Background Biomarkers for predicting suicide risk in hospitalised patients with mental disorders have been understudied.Currently,suicide risk assessment tools based on objective indicators are limited in China.Aims T...Background Biomarkers for predicting suicide risk in hospitalised patients with mental disorders have been understudied.Currently,suicide risk assessment tools based on objective indicators are limited in China.Aims To examine the value of various biomarkers in suicide risk prediction and develop a risk assessment model with clinical utility using machine learning.Methods This cohort study analysed patients with major depressive disorder(MDD) who were hospitalised for the first time between January 2016 and March 2023 from four specialised mental health institutions.A total of 139 features,including biomarker measurements,medical orders and psychological scales,were assessed for analysis.Their suicide risk was evaluated by qualified nurses using Nurse s Global Assessment of Suicide Risk within 1 week after admission.Five machine learning models were trained with 10-fold cross-validation across three hospitals and were externally validated in an independent cohort.The primary performance was assessed using the area under the receiver operating characteristic curve(AUROC).The model was interpreted using the SHapley Additive exPlanations(SHAP) analysis.Biomarker importance was evaluated by comparing model performance with and without these biomarkers.Results Of 3143 patients with MDD included in this study,the incidence of high suicide risk within 1 week after first admission was 660(21.0%).Among all models,the Extreme Gradient Boosting can more effectively predict future risks,with an AUROC higher than 0.8(p<0.001).The SHAP values identified the 10 most important features,including five biomarkers.After clustering analysis,electroconvulsive therapy,physical restraint,β2-microglobulin and triiodothyronine were found to have heterogeneous effects on suicide risk.Combining biomarkers with other data from electronic health records significantly improved the performance and clinical utility of machine learning models based on demographics,diagnosis,laboratory tests,medical orders and psychological scales.Conclusions This study demonstrates the potential for a biomarker-based suicide risk assessment for patients with MDD,emphasising the interaction between biomarkers and therapeutic interventions.展开更多
Regional healthcare platforms collect clinical data from hospitals in specific areas for the purpose of healthcare management.It is a common requirement to reuse the data for clinical research.However,we have to face ...Regional healthcare platforms collect clinical data from hospitals in specific areas for the purpose of healthcare management.It is a common requirement to reuse the data for clinical research.However,we have to face challenges like the inconsistence of terminology in electronic health records (EHR) and the complexities in data quality and data formats in regional healthcare platform.In this paper,we propose methodology and process on constructing large scale cohorts which forms the basis of causality and comparative effectiveness relationship in epidemiology.We firstly constructed a Chinese terminology knowledge graph to deal with the diversity of vocabularies on regional platform.Secondly,we built special disease case repositories (i.e.,heart failure repository) that utilize the graph to search the related patients and to normalize the data.Based on the requirements of the clinical research which aimed to explore the effectiveness of taking statin on 180-days readmission in patients with heart failure,we built a large-scale retrospective cohort with 29647 cases of heart failure patients from the heart failure repository.After the propensity score matching,the study group (n=6346) and the control group (n=6346) with parallel clinical characteristics were acquired.Logistic regression analysis showed that taking statins had a negative correlation with 180-days readmission in heart failure patients.This paper presents the workflow and application example of big data mining based on regional EHR data.展开更多
In early 2020,the new coronavirus pneumonia(COVID-19)broke out in China.Many medical-related products have rapidly appeared in the Artificial Intelligence(AI)field,which have played an important role in fighting again...In early 2020,the new coronavirus pneumonia(COVID-19)broke out in China.Many medical-related products have rapidly appeared in the Artificial Intelligence(AI)field,which have played an important role in fighting against the pandemic.This article summarizes the current research and application status of AI in radiology and pandemic control and analyzes the common problems of AI technology in the research of COVID-19 diagnosis.It mainly includes the thoughts on clinical study design,difficulties in research implementation and challenges in the reliability verification of AI models.In response to the above difficulties,suggestions are proposed for optimizing the scientificity and quality of AI diagnostic research.展开更多
