Development of an Integrated Disposable Device for SARSCoV-2 Nucleic Acid Extraction and Detection

Objective To develop a highly sensitive and rapid nucleic acid detection method for the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Methods We designed,developed,and manufactured an integrated disposable device for SARS-CoV-2 nucleic acid extraction and detection.The precision of the liquid transfer and temperature control was tested.A comparison between our device and a commercial kit for SARS-Cov-2 nucleic acid extraction was performed using real-time fluorescence reverse transcription polymerase chain reaction(RT-PCR).The entire process,from SARS-CoV-2 nucleic acid extraction to amplification,was evaluated.Results The precision of the syringe transfer volume was 19.2±1.9μL(set value was 20),32.2±1.6(set value was 30),and 57.2±3.5(set value was 60).Temperature control in the amplification tube was measured at 60.0±0.0℃(set value was 60)and 95.1±0.2℃(set value was 95)respectively.SARS-Cov-2 nucleic acid extraction yield through the device was 7.10×10^(6) copies/mL,while a commercial kit yielded 2.98×10^(6) copies/mL.The mean time to complete the entire assay,from SARS-CoV-2 nucleic acid extraction to amplification detection,was 36 min and 45 s.The detection limit for SARS-CoV-2 nucleic acid was 250 copies/mL.Conclusion The integrated disposable devices may be used for SARS-CoV-2 Point-of-Care test(POCT).

Rhodamine 6G-based Chemosensor for the Visual Detection of Cu2＋ and Fluorescent Detection of Hg2＋ in Water

A novel water soluble chemosensor 1 based on rhodamine 6G spirolactam scaffold has been synthesized and characterized.Upon addition of a wide range of the environmentally and biologically relevant metal ions,chemosensor 1 shows a colorimetric selective Cu2＋ recognition from colorless to pink confirmed by UV-Vis absorption spectral changes,while it also exhibits a fluorometric selective Hg2＋ recognition by fluorescence spectrometry.An absorption enhancement factor over 17-fold with 1-Cu2＋ complex and a fluorescent enhancement factor over 45-fold with 1-Hg2＋ complex were observed.Their recognition mechanisms were assumed to be a 1：1 stoichiometry for 1-Cu2＋ complex and a 1：2 stoichiometry for 1-Hg2＋ complex,respectively,which were proposed to be different ligation leading to the ring-opening of rhodarnine 6G spirolactam.Furthermore,the detection limits for CU2＋ or Hg2＋ were 3.3 × 10-8 or 1.7x 10-7 mol/L,respectively.

一种具有识别Hg^(2+)和ClO^(-)荧光探针的设计和应用

设计合成了一种含双酯基的1,2,3-三氮唑化合物,与罗丹明B酰肼结合生成了具有“开-关”性质的荧光探针(简称L_(2)),应用光谱学表征了L_(2)的物理化学参数。L_(2)分别在DMF/Tris-HCl(1:1,v/v,pH=6.0,20μmol/L)和MeOH(20μmol/L)溶液中对Hg^(2+)和ClO^(-)显示出高选择性和灵敏性;利用荧光和紫外光谱分别测定了L_(2)对19种金属离子和14种阴离子的光学性能。实验表明,Hg^(2+)和ClO^(-)的存在使得L_(2)在585 nm和576 nm均有一个新的发射峰出现;同时伴随着荧光强度明显的增强,溶液体系发生了裸眼能识别的显色变化,表明Hg^(2+)可以将罗丹明分子的酰肼闭环结构转换为开环结构,并以1:2的比例方式生成了一种新配合物,这也被质谱、工作曲线、核磁滴定和TD-DFT计算的结果所证实;L_(2)对Hg^(2+)和ClO^(-)的检测限分别为7.45 nmol/L和0.67μmol/L。此外,生物活性测定显示L_(2)对HeLa细胞有非常低的毒性,并且可用于HeLa细胞中Hg^(2+)和ClO^(-)的细胞成像,表明L_(2)在体内可进行微测定Hg^(2+)和ClO^(-)的巨大潜力。

