BACKGROUND Over the past few years,research into the pathogenesis of colon cancer has progressed rapidly,and cuproptosis is an emerging mode of cellular apoptosis.Exploring the relationship between colon cancer and cu...BACKGROUND Over the past few years,research into the pathogenesis of colon cancer has progressed rapidly,and cuproptosis is an emerging mode of cellular apoptosis.Exploring the relationship between colon cancer and cuproptosis benefits in identifying novel biomarkers and even improving the outcome of the disease.AIM To look at the prognostic relationship between colon cancer and the genes associated with cuproptosis and the immune system in patients.The main purpose was to assess whether reasonable induction of these biomarkers reduces mortality among patients with colon cancers.METHOD Data obtained from The Cancer Genome Atlas and Gene Expression Omnibus and the Genotype-Tissue Expression were used in differential analysis to explore differential expression genes associated with cuproptosis and immune activation.The least absolute shrinkage and selection operator and Cox regression algorithm was applied to build a cuproptosis-and immune-related combination model,and the model was utilized for principal component analysis and survival analysis to observe the survival and prognosis of the patients.A series of statistically meaningful transcriptional analysis results demonstrated an intrinsic relationship between cuproptosis and the micro-environment of colon cancer.RESULTS Once prognostic characteristics were obtained,the CDKN2A and DLAT genes related to cuproptosis were strongly linked to colon cancer:The first was a risk factor,whereas the second was a protective factor.The finding of the validation analysis showed that the comprehensive model associated with cuproptosis and immunity was statistically significant.Within the component expressions,the expressions of HSPA1A,CDKN2A,and UCN3 differed markedly.Transcription analysis primarily reflects the differential activation of related immune cells and pathways.Furthermore,genes linked to immune checkpoint inhibitors were expressed differently between the subgroups,which may reveal the mechanism of worse prognosis and the different sensitivities of chemotherapy.CONCLUSION The prognosis of the high-risk group evaluated in the combined model was poorer,and cuproptosis was highly correlated with the prognosis of colon cancer.It is possible that we may be able to improve patients’prognosis by regulating the gene expression to intervene the risk score.展开更多
Objective:Through integrated bioinformatics analysis,the goal of this work was to find new,characterised N7-methylguanosine modification-related long non-coding RNAs(m7G-lncRNAs)that might be used to predict the progn...Objective:Through integrated bioinformatics analysis,the goal of this work was to find new,characterised N7-methylguanosine modification-related long non-coding RNAs(m7G-lncRNAs)that might be used to predict the prognosis of laryngeal squamous cell carcinoma(LSCC).Methods:The clinical data and LSCC gene expression data for the current investigation were initially retrieved from the TCGA database&sanitised.Then,using co-expression analysis of m7G-associated mRNAs&lncRNAs&differential expression analysis(DEA)among LSCC&normal sample categories,we discovered lncRNAs that were connected to m7G.The prognosis prediction model was built for the training category using univariate&multivariate COX regression&LASSO regression analyses,&the model’s efficacy was checked against the test category data.In addition,we conducted DEA of prognostic m7G-lncRNAs among LSCC&normal sample categories&compiled a list of co-expression networks&the structure of prognosis m7G-lncRNAs.To compare the prognoses for individuals with LSCC in the high-&low-risk categories in the prognosis prediction model,survival and risk assessments were also carried out.Finally,we created a nomogram to accurately forecast the outcomes of LSCC patients&created receiver operating characteristic(ROC)curves to assess the prognosis prediction model’s predictive capability.Results:Using co-expression network analysis&differential expression analysis,we discovered 774 m7G-lncRNAs and 551 DEm7G-lncRNAs,respectively.We then constructed a prognosis prediction model for six m7G-lncRNAs(FLG−AS1,RHOA−IT1,AC020913.3,AC027307.2,AC010973.2 and AC010789.1),identified 32 DEPm7G-lncRNAs,analyzed the correlation between 32 DEPm7G-lncRNAs and 13 DEPm7G-mRNAs,and performed survival analyses and risk analyses of the prognosis prediction model to assess the prognostic performance of LSCC patients.By displaying ROC curves and a nomogram,we finally checked the prognosis prediction model's accuracy.Conclusion:By creating novel predictive lncRNA signatures for clinical diagnosis&therapy,our findings will contribute to understanding the pathogenetic process of LSCC.展开更多