Mycoplasma pneumoniae(M.pneumoniae),primarily transmitted through respiratory droplets when infected individuals cough or sneeze,is a common cause of communityacquired pneumonia,especially among school-age children an...Mycoplasma pneumoniae(M.pneumoniae),primarily transmitted through respiratory droplets when infected individuals cough or sneeze,is a common cause of communityacquired pneumonia,especially among school-age children and adolescents.The infection occurs endemically with an epidemic peak every few years.The worldwide incidence confirmed by direct test methods was reported to be 8.61%between 2017 and 2020 across all age groups[1].展开更多
Background:This study aimed to evaluate the impact of nipple-sparing mastectomy(NSM)and modified radical mastectomy(MRM)on individual survival outcomes and to assess the potential of neoadjuvant systemic therapy(NST)i...Background:This study aimed to evaluate the impact of nipple-sparing mastectomy(NSM)and modified radical mastectomy(MRM)on individual survival outcomes and to assess the potential of neoadjuvant systemic therapy(NST)in reducing surgical intervention requirements.Methods:To develop treatment recommendations for breast cancer patients,five machine learning models were trained.To mitigate bias in treatment allocation,advanced statistical methods,including propensity score matching(PSM)and inverse probability treatment weighting(IPTW),were applied.Results:NSM demonstrated either superior or noninferior survival outcomes compared with MRM across all breast cancer stages,irrespective of adjustments for IPTW and PSM.Among all models and National Comprehensive Cancer Network guidelines,the Balanced Individual and Mixture Effect(BIME)for survival regression model proposed in this study showed the strongest protective effects in treatment recommendations,as evidenced by an IPTW hazard ratio of 0.39(95%CI:0.26–0.59),an IPTW risk difference of 19.66%(95%CI:18.20–21.13),and an IPTW difference in restricted mean survival time of 17.77 months(95%CI:16.37–19.21).NST independently reduced the probability of surgical intervention by 1.4%(95%CI:0.9%–2.0%),with the greatest impact observed in patients with locally advanced breast cancer,in whom a 4.5%reduction(95%CI:3.8%–5.2%)in surgical selection was noted.展开更多
Food allergy(FA)is one of the global human health problems,affecting about 1 in 12 children and 1 in 10 adults worldwide.Probiotics have alleviating effects on FA,but the mechanisms have not been fully understood.We s...Food allergy(FA)is one of the global human health problems,affecting about 1 in 12 children and 1 in 10 adults worldwide.Probiotics have alleviating effects on FA,but the mechanisms have not been fully understood.We sought to explore the therapeutic potential of probiotics Bifidobacterium longum CECT7894(B.longum CECT7894)on anaphylaxis in the ovalbumin(OVA)-induced FA model using an integrated multiomics approaches.The results revealed treatment with B.longum CECT7894 relieved OVA-induced allergic symptoms such as patho-logical changes in small intestine to some extent.Furthermore,probiotics utilization also reduced IgE levels.The transcriptomic data demonstrated that the expressions of genes associated with sphingolipid metabolism were altered in the group received B.longum CECT7894 compared to the sensitized group,including Sgpl1,Trpc1 and Prss8.In addition,the abundances of Sphingobacterium at the genus level,Sphingobacterium sp.21 and B.longum at the species level,which might related with sphingolipid metabolism,were significantly altered by B.longum CECT7894 when compared to the OVA group.Employed the metabolomics analysis,phytosphingosine,C16 sphinganine,sphinganine and sphingosine were identified as significantly changed metabolites and sphingolipid metabolism pathway was significantly enriched by pathway enrichment analysis.The Spearman’s correlation analysis further demonstrated that there were strong correlations between serum biochemical indicators,tran-scripts,gut microbiota and metabolites.Based on the findings described above,from the perspective of genes,gut flora and metabolites changes,it can be concluded that B.longum CECT7894 might suppress OVA-induced FA by regulating the sphingolipid metabolism pathway.展开更多
基金supported by projects from Shanghai Putuo District Municipal Health Committee(ptkwws202413)Shanghai Municipal Health Commission(202340018)+2 种基金Shanghai Hospital Development Center(Data Sharing and Emulation of Clinical Trials,CCS-DASET:SHDC2024CRI008)Shanghai Changning District Municipal Commission of Health(CNWJXY026)School of Innovation and Entrepreneurship,Tongji University(S202310247388,X2024085 and X2024048).