基于点击-去点击化学反应的Hg^(2+)荧光探针检测体系构建

基于杂化罗丹明与巯基化合物之间的点击化学反应及巯基化合物与金属汞离子的配位反应导致的去点击化学反应机理,构建香豆素杂化罗丹明探针-1,3,5-苯三硫酚-Hg^(2+)的三元荧光检测体系。在该体系中,探针能够与1,3,5-苯三硫酚中的巯基基团发生加成反应,破坏探针的共轭结构,从而导致探针的红色荧光猝灭。加入Hg^(2+)后,Hg^(2+)与1,3,5-苯三硫酚通过配位作用结合,使其从探针结构上离去,探针的共轭结构及红色荧光恢复。体系的荧光强度与Hg^(2+)在5×10^(-9)~1×10^(-8)、1×10^(-8)~1.1×10^(-7)mol/L浓度范围内呈良好的线性关系,检测限为2×10^(-9)mol/L,等倍量的常见重金属离子对Hg^(2+)的特异性识别无干扰。应用于实际饮用水样品中Hg^(2+)含量检测,回收率可达到86.7%~110.2%。

基于N掺杂Ti_(3)C_(2)MXene量子点的荧光探针用于Hg2+和S2-的传感检测

基于N掺杂Ti_(3)C_(2) MXene量子点(N-Ti_(3)C_(2) MQDs)荧光探针和配位相互作用,构建了一种检测Hg^(2+)和S^(2-)的“开-关-开”型荧光传感新方法.研究发现,制备的N-Ti_(3)C_(2) MQDs发射蓝色荧光(λem=440 nm),荧光量子产率为15.7%.Hg^(2+)与N-Ti_(3)C_(2) MQDs表面的—NH2,—COOH,—OH等官能团产生选择性配位作用,导致N-Ti_(3)C_(2) MQDs体系荧光猝灭.当加入S^(2-)后,由于S^(2-)与Hg^(2+)之间强的结合力,形成HgS沉淀,从而使N-Ti_(3)C_(2) MQDs体系荧光恢复.基于该原理,构建了一种“开-关-开”型荧光传感方法,实现了对Hg^(2+)和S^(2-)的定量检测.N-Ti_(3)C_(2) MQDs探针的荧光强度与Hg^(2+)浓度在0.02~200μmol/L范围内呈良好线性关系,检出限为10 nmol/L(S/N=3);与S^(2-)浓度在0.07~150μmol/L范围内呈良好线性关系,检出限为30 nmol/L(S/N=3).该方法具有成本低、操作简单、灵敏度高和选择性好等特点,并可用于水样中Hg^(2+)和S^(2-)的检测.

介质阻挡放电脱除NO_(x)、SO_(2)和Hg^(0)研究进展

燃煤污染物的排放为环境带来负担,减少燃煤烟气污染是控制大气环境污染的重要措施,脱硫脱硝脱汞则是烟气污染控制的重点。综述现有介质阻挡放电脱除燃煤烟气污染物研究,介绍介质阻挡放电脱除燃煤污染物机理,分析了不同结构介质阻挡放电反应器的放电原理及应用场景,主要包括空间型介质阻挡放电、沿面型介质阻挡放电、共面型介质阻挡放电、填充型介质阻挡放电和两段式介质阻挡放电;归纳了反应器反应参数、气体成分以及气体间的相互作用对脱除NO_(x)、SO_(2)和Hg^(0)的影响。讨论了催化剂协同介质阻挡放电脱除燃煤污染物与单介质阻挡放电脱除污染物的区别。确定高效率脱除NO_(x)、SO_(2)和Hg^(0)的最佳方式。随介质阻挡放电的电压和频率增加,污染物脱除效率呈增加趋势,但频率进一步增加会降低脱除效率。O_(2)在一定范围内可促进NO和Hg的氧化。微量H_(2)O在高能电子作用下产生OH^(-)和HO_(2-),从而促进SO_(2)和Hg氧化;但过量H_(2)O会抑制污染物脱除。NH3能促进NO和SO_(2)的氧化。介质阻挡放电通过激活催化剂表面产生电子和空穴,促进NO、SO_(2)和Hg^(0)的氧化,从而具有更优的污染物氧化性能。