BACKGROUND Acute-on-chronic liver failure(ACLF)is the abrupt exacerbation of declined hepatic function in patients with chronic liver disease.AIM To explore the independent predictors of short-term prognosis in patien...BACKGROUND Acute-on-chronic liver failure(ACLF)is the abrupt exacerbation of declined hepatic function in patients with chronic liver disease.AIM To explore the independent predictors of short-term prognosis in patients with hepatitis B virus(HBV)-related ACLF and to establish a predictive short-term prognosis model for HBV-related ACLF.METHODS From January 2016 to December 2019,207 patients with HBV-related ACLF attending the 910^(th) Hospital of Chinese People's Liberation Army were continuously included in this retrospective study.Patients were stratified based on their survival status 3 mo after diagnosis.Information was collected regarding gender and age;coagulation function in terms of prothrombin time and international normalized ratio(INR);hematological profile in terms of neutrophil-tolymphocyte ratio(NLR)and platelet count(PLT);blood biochemistry in terms of alanine aminotransferase,aspartate aminotransferase,total bilirubin(Tbil),albumin,cholinesterase,blood urea nitrogen(BUN),creatinine,blood glucose,and sodium(Na);tumor markers including alpha-fetoprotein(AFP)and Golgi protein 73(GP73);virological indicators including HBV-DNA,HBsAg,HBeAg,Anti-HBe,and Anti-HBc;and complications including hepatic encephalopathy,hepatorenal syndrome,spontaneous peritonitis,gastrointestinal bleeding,and pulmonary infection.RESULTS There were 157 and 50 patients in the survival and death categories,respectively.Univariate analysis revealed significant differences in age,PLT,Tbil,BUN,NLR,HBsAg,AFP,GP73,INR,stage of liver failure,classification of liver failure,and incidence of complications(pulmonary infection,hepatic encephalopathy,spontaneous bacterial peritonitis,and upper gastrointestinal bleeding)between the two groups(P<0.05).GP73[hazard ratio(HR):1.009,95%confidence interval(CI):1.005-1.013,P=0.000],middle stage of liver failure(HR:5.056,95%CI:1.792-14.269,P=0.002),late stage of liver failure(HR:22.335,95%CI:8.544-58.388,P=0.000),pulmonary infection(HR:2.056,95%CI:1.145-3.690,P=0.016),hepatorenal syndrome(HR:6.847,95%CI:1.930-24.291,P=0.003),and HBsAg(HR:0.690,95%CI:0.524-0.908,P=0.008)were independent risk factors for short-term prognosis in patients with HBV-related ACLF.Following binary logistics regression analysis,we arrived at the following formula for predicting short-term prognosis:Logit(P)=Ln(P/1-P)=0.013×(GP73 ng/mL)+1.907×(middle stage of liver failure)+4.146×(late stage of liver failure)+0.734×(pulmonary infection)+22.320×(hepatorenal syndrome)-0.529×(HBsAg)-5.224.The predictive efficacy of the GP73-ACLF score was significantly better than that of the Model for End-Stage Liver Disease(MELD)and MELD-Na score models(P<0.05).CONCLUSION The stage of liver failure,presence of GP73,pulmonary infection,hepatorenal syndrome,and HBsAg are independent predictors of short-term prognosis in patients with HBV-related ACLF,and the GP73-ACLF model has good predictive value among these patients.展开更多
Objective:Lung adenocarcinoma exhibits diverse genetic and morphological backgrounds,in addition to considerable differences in clinical pathology and molecular biological characteristics.Among these,the phenomenon of...Objective:Lung adenocarcinoma exhibits diverse genetic and morphological backgrounds,in addition to considerable differences in clinical pathology and molecular biological characteristics.Among these,the phenomenon of spread through air space(STAS),a distinct mode of lung cancer infiltration,has rarely been reported.Therefore,this study aimed to explore the relationship between STAS tumor cells and the clinical and molecular characteristics of patients with lung adenocarcinoma,as well as their impact on prognosis.Methods:This study included 147 patients who were diagnosed with lung adenocarcinoma at the Inner Mongolia Autonomous Region Cancer Institute between January 2014 and December 2017.Surgical resection specimens were retrospectively analyzed.Using univariate and multivariate Cox analyses,we assessed the association between STAS and the clinicopathological features and molecular characteristics of patients with lung adenocarcinoma.Furthermore,we investigated the effects on patient prognosis.In addition,we developed a column–line plot prediction model and performed internal validation.Results:Patients with positive STAS had a significantly higher proportion of tumors with a diameter≥2 cm,with infiltration around the pleura,blood vessels,and nerves,and a pathological stage>IIB than in STAS-negative patients(P<0.05).Cox multivariate survival analysis revealed that clinical stage,STAS status,tumor size,and visceral pleural invasion were independent prognostic factors influencing the 5-year progression-free survival in patients with lung adenocarcinoma.The predictive values and P values from the Hosmer-Lemeshow test were 0.8 and 0.2,respectively,indicating no statistical difference.Receiver operating characteristic curve analysis demonstrated areas under the curve of 0.884 and 0.872 for the training and validation groups,respectively.The nomogram model exhibited the best fit with a value of 192.09.Conclusions:Clinical stage,pleural invasion,vascular invasion,peripheral nerve invasion,tumor size,and necrosis are independent prognostic factors for patients with STAS-positive lung adenocarcinoma.The nomogrambased on the clinical stage,pleural invasion,vascular invasion,peripheral nerve invasion,tumor size,and necrosis showed good accuracy,differentiation,and clinical practicality.展开更多
