Objective To identify potential serum biomarkers for distinguishing between latent tuberculosis infection（LTBI） and active tuberculosis（TB）. Methods A proteome microarray containing 4,262 antigens was used for scr...Objective To identify potential serum biomarkers for distinguishing between latent tuberculosis infection（LTBI） and active tuberculosis（TB）. Methods A proteome microarray containing 4,262 antigens was used for screening serum biomarkers of 40 serum samples from patients with LTBI and active TB at the systems level. The interaction network and functional classification of differentially expressed antigens were analyzed using STRING 10.0 and the TB database, respectively. Enzyme-linked immunosorbent assays（ELISA） were used to validate candidate antigens further using 279 samples. The diagnostic performances of candidate antigens were evaluated by receiver operating characteristic curve（ROC） analysis. Both antigen combination and logistic regression analysis were used to improve diagnostic ability. Results Microarray results showed that levels of 152 Mycobacterium tuberculosis（Mtb）-antigenspecific IgG were significantly higher in active TB patients than in LTBI patients（P 〈 0.05）, and these differentially expressed antigens showed stronger associations with each other and were involved in various biological processes. Eleven candidate antigens were further validated using ELISA and showed consistent results in microarray analysis. ROC analysis showed that antigens Rv2031 c, Rv1408, and Rv2421 c had higher areas under the curve（AUCs） of 0.8520, 0.8152, and 0.7970, respectively. In addition, both antigen combination and logistic regression analysis improved the diagnostic ability. Conclusion Several antigens have the potential to serve as serum biomarkers for discrimination between LTBI and active TB.展开更多
Nowadays,with the development of technology,the safety performance of traditional single-or dual〓〓mode luminescent materials used for anti-counterfeiting has been significantly reduced due to their single and predic...Nowadays,with the development of technology,the safety performance of traditional single-or dual〓〓mode luminescent materials used for anti-counterfeiting has been significantly reduced due to their single and predictable readout process.In this work,we have synthesized a rare earth single-doped material Mg_(2)SnO_(4):Tb^(3+)with persistent luminescence,photo-luminescence and photo-stimulated luminescence and found a distinction of emission color between photo-luminescence,persistent luminescence and photo-stimulated luminescence.Thus,a multimodal dichromatic luminescence was achieved by single doping.Based on these luminescence properties,we made chalks from the obtained samples to write confidential information on a black substrate and designed a multilevel encrypted strategy for anti-counterfeiting,which proves that the obtained materials have great application potential in the field of information security.展开更多
Introduction:We explored risk factors for latent tuberculosis infection(LTBI)and developed a risk prediction model using machine learning algorithms.Methods:Patients with active pulmonary TB in months 3 to 6 of anti-T...Introduction:We explored risk factors for latent tuberculosis infection(LTBI)and developed a risk prediction model using machine learning algorithms.Methods:Patients with active pulmonary TB in months 3 to 6 of anti-TB treatment in Henan Province,China,July–September 2024 were selected as index cases.Close contacts identified through epidemiological investigation underwent tuberculinpurified protein derivative testing to determine LTBI status.Face-to-face questionnaires were conducted to collect epidemiological data.The dataset was divided into training and testing sets(6:4),using a fixed random seed.Five models—logistic regression(LR),decision tree(DT),random forest(RF),support vector machines(SVM),and multilayer perceptron(MLP)—were trained and evaluated using the mean squared error(MSE)and coefficient of determination.The test set was subjected to external validation.Receiver operating characteristic curve analysis,area under the curve(AUC),and F1-scores were used to quantify predictive performance.Results:Among 795 close contacts,LTBI prevalence was 401(50.5%).By MSE,models ranked:SVM(0.121),RF(0.165),DT(0.197),LR(0.229),and MLP(0.233).SVM identified five key predictors:contact type of index case,key population classification,residential area,frequency of participation in group activities,and etiological results.Internal validation showed strong performance(AUC=0.921,F1=0.858),whereas external validation showed moderate performance(AUC=0.752,F1=0.694).Conclusion:The SVM model incorporating contact type of index case,key population classification,residential area,frequency of group activity participation,and etiological results demonstrated robust predictive value for LTBI risk.This model shows promise for the targeted screening and management of high-risk populations.展开更多
Active tuberculosis infection is a major health concern in the world.Each year,millions of people die of tuberculosis,especially in third-world countries.Though the World Health Organization has recently reported the ...Active tuberculosis infection is a major health concern in the world.Each year,millions of people die of tuberculosis,especially in third-world countries.Though the World Health Organization has recently reported the rate of mortality by this disease is declining by 3%yearly,active tuberculosis infection is still endangering human health seriously.In addition,there are many people who have a latent tuberculosis infection,and these people do not seek treatment because they have no clinical symptoms.It is true that current specific laboratory examination approaches are capable of diagnosing active tuberculosis infection promptly and accurately.But sensitivity and specificity of current diagnostic approaches are at a low level.However,the development of new nanomaterials allows more scientists to combine diagnostic methods with nanotechnology.Recently,a novel Nanodisk mass spectrometry method has been reported.Mycobacterium tuberculosis-specific peptides are enriched using an antibody-conjugated nanodisk,allowing for rapid,quantitative detection of the serum-specific antigen for active tuberculosis infection.This method overcomes the shortcomings of poor sensitivity and long turnaround time associated with current diagnostic approaches.This review discusses the current status and progress of specific laboratory examination methods of active tuberculosis diagnosis and compares the newest diagnostic techniques.展开更多
基金supported by the Natural Science Foundation of China[No:81470091]Beijing Municipal Administration of Hospitals Ascent Plan[DFL20151501]
文摘Objective To identify potential serum biomarkers for distinguishing between latent tuberculosis infection（LTBI） and active tuberculosis（TB）. Methods A proteome microarray containing 4,262 antigens was used for screening serum biomarkers of 40 serum samples from patients with LTBI and active TB at the systems level. The interaction network and functional classification of differentially expressed antigens were analyzed using STRING 10.0 and the TB database, respectively. Enzyme-linked immunosorbent assays（ELISA） were used to validate candidate antigens further using 279 samples. The diagnostic performances of candidate antigens were evaluated by receiver operating characteristic curve（ROC） analysis. Both antigen combination and logistic regression analysis were used to improve diagnostic ability. Results Microarray results showed that levels of 152 Mycobacterium tuberculosis（Mtb）-antigenspecific IgG were significantly higher in active TB patients than in LTBI patients（P 〈 0.05）, and these differentially expressed antigens showed stronger associations with each other and were involved in various biological processes. Eleven candidate antigens were further validated using ELISA and showed consistent results in microarray analysis. ROC analysis showed that antigens Rv2031 c, Rv1408, and Rv2421 c had higher areas under the curve（AUCs） of 0.8520, 0.8152, and 0.7970, respectively. In addition, both antigen combination and logistic regression analysis improved the diagnostic ability. Conclusion Several antigens have the potential to serve as serum biomarkers for discrimination between LTBI and active TB.
