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 disposab...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).展开更多
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(...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%.展开更多
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 ...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.展开更多
Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and...Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and time-consuming;thus,an efficient and accurate measurement method is needed.In recent years,classification-based deep learning and computer vision have shown promise in solving various classification tasks.Results In this study,we propose a new approach for detecting the lint percentage using MobileNetV2 and transfer learning.The model is deployed on a lint percentage detection instrument,which can rapidly and accurately determine the lint percentage of seed cotton.We evaluated the performance of the proposed approach using a dataset comprising 66924 seed cotton images from different regions of China.The results of the experiments showed that the model with transfer learning achieved an average classification accuracy of 98.43%,with an average precision of 94.97%,an average recall of 95.26%,and an average F1-score of 95.20%.Furthermore,the proposed classification model achieved an average accuracy of 97.22%in calculating the lint percentage,showing no significant difference from the performance of experts(independent-sample t-test,t=0.019,P=0.860).Conclusion This study demonstrated the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton.The proposed approach is a promising alternative to traditional methods,providing a rapid and accurate solution for the industry.展开更多
A novel strategy was developed to fabricate FeNx-doped carbon quantum dots(Fe-N-CQDs)to detect Cu^(2+) ions selectively as a fluorescence probe.The Fe-N-CQDs were synthesized by an efficient electrolysis of a carbon c...A novel strategy was developed to fabricate FeNx-doped carbon quantum dots(Fe-N-CQDs)to detect Cu^(2+) ions selectively as a fluorescence probe.The Fe-N-CQDs were synthesized by an efficient electrolysis of a carbon cloth electrode,which was coated with monoatomic ironanchored nitrogen-doped carbon(Fe-N-C).The obtained Fe-N-CQDs emitted blue fluorescence and possessed a quantum yield(QY)of 7.5%.An extremely wide linear relationship between the Cu^(2+) concentration and the fluorescence intensity was obtained in the range from 100 nmol L^(-1) to 1000 nmol L^(-1)(R^(2)=0.997),and the detection limit was calculated as 59 nmol L^(-1).Moreover,the Fe-N-CQDs demonstrated wide range pH compatibility between 2 and 13 due to the coordination between pyridine nitrogen and Fe^(3+),which dramatically reduced the affection of the protonation and deprotonation process between H^(+) and Fe-N-CQDs.It is notable that the Fe-N-CQDs exhibited a rapid response in Cu^(2+) detection,where stable quenching can be completed in 7 s.The mechanism of excellent selective detection of Cu^(2+) was revealed by energy level simulation that the LUMO level of Fe-N-CQDs(-4.37 eV)was close to the redox potential of Cu^(2+),thus facilitating the electron transport from Fe-N-CQDs to Cu^(2+).展开更多
Objective Late 2019 witnessed the outbreak and widespread transmission of coronavirus disease 2019(COVID-19),a new,highly contagious disease caused by novel severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)....Objective Late 2019 witnessed the outbreak and widespread transmission of coronavirus disease 2019(COVID-19),a new,highly contagious disease caused by novel severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Consequently,considerable attention has been paid to the development of new diagnostic tools for the early detection of SARS-CoV-2.Methods In this study,a new poly-N-isopropylacrylamide microgel-based electrochemical sensor was explored to detect the SARS-CoV-2 spike protein(S protein)in human saliva.The microgel was composed of a copolymer of N-isopropylacrylamide and acrylic acid,and gold nanoparticles