1,3-氧硫戊环为受体的Hg^(2+)荧光探针的设计、合成及性能

Hg^(2+)是毒性最强的重金属离子之一,会造成空气、土壤和水的污染,严重损害人体健康,开发有效的分析方法检测环境体系中的Hg^(2+)尤为重要。荧光探针因其灵敏度高、选择性好、响应速度快、可实时在线检测等优点,已广泛应用于Hg^(2+)检测。以Hg^(2+)促进硫代缩醛的去保护反应设计合成了一种全新的以1,3-氧硫戊环为受体的开启型Hg^(2+)荧光探针[2-(pyren-1-yl)-1,3-oxathiolane,POX],通过^(1)H NMR、^(13)C NMR和HRMS对POX结构进行表征,考察了POX在CH_(3)CH_(2)OH/H_(2)O中对Hg^(2+)的选择性、竞争性、浓度滴定、pH滴定、时间依赖性、检出限和识别机理等。研究结果表明,POX可在较宽的pH范围内对Hg^(2+)快速识别,并表现出高度的选择性和灵敏度;向POX中加入Hg^(2+)后,在386 nm处出现明显的荧光发射峰,表明POX对Hg^(2+)呈现出显著的荧光“开启”效应,其识别过程几乎不受其他金属离子干扰;荧光滴定实验表明POX在Hg^(2+)浓度为0~6.5μmol·L^(-1)范围内具有良好的线性响应(R^(2)=0.9994),检出限为0.168μmol·L^(-1),在实际水样中检测Hg^(2+)的RSD小于2.92%。由于POX合成简单、原料易得且pH适用范围较广,可作为定性和定量检测环境中Hg^(2+)的潜在工具。

氨基酸基氮掺杂荧光碳点的制备及饮料中Hg^(2+)的检测

汞离子是一种高毒性的重金属污染物,人体摄入后会带来健康危害,因此控制食品中的汞离子含量非常重要。以柠檬酸为碳源,不同的氨基酸为氮源掺杂,采用一步水热法制备高性能荧光的碳点(CDs),探究不同氨基酸基氮掺杂对碳点荧光量子产率(QY)的影响,以及这些氨基酸基氮掺杂碳点对汞离子的响应。结果显示,不同氨基酸的碳链长度和官能团对CDs的QY有一定的影响。Hg^(2+)能高效猝灭以甘氨酸(Gly)为氮源掺杂的Gly-CDs的荧光,Gly-CDs具有良好的荧光稳定性,在优化的试验条件下,用于Hg^(2+)检测的线性范围为0.00~7.00μmol/L和8.00~60.00μmol/L,检出限为0.20μmol/L。据此构建的荧光探针用于检测实际饮料样品中的Hg^(2+),回收率在90.08%~107.90%。该方法简便、快速、灵敏、适用于饮料中Hg^(2+)的测定。

空心多孔金纳米酶的制备及其对Hg^(2+)和Ag^+的比色检测

开发基于纳米酶的重金属离子比色传感分析方法,进而克服纳米酶在近中性p H环境催化活性较低的不足,仍然是当前面临的一项重要挑战.文中通过一步法制备了空心多孔金纳米酶(HPAuNSs),采用TEM,XRD,EDS,XPS等对纳米酶进行了表征,并将HPAuNSs用于Hg^(2+)和Ag^(+)的比色分析检测.结果表明,Hg^(2+)和Ag^(+)的加入均显著增强HPAuNSs在近中性条件下的类过氧化物酶(POX)活性,提高了H_(2)O_(2)的分解速率并加速底物3,3',5,5'-四甲基联苯胺(TMB)的氧化反应.利用显色底物TMB氧化后蓝色溶液的颜色变化,成功实现了Hg^(2+)和Ag^(+)的特异性分析检测,检测限分别为7.43nmol·L^(-1)和0.89μmol·L^(-1).构建的纳米酶传感体系打破了纳米酶活性的p H限制,以裸眼可见的溶液体系颜色变化为读出信号,实现了两种重金属离子的检测,为拓宽纳米酶在分析传感领域的应用提供了参考.

Network Intrusion Traffic Detection Based on Feature Extraction

With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.