文摘Background Biomarkers for predicting suicide risk in hospitalised patients with mental disorders have been understudied.Currently,suicide risk assessment tools based on objective indicators are limited in China.Aims To examine the value of various biomarkers in suicide risk prediction and develop a risk assessment model with clinical utility using machine learning.Methods This cohort study analysed patients with major depressive disorder(MDD) who were hospitalised for the first time between January 2016 and March 2023 from four specialised mental health institutions.A total of 139 features,including biomarker measurements,medical orders and psychological scales,were assessed for analysis.Their suicide risk was evaluated by qualified nurses using Nurse s Global Assessment of Suicide Risk within 1 week after admission.Five machine learning models were trained with 10-fold cross-validation across three hospitals and were externally validated in an independent cohort.The primary performance was assessed using the area under the receiver operating characteristic curve(AUROC).The model was interpreted using the SHapley Additive exPlanations(SHAP) analysis.Biomarker importance was evaluated by comparing model performance with and without these biomarkers.Results Of 3143 patients with MDD included in this study,the incidence of high suicide risk within 1 week after first admission was 660(21.0%).Among all models,the Extreme Gradient Boosting can more effectively predict future risks,with an AUROC higher than 0.8(p<0.001).The SHAP values identified the 10 most important features,including five biomarkers.After clustering analysis,electroconvulsive therapy,physical restraint,β2-microglobulin and triiodothyronine were found to have heterogeneous effects on suicide risk.Combining biomarkers with other data from electronic health records significantly improved the performance and clinical utility of machine learning models based on demographics,diagnosis,laboratory tests,medical orders and psychological scales.Conclusions This study demonstrates the potential for a biomarker-based suicide risk assessment for patients with MDD,emphasising the interaction between biomarkers and therapeutic interventions.
基金Supported by the National Major Scientific and Technological Special Project for"Significant New Drugs Development’’(No.2018ZX09201008)Special Fund Project for Information Development from Shanghai Municipal Commission of Economy and Information(No.201701013)
文摘Regional healthcare platforms collect clinical data from hospitals in specific areas for the purpose of healthcare management.It is a common requirement to reuse the data for clinical research.However,we have to face challenges like the inconsistence of terminology in electronic health records (EHR) and the complexities in data quality and data formats in regional healthcare platform.In this paper,we propose methodology and process on constructing large scale cohorts which forms the basis of causality and comparative effectiveness relationship in epidemiology.We firstly constructed a Chinese terminology knowledge graph to deal with the diversity of vocabularies on regional platform.Secondly,we built special disease case repositories (i.e.,heart failure repository) that utilize the graph to search the related patients and to normalize the data.Based on the requirements of the clinical research which aimed to explore the effectiveness of taking statin on 180-days readmission in patients with heart failure,we built a large-scale retrospective cohort with 29647 cases of heart failure patients from the heart failure repository.After the propensity score matching,the study group (n=6346) and the control group (n=6346) with parallel clinical characteristics were acquired.Logistic regression analysis showed that taking statins had a negative correlation with 180-days readmission in heart failure patients.This paper presents the workflow and application example of big data mining based on regional EHR data.
基金the The Scientific Project of Shanghai Municipal Health Commission(No.ZHYL0202).
文摘In early 2020,the new coronavirus pneumonia(COVID-19)broke out in China.Many medical-related products have rapidly appeared in the Artificial Intelligence(AI)field,which have played an important role in fighting against the pandemic.This article summarizes the current research and application status of AI in radiology and pandemic control and analyzes the common problems of AI technology in the research of COVID-19 diagnosis.It mainly includes the thoughts on clinical study design,difficulties in research implementation and challenges in the reliability verification of AI models.In response to the above difficulties,suggestions are proposed for optimizing the scientificity and quality of AI diagnostic research.