基金supported by the National Natural Science Foundation of China(Grant No.21871122 and 21431002)the Fundamental Research Funds for the Central Universities(Grant No.lzujbky-2020-kb13).
文摘Nowadays,with the development of technology,the safety performance of traditional single-or dual〓〓mode luminescent materials used for anti-counterfeiting has been significantly reduced due to their single and predictable readout process.In this work,we have synthesized a rare earth single-doped material Mg_(2)SnO_(4):Tb^(3+)with persistent luminescence,photo-luminescence and photo-stimulated luminescence and found a distinction of emission color between photo-luminescence,persistent luminescence and photo-stimulated luminescence.Thus,a multimodal dichromatic luminescence was achieved by single doping.Based on these luminescence properties,we made chalks from the obtained samples to write confidential information on a black substrate and designed a multilevel encrypted strategy for anti-counterfeiting,which proves that the obtained materials have great application potential in the field of information security.
基金Supported by grants from Henan Provincial Science and Technology Development Program(Grant No.242102311109).
文摘Introduction:We explored risk factors for latent tuberculosis infection(LTBI)and developed a risk prediction model using machine learning algorithms.Methods:Patients with active pulmonary TB in months 3 to 6 of anti-TB treatment in Henan Province,China,July–September 2024 were selected as index cases.Close contacts identified through epidemiological investigation underwent tuberculinpurified protein derivative testing to determine LTBI status.Face-to-face questionnaires were conducted to collect epidemiological data.The dataset was divided into training and testing sets(6:4),using a fixed random seed.Five models—logistic regression(LR),decision tree(DT),random forest(RF),support vector machines(SVM),and multilayer perceptron(MLP)—were trained and evaluated using the mean squared error(MSE)and coefficient of determination.The test set was subjected to external validation.Receiver operating characteristic curve analysis,area under the curve(AUC),and F1-scores were used to quantify predictive performance.Results:Among 795 close contacts,LTBI prevalence was 401(50.5%).By MSE,models ranked:SVM(0.121),RF(0.165),DT(0.197),LR(0.229),and MLP(0.233).SVM identified five key predictors:contact type of index case,key population classification,residential area,frequency of participation in group activities,and etiological results.Internal validation showed strong performance(AUC=0.921,F1=0.858),whereas external validation showed moderate performance(AUC=0.752,F1=0.694).Conclusion:The SVM model incorporating contact type of index case,key population classification,residential area,frequency of group activity participation,and etiological results demonstrated robust predictive value for LTBI risk.This model shows promise for the targeted screening and management of high-risk populations.
基金financial support by the National Natural Scientific Foundation of China(Grant No.81571835,81671737,61503246 and 81672247)Shanghai Science and Technology Fund No.15DZ2252000Shanghai Municipal Commission of Economy and Informatization Technology Fund NO.XC-ZXSJ-02-2016-05.
文摘Active tuberculosis infection is a major health concern in the world.Each year,millions of people die of tuberculosis,especially in third-world countries.Though the World Health Organization has recently reported the rate of mortality by this disease is declining by 3%yearly,active tuberculosis infection is still endangering human health seriously.In addition,there are many people who have a latent tuberculosis infection,and these people do not seek treatment because they have no clinical symptoms.It is true that current specific laboratory examination approaches are capable of diagnosing active tuberculosis infection promptly and accurately.But sensitivity and specificity of current diagnostic approaches are at a low level.However,the development of new nanomaterials allows more scientists to combine diagnostic methods with nanotechnology.Recently,a novel Nanodisk mass spectrometry method has been reported.Mycobacterium tuberculosis-specific peptides are enriched using an antibody-conjugated nanodisk,allowing for rapid,quantitative detection of the serum-specific antigen for active tuberculosis infection.This method overcomes the shortcomings of poor sensitivity and long turnaround time associated with current diagnostic approaches.This review discusses the current status and progress of specific laboratory examination methods of active tuberculosis diagnosis and compares the newest diagnostic techniques.