were encapsulated within the microgel through facile and economical fabrication.The electrochemical performance of the sensor was evaluated through differential pulse voltammetry.Results Under optimal experimental conditions,the linear range of the sensor was 10-13-10-9 mg/m L,whereas the detection limit was 9.55 fg/mL.Furthermore,the S protein was instilled in artificial saliva as the infected human saliva model,and the sensing platform showed satisfactory detection capability.Conclusion The sensing platform exhibited excellent specificity and sensitivity in detecting spike protein,indicating its potential application for the time-saving and inexpensive detection of SARS-CoV-2.展开更多
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 antibodi...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.展开更多
Polythiophene/WO3（PTP/WO3）organic-inorganic hybrids were synthesized by an in situ chemical oxidative polymerization method,and char- acterized by X-ray diffraction（XRD）,transmission electron microscopy（TEM）and ...Polythiophene/WO3（PTP/WO3）organic-inorganic hybrids were synthesized by an in situ chemical oxidative polymerization method,and char- acterized by X-ray diffraction（XRD）,transmission electron microscopy（TEM）and thermo-gravimetric analysis（TGA）.The Polythiophene/ WO3 hybrids have higher thermal stability than pure polythiophene,which is beneficial to potential application as chemical sensors.Gas sensing measurements demonstrate that the gas sensor based on the Polythiophene/WO3 hybrids has high response and good selectivity for de- tecting NO2 of ppm level at low temperature.Both the operating temperature and PTP contents have an influence on the response of PTP/WO3 hybrids to NO2.The 10 wt%PTP/WO3 hybrid showed the highest response at low operating temperature of 70-C.It is expected that the PTP/WO3 hybrids can be potentially used as gas sensor material for detecting the low concentration of NO2 at low temperature.展开更多
Ara h 2是花生主要过敏原之一,为开发食物中Ara h 2过敏原成分的快速检测方法,减少因误食导致花生过敏事件的发生,该研究采用鼠源单克隆抗体作为捕获抗体、兔源多克隆抗体作为检测抗体,通过棋盘法优化抗体工作浓度,建立了一种检测花生...Ara h 2是花生主要过敏原之一,为开发食物中Ara h 2过敏原成分的快速检测方法,减少因误食导致花生过敏事件的发生,该研究采用鼠源单克隆抗体作为捕获抗体、兔源多克隆抗体作为检测抗体,通过棋盘法优化抗体工作浓度,建立了一种检测花生过敏原Ara h 2的间接双抗夹心化学发光酶免疫分析法,并对该方法的灵敏度、准确度、精密度和特异性进行评价。该方法的检出限为1.085 ng/mL,线性范围为3.12~200 ng/mL,添加回收率为78.30%~94.39%,批内和批间变异系数均小于10%,且特异性良好,与其他常见食物过敏原无交叉反应。该方法与相同抗体所建立的间接双抗夹心酶联免疫吸附测定(enzyme-linked immunosorbent assay, ELISA)方法相比,在灵敏度上表现出一定优势。该研究开发的化学发光酶免疫分析法可对花生食品生产过程中和消费前的Ara h 2过敏原成分检测提供可靠的技术支持。展开更多
Theε-Ga_(2)O_(3) thin film was grown on sapphire substrate by using metalorganic chemical vapor deposition(MOCVD)method,and then was used to fabricate a deep-ultraviolet(DUV)photodetector(PD).Theε-Ga_(2)O_(3) thin f...Theε-Ga_(2)O_(3) thin film was grown on sapphire substrate by using metalorganic chemical vapor deposition(MOCVD)method,and then was used to fabricate a deep-ultraviolet(DUV)photodetector(PD).Theε-Ga_(2)O_(3) thin film shown good crystal quality and decent surface morphology.Irradiated by a 254-nm DUV light,the photodetector displayed good optoelectronic performance and high wavelength selectivity,such as photoresponsivity(R)of 175.69 A/W,detectivity(D*)of 2.46×10^(15) Jones,external quantum efficiency(EQE)of 8.6×10^(4)%and good photocurrent-intensity linearity,suggesting decent DUV photosensing performance.At 5 V and under illumination with light intensity of 800μW/cm2,the photocurrent gain is as high as 859 owing to the recycling gain mechanism and delayed carrier recombination;and the photocurrent gain decreases as the incident light intensity increases because of the recombination of photogenerated carriers by the large photon flux.展开更多
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 ...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.展开更多
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...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.展开更多
基金supported by National Key R&D Program of China[2021YFC2301103 and 2022YFE0202600]Shenzhen Science and Technology Program[JSGG20220606142605011].
文摘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).