Background The global spread of coronavirus disease 2019(COVID-19)continues to threaten human health security,exerting considerable pressure on healthcare systems worldwide.While prognostic models for COVID-19 hospita...Background The global spread of coronavirus disease 2019(COVID-19)continues to threaten human health security,exerting considerable pressure on healthcare systems worldwide.While prognostic models for COVID-19 hospitalized or intensive care patients are currently available,prognostic models developed for large cohorts of thousands of individuals are still lacking.Methods Between February 4 and April 16,2020,we enrolled 3,974 patients admitted with COVID-19 disease in the Wuhan Huo-Shen-Shan Hospital and the Maternal and Child Hospital,Hubei Province,China.(1)Screening of key prognostic factors:A univariate Cox regression analysis was performed on 2,649 patients in the training set,and factors affecting prognosis were initially screened.Subsequently,a random survival forest model was established through machine analysis to further screen for factors that are important for prognosis.Finally,multivariate Cox regression analysis was used to determine the synergy among various factors related to prognosis.(2)Establishment of a scoring system:The nomogram algorithm established a COVID-19 patient death risk assessment scoring system for the nine selected key prognostic factors,calculated the C index,drew calibration curves and drew training set patient survival curves.(3)Verification of the scoring system:The scoring system assessed 1,325 patients in the test set,splitting them into high-and low-risk groups,calculated the C-index,and drew calibration and survival curves.Results The cross-sectional study found that age,clinical classification,sex,pulmonary insufficiency,hypoproteinemia,and four other factors(underlying diseases:blood diseases,malignant tumor;complications:digestive tract bleeding,heart dysfunction)have important significance for the prognosis of the enrolled patients with COVID-19.Herein,we report the discovery of the effects of hypoproteinemia and hematological diseases on the prognosis of COVID-19.Meanwhile,the scoring system established here can effectively evaluate objective scores for the early prognoses of patients with COVID-19 and can divide them into high-and low-risk groups(using a scoring threshold of 117.77,a score below which is considered low risk).The efficacy of the system was better than that of clinical classification using the current COVID-19 guidelines(C indexes,0.95 vs.0.89).Conclusions Age,clinical typing,sex,pulmonary insufficiency,hypoproteinemia,and four other factors were important for COVID-19 survival.Compared with general statistical methods,this method can quickly and accurately screen out the relevant factors affecting prognosis,provide an order of importance,and establish a scoring system based on the nomogram model,which is of great clinical significance.展开更多
To evaluate the influence of various clinicopathologic factors on the survival of patients with bile duct carcinoma after curative resection Methods A retrospective analysis was performed on 86 cases of bile duct ca...To evaluate the influence of various clinicopathologic factors on the survival of patients with bile duct carcinoma after curative resection Methods A retrospective analysis was performed on 86 cases of bile duct carcinoma treated from January 1981 to September 1995 Fifteen clinicopathologic factors that could possibly influence survival were selected A multivariate analysis of these individuals was performed using the Cox Proportional Hazards Model Results The overall cumulative survival rate was 73% for 1 year, 32% for 3 years and 19% for 5 years The results of univariate analysis showed that the major significant prognostic factors for influencing survival of these patients were type of histological lesion, lymph node metastasis, pancreatic invasion, duodenal invasion, perineural invasion, macroscopic vessel involvement, resected surgical margin and depth of cancer invasion ( P 【0 05) Pancreatic invasion, perineural invasion and lymph node metastases were the three most important prognostic factors determined by multivariate analysis using the Cox Proportional Hazards Model Conclusion Pancreatic invasion, perineural invasion and lymph node metastases are the most important prognostic factors for bile duct carcinoma after curative resection展开更多