磁性聚氨基噻唑吸附剂脱除水体Hg^(2+)性能

聚多巴胺(PDA)辅助负载聚氨基噻唑(PAT)法制备了磁性颗粒吸附剂Fe_(3)O_(4)@SiO_(2)@PDA-PAT。对吸附剂进行了XRD、VSM、TG、SEM、XPS、EDS和Zeta电位等表征分析,考察了吸附剂对Hg^(2+)的吸附性能。结果表明,Fe_(3)O_(4)@SiO_(2)@PDA-PAT具有超顺磁性,在pH小于2时Zeta电位为正,pH大于2时Zeta电位为负。在303 K和Hg^(2+)浓度为50 mg/L模拟废水中,当pH为1.3和5.0时,Hg^(2+)的平衡吸附量分别为121.9 mg/g和153.1 mg/g。在强酸(如pH 1.3)和弱酸(如pH 5.0)环境下Fe_(3)O_(4)@SiO_(2)@PDA-PAT吸附Hg^(2+)的过程均是自发过程,且符合二级动力学模型和Langmuir等温吸附模型。强酸环境下(如pH 1.3)Fe_(3)O_(4)@SiO_(2)@PDA-PAT吸附Hg^(2+)是焓驱动的放热过程,弱酸(如pH 5.0)环境下是熵驱动的吸热过程。用2 mol/L混酸(盐酸和硝酸摩尔比为1∶1)作为解吸液可使Hg^(2+)解吸率达91%以上。在303 K、pH 1.3、Hg^(2+)浓度20 mg/L条件下,当Na^(+)、K^(+)、Mg^(2+)、Ca^(2+)、Cu^(2+)、Zn^(2+)、Ni^(2+)质量浓度为Hg^(2+)的20倍时,Fe_(3)O_(4)@SiO_(2)@PDA-PAT的Hg^(2+)平衡吸附量分别下降了33.2%、32.1%、20.6%、26.7%、21.2%、29.6%、17.8%。在模拟海水下,Hg^(2+)吸附量下降40.9%。Fe_(3)O_(4)@SiO_(2)@PDA-PAT具有较好的Hg^(2+)选择性,具有净化海水脱除重金属的应用潜力。

Hg^(2+)光热传感实验的设计与实践

以问题导向学习(PBL)设计了基于温度信号的Hg^(2+)光热传感综合教学实验。在该教学实验中,学生首先基于机械化学,采用湿法研磨策略,制备了具有高光热转换效率的CuS纳米颗粒(CuS NPs),而后基于CuS NPs与Hg^(2+)的沉淀置换反应诱导的CuS NPs分散液的光热信号变化实现了环境水样中Hg^(2+)的检测。将Hg^(2+)光热传感的科研成果融入实验教学环节,探讨本科化学实验的创新教学设计理念,实现了以科学研究思维来指导设计大学化学创新实验,有助于系统全面地培养学生发现问题及解决问题的能力,同时将有效提升学生的基础知识综合利用与实践创新能力,促进高校本科实验教学改革。

Human ACE2-Functionalized Gold“Virus-Trap”Nanostructures for Accurate Capture of SARS-CoV-2 and Single-Virus SERS Detection

The current COVID-19 pandemic urges the extremely sensitive and prompt detection of SARS-CoV-2 virus.Here,we present a Human Angiotensin-converting-enzyme 2(ACE2)-functionalized gold“virus traps”nanostructure as an extremely sensitive SERS biosensor,to selectively capture and rapidly detect S-protein expressed coronavirus,such as the current SARS-CoV-2 in the contaminated water,down to the single-virus level.Such a SERS sensor features extraordinary 106-fold virus enrichment originating from high-affinity of ACE2 with S protein as well as“virus-traps”composed of oblique gold nanoneedles,and 109-fold enhancement of Raman signals originating from multi-component SERS effects.Furthermore,the identification standard of virus signals is established by machine-learning and identification techniques,resulting in an especially low detection limit of 80 copies mL^(−1) for the simulated contaminated water by SARS-CoV-2 virus with complex circumstance as short as 5 min,which is of great significance for achieving real-time monitoring and early warning of coronavirus.Moreover,here-developed method can be used to establish the identification standard for future unknown coronavirus,and immediately enable extremely sensitive and rapid detection of novel virus.

