 participants with COVID-19 are scheduled to be enrolled in this trial. Participants with positive tests of throat swab virus nucleic acid are randomized (1:1) into two groups: standard therapy plus TZV or standard therapy plus placebo for a 7-day treatment with a 21-day follow-up. The primary outcome is the time to clinical improvement of the subjects. Secondary outcomes include clinical improvement rate, time to alleviation of fever, mean time and proportion of obvious inflammatory absorption in the lung, conversion rate of repeated negative virus nucleic acid tests, mortality rate, and conversion rate to severe and critically severe patients. Adverse events, serious adverse events, liver function, kidney function, and concurrent treatments will be monitored and recorded throughout the trial. The results of this trial should provide evidence-based recommendations to clinicians for the treatment of COVID-19.     

Overcoming the limitations of COVID-19 diagnostics with nanostructures,nucleic acid engineering,and additive manufacturing

The COVID-19 pandemic revealed fundamental limitations in the current model for infectious disease diagnosis and serology, based upon complex assay workflows, laboratory-based instrumentation, and expensive materials for managing samples and reagents. The lengthy time delays required to obtain test results, the high cost of gold-standard PCR tests, and poor sensitivity of rapid point-of-care tests contributed directly to society’s inability to efficiently identify COVID-19-positive individuals for quarantine, which in turn continues to impact return to normal activities throughout the economy. Over the past year, enormous resources have been invested to develop more effective rapid tests and laboratory tests with greater throughput, yet the vast majority of engineering and chemistry approaches are merely incremental improvements to existing methods for nucleic acid amplification, lateral flow test strips, and enzymatic amplification assays for protein-based biomarkers. Meanwhile, widespread commercial availability of new test kits continues to be hampered by the cost and time required to develop single-use disposable microfluidic plastic cartridges manufactured by injection molding. Through development of novel technologies for sensitive, selective, rapid, and robust viral detection and more efficient approaches for scalable manufacturing of microfluidic devices, we can be much better prepared for future management of infectious pathogen outbreaks. Here, we describe how photonic metamaterials, graphene nanomaterials, designer DNA nanostructures, and polymers amenable to scalable additive manufacturing are being applied towards overcoming the fundamental limitations of currently dominant COVID-19 diagnostic approaches. In this paper, we review how several distinct classes of nanomaterials and nanochemistry enable simple assay workflows, high sensitivity, inexpensive instrumentation, point-of-care sample-to-answer virus diagnosis, and rapidly scaled manufacturing.     

Nanozyme-Based Colorimetric SARS-CoV-2 Nucleic Acid Detection by Naked Eye

Fluorescence-based PCR and other amplification methods have been used for SARS-CoV-2 diagnostics, however, it requires costly fluorescence detectors and probes limiting deploying large-scale screening. Here, a cut-price colorimetric method for SARS-CoV-2 RNA detection by iron manganese silicate nanozyme (IMSN) is established. IMSN catalyzes the oxidation of chromogenic substrates by its peroxidase (POD)-like activity, which is effectively inhibited by pyrophosphate ions (PPi). Due to the large number of PPi generated by amplification processes, SARS-CoV-2 RNA can be detected by a colorimetric readout visible to the naked eye, with the detection limit of 240 copies mL⁻¹. This conceptually new method has been successfully applied to correctly distinguish positive and negative oropharyngeal swab samples of COVID-19. Colorimetric assay provides a low-cost and instrumental-free solution for nucleic acid detection, which holds great potential for facilitating virus surveillance.     

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables in layer 2. In layer 1, the initial identification for COVID-19 is considered, whereas in layer 2, the different factors involved are studied. Finally, advanced lab tests are conducted to identify the actual current status of the disease. The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19; the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining. MATLAB-2019a tool is used to conduct the simulation. The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%. Finally, to achieve improved performance, the analysis results of the system were shared with experts of the Lahore General Hospital, Lahore, Pakistan.     

Clinical Characteristics and Outcomes of Severe or Critical COVID-19 Patients Presenting No Respiratory Symptoms or Fever at Onset

It is difficult to identify suspected cases of atypical patients with coronavirus disease 2019 (COVID-19), and data on severe or critical patients are scanty. This retrospective study presents the clinical, laboratory, and radiological profiles, treatments, and outcomes of atypical COVID-19 patients without respiratory symptoms or fever at onset. The study examined ten atypical patients out of 909 severe or critical patients diagnosed with COVID-19 in Wuhan Union Hospital West Campus between 25 January 2020 and 10 February 2020. Data were obtained from the electronic medical records of severe or critical patients without respiratory symptoms or fever at onset. Outcomes were followed up to discharge or death. Among 943 COVID-19 patients, 909 (96.4%) were severe or critical type. Of the severe or critical patients, ten (1.1%) presented without respiratory symptoms or fever at admission. The median age of the ten participants was 63 years (interquartile range (IQR): 57–72), and seven participants were men. The median time from symptom onset to admission was 14 d (IQR: 7–20). Eight of the ten patients had chronic diseases. The patients had fatigue (n = 5), headache or dizziness (n = 4), diarrhea (n = 5), anorexia (n = 3), nausea or vomiting (n = 3), and eye discomfort (n = 1). Four patients were found to have lymphopenia. Imaging examination revealed that nine patients had bilateral pneumonia and one had unilateral pneumonia. Eventually, two patients died and eight were discharged. In the discharged patients, the median time from admission to discharge lasted 24 d (IQR: 13–43). In summary, some severe or critical COVID-19 patients were found to have no respiratory symptoms or fever at onset. All such atypical cases should be identified and quarantined as early as possible, since they tend to have a prolonged hospital stay or fatal outcomes. Chest computed tomography (CT) scan and nucleic acid detection should be performed immediately on close contacts of COVID-19 patients to screen out those with atypical infections, even if the contacts present without respiratory symptoms or fever at onset.     

Automatic Detection of COVID-19 Using a Stacked Denoising Convolutional Autoencoder

The exponential increase in new coronavirus disease 2019 ({COVID-19}) cases and deaths has made COVID-19 the leading cause of death in many countries. Thus, in this study, we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images. A stacked denoising convolutional autoencoder (SDCA) model was proposed to classify X-ray images into three classes: normal, pneumonia, and {COVID-19}. The SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy images. The proposed model’s architecture mainly composed of eight autoencoders, which were fed to two dense layers and SoftMax classifiers. The proposed model was evaluated with 6356 images from the datasets from different sources. The experiments and evaluation of the proposed model were applied to an 80/20 training/validation split and for five cross-validation data splitting, respectively. The metrics used for the SDCA model were the classification accuracy, precision, sensitivity, and specificity for both schemes. Our results demonstrated the superiority of the proposed model in classifying X-ray images with high accuracy of 96.8%. Therefore, this model can help physicians accelerate COVID-19 diagnosis.