基金supported by Tianshan Talent Training Project-Xinjiang Science and Technology Innovation Team Program(2023TSYCTD).
文摘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%.
文摘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.
基金National Natural Science Foundation of China(Grant number:11904327,61905223,and 62073299)Training Plan of Young Backbone Teachers in Universities of Henan Province(2023GGJS087)+1 种基金Henan Provincial Science and Technology Research Project(222102110279,222102210085,and 242102210157)Project of Central Plains Science and Technology Innovation Leading Talents(224200510026).
文摘Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and time-consuming;thus,an efficient and accurate measurement method is needed.In recent years,classification-based deep learning and computer vision have shown promise in solving various classification tasks.Results In this study,we propose a new approach for detecting the lint percentage using MobileNetV2 and transfer learning.The model is deployed on a lint percentage detection instrument,which can rapidly and accurately determine the lint percentage of seed cotton.We evaluated the performance of the proposed approach using a dataset comprising 66924 seed cotton images from different regions of China.The results of the experiments showed that the model with transfer learning achieved an average classification accuracy of 98.43%,with an average precision of 94.97%,an average recall of 95.26%,and an average F1-score of 95.20%.Furthermore,the proposed classification model achieved an average accuracy of 97.22%in calculating the lint percentage,showing no significant difference from the performance of experts(independent-sample t-test,t=0.019,P=0.860).Conclusion This study demonstrated the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton.The proposed approach is a promising alternative to traditional methods,providing a rapid and accurate solution for the industry.
基金the National Natural Science Foundation of China(Nos.21776302 and 21776308)the Science Foundation of China University of Petroleum,Beijing(No.2462020YXZZ033).
文摘A novel strategy was developed to fabricate FeNx-doped carbon quantum dots(Fe-N-CQDs)to detect Cu^(2+) ions selectively as a fluorescence probe.The Fe-N-CQDs were synthesized by an efficient electrolysis of a carbon cloth electrode,which was coated with monoatomic ironanchored nitrogen-doped carbon(Fe-N-C).The obtained Fe-N-CQDs emitted blue fluorescence and possessed a quantum yield(QY)of 7.5%.An extremely wide linear relationship between the Cu^(2+) concentration and the fluorescence intensity was obtained in the range from 100 nmol L^(-1) to 1000 nmol L^(-1)(R^(2)=0.997),and the detection limit was calculated as 59 nmol L^(-1).Moreover,the Fe-N-CQDs demonstrated wide range pH compatibility between 2 and 13 due to the coordination between pyridine nitrogen and Fe^(3+),which dramatically reduced the affection of the protonation and deprotonation process between H^(+) and Fe-N-CQDs.It is notable that the Fe-N-CQDs exhibited a rapid response in Cu^(2+) detection,where stable quenching can be completed in 7 s.The mechanism of excellent selective detection of Cu^(2+) was revealed by energy level simulation that the LUMO level of Fe-N-CQDs(-4.37 eV)was close to the redox potential of Cu^(2+),thus facilitating the electron transport from Fe-N-CQDs to Cu^(2+).
基金supported by Key Research and Development Project of Hubei Province[Number 2020BCB022]Opening Fund of State Key Laboratory of Virology of Wuhan University[grant number 2022KF002]+2 种基金Royal Society International Exchanges Scheme[IECNSFC201116]The Academy of Medical Sciences/Wellcome Trust[Springboard grantSBF007100054]。
文摘Objective Late 2019 witnessed the outbreak and widespread transmission of coronavirus disease 2019(COVID-19),a new,highly contagious disease caused by novel severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Consequently,considerable attention has been paid to the development of new diagnostic tools for the early detection of SARS-CoV-2.Methods In this study,a new poly-N-isopropylacrylamide microgel-based electrochemical sensor was explored to detect the SARS-CoV-2 spike protein(S protein)in human saliva.The microgel was composed of a copolymer of N-isopropylacrylamide and acrylic acid,and gold nanoparticles were encapsulated within the microgel through facile and economical fabrication.The electrochemical performance of the sensor was evaluated through differential pulse voltammetry.Results Under optimal experimental conditions,the linear range of the sensor was 10-13-10-9 mg/m L,whereas the detection limit was 9.55 fg/mL.Furthermore,the S protein was instilled in artificial saliva as the infected human saliva model,and the sensing platform showed satisfactory detection capability.Conclusion The sensing platform exhibited excellent specificity and sensitivity in detecting spike protein,indicating its potential application for the time-saving and inexpensive detection of SARS-CoV-2.