Current subtyping methods of diffuse large B-cell lymphoma(DLBCL)could not satisfy the clinical demands for risk assessment and prognostic prediction.We aimed to investigate the prognostic effect of autophagy-related ...Current subtyping methods of diffuse large B-cell lymphoma(DLBCL)could not satisfy the clinical demands for risk assessment and prognostic prediction.We aimed to investigate the prognostic effect of autophagy-related genes(ARGs)in DLBCL.Transcriptomic data of 1,409 DLBCL patients,531 healthy controls(HCs),and single-cell sequencing data of 4 DLBCL were included.Validation involved spatial transcriptomics from 10 DLBCL patients and 110 DLBCL proteomic data from a local cohort.We identified 153 differentially expressed ARGs between DLBCL patients(n=48)and HCs(n=531),classifying 414 DLBCL patients into two subtypes based on autophagy heterogeneity.Subtype I,characterized by upregulated T regulatory(Treg)cells(P<0.0001)and T follicular helper(Tfh)cells(P=0.0012),showed a superior prognosis(P=0.035).Eight prognostic ARGs were selected to construct an autophagy-related model,dividing patients into low-and high-risk groups.Kaplan-Meier survival analysis revealed significantly better outcomes for the low-risk group in both the discovery(P<0.0001)and validation cohorts(P=0.0041).High-risk patients exhibited elevated IDO1(P=0.042)and LAG3(P<0.001)levels.Among the eight signature proteins,higher FAS was further verified to indicate a better prognosis in the local cohort(n=110)using antibody array(P=0.0083).FAS was primarily expressed in T cells such as Treg and Tfh cells and was elevated in non-progressive disease patients.FASpositive T cells showed increased interferon-gamma(normalized enrichment score(NES)=2.196,FDR<0.0001)and alpha(NES=1.836,FDR<0.01)response activities.We constructed an autophagy-related model and identified FAS as a prognostic biomarker.FAS+Treg and Tfh cell-enriched TME indicated a favorable prognosis.展开更多
Purpose:Early mortality in major trauma has decreased,but MODS remains a leading cause of poor outcomes,driven by trauma-induced cytokine storms that exacerbate injuries and organ damage.Methods:This prospective cohor...Purpose:Early mortality in major trauma has decreased,but MODS remains a leading cause of poor outcomes,driven by trauma-induced cytokine storms that exacerbate injuries and organ damage.Methods:This prospective cohort study included 79 major trauma patients(ISS>15)treated in the National Center for Trauma Medicine,Peking University People''s Hospital,from September 1,2021,to July 31,2023.Patients(1)with ISS>15(according to AIS 2015),(2)aged 15-80 years,(3)admitted within 6 h of injury,(4)having no prior treatment before admission,were included.Exclusion criteria were(1)GCS score<9 or AIS score≥3 for TBI,(2)confirmed infection,infectious disease,or high infection risk,(3)pregnancy,(4)severe primary diseases affecting survival,(5)recent use of immunosuppressive or cytotoxic drugs within the past 6 months,(6)psychiatric patients,(7)participation in other clinical trials within the past 30 days,(8)patients with incomplete data or missing blood samples.Admission serum inflammatory cytokines and pathophysiological data were analyzed to develop machine learning models predicting MODS within 7 days.LR,DR,RF,SVM,NB,and XGBoost were evaluated based on the area under the AUROC.The SHAP method was used to interpret results.Results:This study enrolled 79 patients with major trauma,and the median(Q1,Q3)age was 51(35,59)years(52 males,65.8%).The inflammatory cytokine data were collected for all participants.Among these patients,35(44.3%)developed MODS,and 44(55.7%)did not.Additionally,2 patients(2.5%)from the MODS group succumbed.The logistic regression model showed strong performance in predicting MODS.Ten key cytokines,IL-18,Eotaxin,MCP-4,IP-10,CXCL12,MIP-3α,MCP-1,IL-1RA,Cystatin C,and MRP8/14 were identified as critical to the trauma-induced cytokine storm and MODS development.Early elevation of these cytokines achieved high predictive accuracy,with an AUROC of 0.887(95%CI 0.813-0.976).Conclusion:Trauma-induced cytokine storms are strongly associated with MODS.Early identification of inflammatory cytokine changes enables better prediction and timely interventions to improve outcomes.展开更多
Background:The impact of donor-recipient sex-matching patterns on the outcomes of liver transplantation(LT)remains controversial.We aim to compare the long-term graft and recipient survival of LT among four donor-reci...Background:The impact of donor-recipient sex-matching patterns on the outcomes of liver transplantation(LT)remains controversial.We aim to compare the long-term graft and recipient survival of LT among four donor-recipient sex-matching patterns using United Network of Organ Sharing(UNOS)database,and develop a grading system for prognostic stratification.Methods:Adult primary LT recipients from February,2002 to June,2022 in the UNOS database were divided into four donor-recipient sex groups,including female donor-female recipient(F-F)group,female donor-male recipient(F-M)group,male donor-female recipient(M-F)group and male donor-male recipient(M-M)group.Propensity score matching(PSM)analysis,inverse probability treatment weighting and multivariable adjustment were used to minimize between-group imbalances.Univariate and multivariate Cox regression analyses were performed to determine risk factors.Results:A total of 107,183 recipients