Highly sensitive and stable SERS probes of alternately deposited Ag and Au layers on 3D SiO2 nanogrids for detection of trace mercury ions

The hazard of Hg ion pollution triggers the motivation to explore a fast, sensitive, and reliable detection method. Here, we design and fabricate novel 36-nm-thick Ag-Au composite layers alternately deposited on three-dimensional （3D） periodic SiO2 nanogrids as surface-enhanced Raman scattering （SERS） probes. The SERS effects of the probes depend mainly on the positions and intensities of their localized surface plasmon resonance （LSPR） peaks, which is confirmed by the absorption spectra from finite-difference time-domain （FDTD） calculations. By optimizing the structure and material to maximize the intrinsic electric field enhancement based on the design method of 3D periodic SERS probes proposed, high performance of the Ag-Au/SiO2 nanogrid probes is achieved with the stability further enhanced by annealing. The optimized probes show the outstanding stability with only 4.0% SERS intensity change during 10-day storage, the excellent detection uniformity of 5.78% （RSD）, the detection limit of 5.0 × 10-12 M （1 ppt）, and superior selectivity for Hg ions. The present study renders it possible to realize the rapid and reliable detection of trace heavy metal ions by developing high- performance 3D periodic structure SERS probes by designing novel 3D structure and optimizing plasmonic material.

An ELISA Based on a Truncated Soluble ORF2 Protein for the Detection of PCV2 Antibodies in Domestic Pigs

Postweaning multisystemic wasting syndrome (PMWS) is an important swine disease that is closely associated with porcine circovirus type 2 (PCV2). The capsid protein (Cap protein) is a major structural protein that has at least three immunoreactive regions, and it can be a suitable candidate antigen for detecting the specific antibodies of a PCV2 infection. In the present study, an indirect enzyme-linked immunosorbent assay (TcELISA) based on a truncated soluble Cap protein produced in Escherichia coli (E.coli) was established and validated for the diagnostic PCV2 antibodies in swine. The TcELISA was validated by comparison with an indirect immunofluorescence assay (IIFA). The diagnostic sensitivity (DSN), specificity (DSP), and accuracy of the TcELISA were 88.6%, 90.7% and 89.4%, respectively. The agreement rate was 89.38% between results obtained with TcELISA and IIFA on 113 field sera. A cross-reactivity assay showed that the method was PCV2-specific by comparison with other sera of viral disease. Therefore ,the TcELISA will be helpful for the development of a reliable serology diagnostic test for large scale detection of PCV2 antibodies and for the evaluation of vaccine against PCV2 in swine.

YOLOv2PD:An Efficient Pedestrian Detection Algorithm Using Improved YOLOv2 Model

Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance.The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases.Therefore,the proposed algorithm YOLOv2(“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection(referred to as YOLOv2PD)would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes.The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion(MLFF)strategy,which helps to improve the model’s feature extraction ability.In addition,one repeated convolution layer is removed from the final layer,which in turn reduces the computational complexity without losing any detection accuracy.The proposed algorithm applies the K-means clustering method on the Pascal Voc-2007+2012 pedestrian dataset before training to find the optimal anchor boxes.Both the proposed network structure and the loss function are improved to make the model more accurate and faster while detecting smaller pedestrians.Experimental results show that,at 544×544 image resolution,the proposed model achieves 80.7%average precision(AP),which is 2.1%higher than the YOLOv2 Model on the Pascal Voc-2007+2012 pedestrian dataset.Besides,based on the experimental results,the proposed model YOLOv2PD achieves a good trade-off balance between detection accuracy and real-time speed when evaluated on INRIA and Caltech test pedestrian datasets and achieves state-of-the-art detection results.

SARS-Cov-2 Delayed Tokyo 2020 Olympics: Very Recent Advances in COVID-19 Detection, Treatment, and Vaccine Development Useful Conducting the Games in 2021

The novel coronavirus (SARS-Cov-2) delayed the Tokyo 2020 Games. The traveling by air, rail, road, and sea inside and outside the countries has stopped to contain the virus. The amount of money lost and assistance needed to reschedule and conduct the Games in 2021 have been estimated. With more than one billion population is under the semi-locked down and movement of people is restricted, athletes cannot prepare at home and participate in the Games. The COVID-19 outbreak has spread around the world;it has already infected 5.7 million people and caused 355,000 deaths reported on May 28, 2020 and the figures increasing every day. The publication of this article is important as the postponement of the Olympics has costed Japan $6 billion and the organizers have worked very hard for seven years. If the Games are conducted in 2021, it will be the—beginning of the world recovery—from big COVID-19 pandemic. In this communication, the development in testing, treatment, and vaccine preparation for SARS-Cov-2 have occurred so far in different countries and companies have been discussed to know the possibilities if the pandemic can be overcome and the Games can be conducted in 2021.