文摘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.
基金financially supported by the National Natural Science Foundation of China(No.20871071)the Science and Technology Commission Foundation of Tianjin(No.09JCYBJC03600 and 10JCYBJC03900)
文摘Polythiophene/WO3（PTP/WO3）organic-inorganic hybrids were synthesized by an in situ chemical oxidative polymerization method,and char- acterized by X-ray diffraction（XRD）,transmission electron microscopy（TEM）and thermo-gravimetric analysis（TGA）.The Polythiophene/ WO3 hybrids have higher thermal stability than pure polythiophene,which is beneficial to potential application as chemical sensors.Gas sensing measurements demonstrate that the gas sensor based on the Polythiophene/WO3 hybrids has high response and good selectivity for de- tecting NO2 of ppm level at low temperature.Both the operating temperature and PTP contents have an influence on the response of PTP/WO3 hybrids to NO2.The 10 wt%PTP/WO3 hybrid showed the highest response at low operating temperature of 70-C.It is expected that the PTP/WO3 hybrids can be potentially used as gas sensor material for detecting the low concentration of NO2 at low temperature.
文摘Ara h 2是花生主要过敏原之一,为开发食物中Ara h 2过敏原成分的快速检测方法,减少因误食导致花生过敏事件的发生,该研究采用鼠源单克隆抗体作为捕获抗体、兔源多克隆抗体作为检测抗体,通过棋盘法优化抗体工作浓度,建立了一种检测花生过敏原Ara h 2的间接双抗夹心化学发光酶免疫分析法,并对该方法的灵敏度、准确度、精密度和特异性进行评价。该方法的检出限为1.085 ng/mL,线性范围为3.12~200 ng/mL,添加回收率为78.30%~94.39%,批内和批间变异系数均小于10%,且特异性良好,与其他常见食物过敏原无交叉反应。该方法与相同抗体所建立的间接双抗夹心酶联免疫吸附测定(enzyme-linked immunosorbent assay, ELISA)方法相比,在灵敏度上表现出一定优势。该研究开发的化学发光酶免疫分析法可对花生食品生产过程中和消费前的Ara h 2过敏原成分检测提供可靠的技术支持。
基金the National Natural Science Foundation of China(Ganrt No.62004047)。
文摘Theε-Ga_(2)O_(3) thin film was grown on sapphire substrate by using metalorganic chemical vapor deposition(MOCVD)method,and then was used to fabricate a deep-ultraviolet(DUV)photodetector(PD).Theε-Ga_(2)O_(3) thin film shown good crystal quality and decent surface morphology.Irradiated by a 254-nm DUV light,the photodetector displayed good optoelectronic performance and high wavelength selectivity,such as photoresponsivity(R)of 175.69 A/W,detectivity(D*)of 2.46×10^(15) Jones,external quantum efficiency(EQE)of 8.6×10^(4)%and good photocurrent-intensity linearity,suggesting decent DUV photosensing performance.At 5 V and under illumination with light intensity of 800μW/cm2,the photocurrent gain is as high as 859 owing to the recycling gain mechanism and delayed carrier recombination;and the photocurrent gain decreases as the incident light intensity increases because of the recombination of photogenerated carriers by the large photon flux.
基金the National Natural Science Foundation of China(No.51471182)this work is also supported by Shanghai international science and Technology Cooperation Fund(No.17520711700)the National Key Research and Development Project(No.2017YFB0310600).
文摘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.
基金supported by a project from National Key Technology R&D Program in the 11th Fiveyear Plan of China (2006BAD06A12)
文摘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.