were included,consisting of 46,037(43.0%)recipients in M-M group,25,699(24.0%)in F-M group,18,340(17.1%)in M-F group and 17,107(15.9%)in F-F group.After PSM,there were significant differences in the graft survival(GS)and overall survival(OS)among four groups(both P<0.001).F-F group showed best prognosis,with 1-,5-and 10-year GS of 89.1%,76.3%and 63.1%,respectively,while F-M group showed poor prognosis,with 1-,5-and 10-year GS of 87.4%,73.6%and 58.4%,respectively.In F-M group,three subgroups with distinctive prognosis were precisely stratified based on a grading system incorporating six independent risk factors.Conclusions:Donor-recipient sex-matching patterns significantly influence long-term LT outcomes.The grading system provides potential value to amend allocation process and optimize organ utility.展开更多
Predicting the progression from Mild Cognitive Impairment(MCI)to Alzheimer's Disease(AD)is a critical challenge for enabling early intervention and improving patient outcomes.While longitudinal multi-modal neuroim...Predicting the progression from Mild Cognitive Impairment(MCI)to Alzheimer's Disease(AD)is a critical challenge for enabling early intervention and improving patient outcomes.While longitudinal multi-modal neuroimaging data holds immense potential for capturing the spatio-temporal dynamics of disease progression,its effective analysis is hampered by significant challenges:temporal heterogeneity(irregularly sampled scans),multi-modal misalignment,and the propensity of deep learning models to learn spurious,noncausal correlations.We propose CASCADE-Net,a novel end-to-end pipeline for robust and interpretable MCI-to-AD progression prediction.Our architecture introduces a Dynamic Temporal Alignment Module that employs a Neural Ordinary Differential Equation(Neural ODE)to model the continuous,underlying progression of pathology from irregularly sampled scans,effectively mapping heterogeneous patient data to a unified latent timeline.This aligned,noise-reduced spatio-temporal data is then processed by a predictive model featuring a novel Causal Spatial Attention mechanism.This mechanism not only identifies the critical brain regions and their evolution predictive of conversion but also incorporates a counterfactual constraint during training.This constraint ensures the learned features are causally linked to AD pathology by encouraging invariance to non-causal,confounder-based changes.Extensive experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that CASCADE-Net significantly outperforms state-of-the-art sequential models in prognostic accuracy.Furthermore,our model provides highly interpretable,causally-grounded attention maps,offering valuable insights into the disease progression process and fostering greater clinical trust.展开更多
文摘BACKGROUND Over the past few years,research into the pathogenesis of colon cancer has progressed rapidly,and cuproptosis is an emerging mode of cellular apoptosis.Exploring the relationship between colon cancer and cuproptosis benefits in identifying novel biomarkers and even improving the outcome of the disease.AIM To look at the prognostic relationship between colon cancer and the genes associated with cuproptosis and the immune system in patients.The main purpose was to assess whether reasonable induction of these biomarkers reduces mortality among patients with colon cancers.METHOD Data obtained from The Cancer Genome Atlas and Gene Expression Omnibus and the Genotype-Tissue Expression were used in differential analysis to explore differential expression genes associated with cuproptosis and immune activation.The least absolute shrinkage and selection operator and Cox regression algorithm was applied to build a cuproptosis-and immune-related combination model,and the model was utilized for principal component analysis and survival analysis to observe the survival and prognosis of the patients.A series of statistically meaningful transcriptional analysis results demonstrated an intrinsic relationship between cuproptosis and the micro-environment of colon cancer.RESULTS Once prognostic characteristics were obtained,the CDKN2A and DLAT genes related to cuproptosis were strongly linked to colon cancer:The first was a risk factor,whereas the second was a protective factor.The finding of the validation analysis showed that the comprehensive model associated with cuproptosis and immunity was statistically significant.Within the component expressions,the expressions of HSPA1A,CDKN2A,and UCN3 differed markedly.Transcription analysis primarily reflects the differential activation of related immune cells and pathways.Furthermore,genes linked to immune checkpoint inhibitors were expressed differently between the subgroups,which may reveal the mechanism of worse prognosis and the different sensitivities of chemotherapy.CONCLUSION The prognosis of the high-risk group evaluated in the combined model was poorer,and cuproptosis was highly correlated with the prognosis of colon cancer.It is possible that we may be able to improve patients’prognosis by regulating the gene expression to intervene the risk score.