Defect detection method based on 2D entropy image segmentation

In order to improve the work efficiency of non-destructive testing(NDT)and the reliability of NDT results,an automatic method to detect defects in the ultrasonic image was researched.According to the characterization of ultrasonic D-scan image,clutter wave suppression and de-noising were presented firstly.Then,the image is processed by binaryzation using KSW 2 D entropy based on image segmentation method.The results showed that,the global threshold based segmentation method was somewhat ineffective for D-scan image because of under-segmentation.Especially,when the image is big in size,small targets which are composed by a small amount of pixels are often undetected.Whereas,local threshold based image segmentation method is effective in recognizing small defects because it takes local image character into account.

Effectiveness of Wastewater-Based Epidemiology as an Early Warning Tool to Detect SARS-CoV-2 (COVID-19)

Medical diagnostic tests to detect Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) for individuals in the United States were initially limited to people who were traveling or symptomatic to track disease incidence due to the cost of providing testing for all people in a community on a routine basis. As an alternative to randomly sampling large groups of people to track disease incidence at significant cost, wastewater-based epidemiology (WBE) is a well-established and cost-effective technique to passively measure the prevalence of disease in communities without requiring invasive testing. WBE can also be used as a forecasting tool since the virus is shed in individuals prior to developing symptoms that might otherwise prompt testing. This study applied the WBE approach to understand its effectiveness as a possible forecasting tool by monitoring the SARS-CoV-2 levels in raw wastewater sampled from sewer lift stations at a large public university campus setting including dormitories, academic buildings, and athletic facilities. The WBE analysis was conducted by sampling from building-specific lift stations and enumerating target viral copies using RT-qPCR analysis. The WBE results were compared with the 7-day rolling averages of confirmed infected individuals for the following week after the wastewater sample analysis. In most cases, changes in the WBE outcomes were followed by similar trends in the clinical data. The positive predictive value of the applied WBE approach was 86% for the following week of the sample collection. In contrast, positive correlations between the two data with Spearmen correlation (rs) ranged from 0.16 to 0.36. A stronger correlation (rs = 0.18 to 0.51) was observed when WBE results were compared with COVID-19 cases identified on the next day of the sampling events. The P value of 0.007 for Dorm A suggests high significance, while moderate significance was observed for the other dormitories (B, C, and D). The outcomes of this investigation demonstrate that WBE can be a valuable tool to track the progression of diseases like COVID-19 seven days before diagnostic cases are confirmed, allowing authorities to take necessary measures in advance and also enable authorities to decide to reopen a facility after a quarantine.

Targeted Sub-Attomole Cancer Biomarker Detection Based on Phase Singularity 2D Nanomaterial-Enhanced Plasmonic Biosensor

Detection of small cancer biomarkers with low molecular weight and a low concentration range has always been challenging yet urgent in many clinical applications such as diagnosing early-stage cancer,monitoring treatment and detecting relapse.Here,a highly enhanced plasmonic biosensor that can overcome this challenge is developed using atomically thin two-dimensional phase change nanomaterial.By precisely engineering the configuration with atomically thin materials,the phase singularity has been successfully achieved with a significantly enhanced lateral position shift effect.Based on our knowledge,it is the first experimental demonstration of a lateral position signal change>340μm at a sensing interface from all optical techniques.With this enhanced plasmonic effect,the detection limit has been experimentally demonstrated to be 10^(-15) mol L^(−1) for TNF-α cancer marker,which has been found in various human diseases including inflammatory diseases and different kinds of cancer.The as-reported novel integration of atomically thin Ge_(2)Sb_(2)Te_(5) with plasmonic substrate, which results in a phase singularity and thus a giant lateral position shift, enables the detection of cancer markers with low molecular weight at femtomolar level. These results will definitely hold promising potential in biomedical application and clinical diagnostics.