基金funded by the Science and Technology Commission of Shanghai Municipality(No.21511104502)Shanghai Hospital Development Center(No.SHDC22022221).
文摘Mycoplasma pneumoniae(M.pneumoniae),primarily transmitted through respiratory droplets when infected individuals cough or sneeze,is a common cause of communityacquired pneumonia,especially among school-age children and adolescents.The infection occurs endemically with an epidemic peak every few years.The worldwide incidence confirmed by direct test methods was reported to be 8.61%between 2017 and 2020 across all age groups[1].
基金supported by the Medical Discipline Construction Health Committee Project of Pudong,Shanghai(Grant No.PWYgV2021‐02).
文摘Background:This study aimed to evaluate the impact of nipple-sparing mastectomy(NSM)and modified radical mastectomy(MRM)on individual survival outcomes and to assess the potential of neoadjuvant systemic therapy(NST)in reducing surgical intervention requirements.Methods:To develop treatment recommendations for breast cancer patients,five machine learning models were trained.To mitigate bias in treatment allocation,advanced statistical methods,including propensity score matching(PSM)and inverse probability treatment weighting(IPTW),were applied.Results:NSM demonstrated either superior or noninferior survival outcomes compared with MRM across all breast cancer stages,irrespective of adjustments for IPTW and PSM.Among all models and National Comprehensive Cancer Network guidelines,the Balanced Individual and Mixture Effect(BIME)for survival regression model proposed in this study showed the strongest protective effects in treatment recommendations,as evidenced by an IPTW hazard ratio of 0.39(95%CI:0.26–0.59),an IPTW risk difference of 19.66%(95%CI:18.20–21.13),and an IPTW difference in restricted mean survival time of 17.77 months(95%CI:16.37–19.21).NST independently reduced the probability of surgical intervention by 1.4%(95%CI:0.9%–2.0%),with the greatest impact observed in patients with locally advanced breast cancer,in whom a 4.5%reduction(95%CI:3.8%–5.2%)in surgical selection was noted.
基金funded by grants from the Dipro Medical Research Foundation(DiPRO/2019Oct/7894/SHRJ02),China.
文摘Food allergy(FA)is one of the global human health problems,affecting about 1 in 12 children and 1 in 10 adults worldwide.Probiotics have alleviating effects on FA,but the mechanisms have not been fully understood.We sought to explore the therapeutic potential of probiotics Bifidobacterium longum CECT7894(B.longum CECT7894)on anaphylaxis in the ovalbumin(OVA)-induced FA model using an integrated multiomics approaches.The results revealed treatment with B.longum CECT7894 relieved OVA-induced allergic symptoms such as patho-logical changes in small intestine to some extent.Furthermore,probiotics utilization also reduced IgE levels.The transcriptomic data demonstrated that the expressions of genes associated with sphingolipid metabolism were altered in the group received B.longum CECT7894 compared to the sensitized group,including Sgpl1,Trpc1 and Prss8.In addition,the abundances of Sphingobacterium at the genus level,Sphingobacterium sp.21 and B.longum at the species level,which might related with sphingolipid metabolism,were significantly altered by B.longum CECT7894 when compared to the OVA group.Employed the metabolomics analysis,phytosphingosine,C16 sphinganine,sphinganine and sphingosine were identified as significantly changed metabolites and sphingolipid metabolism pathway was significantly enriched by pathway enrichment analysis.The Spearman’s correlation analysis further demonstrated that there were strong correlations between serum biochemical indicators,tran-scripts,gut microbiota and metabolites.Based on the findings described above,from the perspective of genes,gut flora and metabolites changes,it can be concluded that B.longum CECT7894 might suppress OVA-induced FA by regulating the sphingolipid metabolism pathway.