基金supported by a grant Hebei Provincial Health Commission project from the Foundation of Basic Research(No.20191843).
文摘Objective:Through integrated bioinformatics analysis,the goal of this work was to find new,characterised N7-methylguanosine modification-related long non-coding RNAs(m7G-lncRNAs)that might be used to predict the prognosis of laryngeal squamous cell carcinoma(LSCC).Methods:The clinical data and LSCC gene expression data for the current investigation were initially retrieved from the TCGA database&sanitised.Then,using co-expression analysis of m7G-associated mRNAs&lncRNAs&differential expression analysis(DEA)among LSCC&normal sample categories,we discovered lncRNAs that were connected to m7G.The prognosis prediction model was built for the training category using univariate&multivariate COX regression&LASSO regression analyses,&the model’s efficacy was checked against the test category data.In addition,we conducted DEA of prognostic m7G-lncRNAs among LSCC&normal sample categories&compiled a list of co-expression networks&the structure of prognosis m7G-lncRNAs.To compare the prognoses for individuals with LSCC in the high-&low-risk categories in the prognosis prediction model,survival and risk assessments were also carried out.Finally,we created a nomogram to accurately forecast the outcomes of LSCC patients&created receiver operating characteristic(ROC)curves to assess the prognosis prediction model’s predictive capability.Results:Using co-expression network analysis&differential expression analysis,we discovered 774 m7G-lncRNAs and 551 DEm7G-lncRNAs,respectively.We then constructed a prognosis prediction model for six m7G-lncRNAs(FLG−AS1,RHOA−IT1,AC020913.3,AC027307.2,AC010973.2 and AC010789.1),identified 32 DEPm7G-lncRNAs,analyzed the correlation between 32 DEPm7G-lncRNAs and 13 DEPm7G-mRNAs,and performed survival analyses and risk analyses of the prognosis prediction model to assess the prognostic performance of LSCC patients.By displaying ROC curves and a nomogram,we finally checked the prognosis prediction model's accuracy.Conclusion:By creating novel predictive lncRNA signatures for clinical diagnosis&therapy,our findings will contribute to understanding the pathogenetic process of LSCC.
基金Supported by Science and Technology Project of Quanzhou to Dr.Zhengju Xu,No.2017Z018。
文摘BACKGROUND Acute-on-chronic liver failure(ACLF)is the abrupt exacerbation of declined hepatic function in patients with chronic liver disease.AIM To explore the independent predictors of short-term prognosis in patients with hepatitis B virus(HBV)-related ACLF and to establish a predictive short-term prognosis model for HBV-related ACLF.METHODS From January 2016 to December 2019,207 patients with HBV-related ACLF attending the 910^(th) Hospital of Chinese People's Liberation Army were continuously included in this retrospective study.Patients were stratified based on their survival status 3 mo after diagnosis.Information was collected regarding gender and age;coagulation function in terms of prothrombin time and international normalized ratio(INR);hematological profile in terms of neutrophil-tolymphocyte ratio(NLR)and platelet count(PLT);blood biochemistry in terms of alanine aminotransferase,aspartate aminotransferase,total bilirubin(Tbil),albumin,cholinesterase,blood urea nitrogen(BUN),creatinine,blood glucose,and sodium(Na);tumor markers including alpha-fetoprotein(AFP)and Golgi protein 73(GP73);virological indicators including HBV-DNA,HBsAg,HBeAg,Anti-HBe,and Anti-HBc;and complications including hepatic encephalopathy,hepatorenal syndrome,spontaneous peritonitis,gastrointestinal bleeding,and pulmonary infection.RESULTS There were 157 and 50 patients in the survival and death categories,respectively.Univariate analysis revealed significant differences in age,PLT,Tbil,BUN,NLR,HBsAg,AFP,GP73,INR,stage of liver failure,classification of liver failure,and incidence of complications(pulmonary infection,hepatic encephalopathy,spontaneous bacterial peritonitis,and upper gastrointestinal bleeding)between the two groups(P<0.05).GP73[hazard ratio(HR):1.009,95%confidence interval(CI):1.005-1.013,P=0.000],middle stage of liver failure(HR:5.056,95%CI:1.792-14.269,P=0.002),late stage of liver failure(HR:22.335,95%CI:8.544-58.388,P=0.000),pulmonary infection(HR:2.056,95%CI:1.145-3.690,P=0.016),hepatorenal syndrome(HR:6.847,95%CI:1.930-24.291,P=0.003),and HBsAg(HR:0.690,95%CI:0.524-0.908,P=0.008)were independent risk factors for short-term prognosis in patients with HBV-related ACLF.Following binary logistics regression analysis,we arrived at the following formula for predicting short-term prognosis:Logit(P)=Ln(P/1-P)=0.013×(GP73 ng/mL)+1.907×(middle stage of liver failure)+4.146×(late stage of liver failure)+0.734×(pulmonary infection)+22.320×(hepatorenal syndrome)-0.529×(HBsAg)-5.224.The predictive efficacy of the GP73-ACLF score was significantly better than that of the Model for End-Stage Liver Disease(MELD)and MELD-Na score models(P<0.05).CONCLUSION The stage of liver failure,presence of GP73,pulmonary infection,hepatorenal syndrome,and HBsAg are independent predictors of short-term prognosis in patients with HBV-related ACLF,and the GP73-ACLF model has good predictive value among these patients.
基金Funded by the Health Science and Technology Program of Inner Mongolia Autonomous Region(no.202201061)Supported by the Joint Project of theMillion Science and Technology Initiatives of Inner Mongolia Medical University(no.YKD2020KJBW(LH)057).
文摘Objective:Lung adenocarcinoma exhibits diverse genetic and morphological backgrounds,in addition to considerable differences in clinical pathology and molecular biological characteristics.Among these,the phenomenon of spread through air space(STAS),a distinct mode of lung cancer infiltration,has rarely been reported.Therefore,this study aimed to explore the relationship between STAS tumor cells and the clinical and molecular characteristics of patients with lung adenocarcinoma,as well as their impact on prognosis.Methods:This study included 147 patients who were diagnosed with lung adenocarcinoma at the Inner Mongolia Autonomous Region Cancer Institute between January 2014 and December 2017.Surgical resection specimens were retrospectively analyzed.Using univariate and multivariate Cox analyses,we assessed the association between STAS and the clinicopathological features and molecular characteristics of patients with lung adenocarcinoma.Furthermore,we investigated the effects on patient prognosis.In addition,we developed a column–line plot prediction model and performed internal validation.Results:Patients with positive STAS had a significantly higher proportion of tumors with a diameter≥2 cm,with infiltration around the pleura,blood vessels,and nerves,and a pathological stage>IIB than in STAS-negative patients(P<0.05).Cox multivariate survival analysis revealed that clinical stage,STAS status,tumor size,and visceral pleural invasion were independent prognostic factors influencing the 5-year progression-free survival in patients with lung adenocarcinoma.The predictive values and P values from the Hosmer-Lemeshow test were 0.8 and 0.2,respectively,indicating no statistical difference.Receiver operating characteristic curve analysis demonstrated areas under the curve of 0.884 and 0.872 for the training and validation groups,respectively.The nomogram model exhibited the best fit with a value of 192.09.Conclusions:Clinical stage,pleural invasion,vascular invasion,peripheral nerve invasion,tumor size,and necrosis are independent prognostic factors for patients with STAS-positive lung adenocarcinoma.The nomogrambased on the clinical stage,pleural invasion,vascular invasion,peripheral nerve invasion,tumor size,and necrosis showed good accuracy,differentiation,and clinical practicality.
基金supported by National Key Research and Development Program of China(2020YFC2002706).
文摘Background The global spread of coronavirus disease 2019(COVID-19)continues to threaten human health security,exerting considerable pressure on healthcare systems worldwide.While prognostic models for COVID-19 hospitalized or intensive care patients are currently available,prognostic models developed for large cohorts of thousands of individuals are still lacking.Methods Between February 4 and April 16,2020,we enrolled 3,974 patients admitted with COVID-19 disease in the Wuhan Huo-Shen-Shan Hospital and the Maternal and Child Hospital,Hubei Province,China.(1)Screening of key prognostic factors:A univariate Cox regression analysis was performed on 2,649 patients in the training set,and factors affecting prognosis were initially screened.Subsequently,a random survival forest model was established through machine analysis to further screen for factors that are important for prognosis.Finally,multivariate Cox regression analysis was used to determine the synergy among various factors related to prognosis.(2)Establishment of a scoring system:The nomogram algorithm established a COVID-19 patient death risk assessment scoring system for the nine selected key prognostic factors,calculated the C index,drew calibration curves and drew training set patient survival curves.(3)Verification of the scoring system:The scoring system assessed 1,325 patients in the test set,splitting them into high-and low-risk groups,calculated the C-index,and drew calibration and survival curves.Results The cross-sectional study found that age,clinical classification,sex,pulmonary insufficiency,hypoproteinemia,and four other factors(underlying diseases:blood diseases,malignant tumor;complications:digestive tract bleeding,heart dysfunction)have important significance for the prognosis of the enrolled patients with COVID-19.Herein,we report the discovery of the effects of hypoproteinemia and hematological diseases on the prognosis of COVID-19.Meanwhile,the scoring system established here can effectively evaluate objective scores for the early prognoses of patients with COVID-19 and can divide them into high-and low-risk groups(using a scoring threshold of 117.77,a score below which is considered low risk).The efficacy of the system was better than that of clinical classification using the current COVID-19 guidelines(C indexes,0.95 vs.0.89).Conclusions Age,clinical typing,sex,pulmonary insufficiency,hypoproteinemia,and four other factors were important for COVID-19 survival.Compared with general statistical methods,this method can quickly and accurately screen out the relevant factors affecting prognosis,provide an order of importance,and establish a scoring system based on the nomogram model,which is of great clinical significance.
文摘To evaluate the influence of various clinicopathologic factors on the survival of patients with bile duct carcinoma after curative resection Methods A retrospective analysis was performed on 86 cases of bile duct carcinoma treated from January 1981 to September 1995 Fifteen clinicopathologic factors that could possibly influence survival were selected A multivariate analysis of these individuals was performed using the Cox Proportional Hazards Model Results The overall cumulative survival rate was 73% for 1 year, 32% for 3 years and 19% for 5 years The results of univariate analysis showed that the major significant prognostic factors for influencing survival of these patients were type of histological lesion, lymph node metastasis, pancreatic invasion, duodenal invasion, perineural invasion, macroscopic vessel involvement, resected surgical margin and depth of cancer invasion ( P 【0 05) Pancreatic invasion, perineural invasion and lymph node metastases were the three most important prognostic factors determined by multivariate analysis using the Cox Proportional Hazards Model Conclusion Pancreatic invasion, perineural invasion and lymph node metastases are the most important prognostic factors for bile duct carcinoma after curative resection
基金supported by Chinese Academy of Medical Sciences(CAMS)Innovation Fund for Medical Sciences(CIFMS 2021-12M-1003)National High Level Hospital Clinical Research Funding(2022-PUMCH-D-002)China National Major Project for New Drug Innovation(2019ZX09201-002)。
文摘Current subtyping methods of diffuse large B-cell lymphoma(DLBCL)could not satisfy the clinical demands for risk assessment and prognostic prediction.We aimed to investigate the prognostic effect of autophagy-related genes(ARGs)in DLBCL.Transcriptomic data of 1,409 DLBCL patients,531 healthy controls(HCs),and single-cell sequencing data of 4 DLBCL were included.Validation involved spatial transcriptomics from 10 DLBCL patients and 110 DLBCL proteomic data from a local cohort.We identified 153 differentially expressed ARGs between DLBCL patients(n=48)and HCs(n=531),classifying 414 DLBCL patients into two subtypes based on autophagy heterogeneity.Subtype I,characterized by upregulated T regulatory(Treg)cells(P<0.0001)and T follicular helper(Tfh)cells(P=0.0012),showed a superior prognosis(P=0.035).Eight prognostic ARGs were selected to construct an autophagy-related model,dividing patients into low-and high-risk groups.Kaplan-Meier survival analysis revealed significantly better outcomes for the low-risk group in both the discovery(P<0.0001)and validation cohorts(P=0.0041).High-risk patients exhibited elevated IDO1(P=0.042)and LAG3(P<0.001)levels.Among the eight signature proteins,higher FAS was further verified to indicate a better prognosis in the local cohort(n=110)using antibody array(P=0.0083).FAS was primarily expressed in T cells such as Treg and Tfh cells and was elevated in non-progressive disease patients.FASpositive T cells showed increased interferon-gamma(normalized enrichment score(NES)=2.196,FDR<0.0001)and alpha(NES=1.836,FDR<0.01)response activities.We constructed an autophagy-related model and identified FAS as a prognostic biomarker.FAS+Treg and Tfh cell-enriched TME indicated a favorable prognosis.
文摘Purpose:Early mortality in major trauma has decreased,but MODS remains a leading cause of poor outcomes,driven by trauma-induced cytokine storms that exacerbate injuries and organ damage.Methods:This prospective cohort study included 79 major trauma patients(ISS>15)treated in the National Center for Trauma Medicine,Peking University People''s Hospital,from September 1,2021,to July 31,2023.Patients(1)with ISS>15(according to AIS 2015),(2)aged 15-80 years,(3)admitted within 6 h of injury,(4)having no prior treatment before admission,were included.Exclusion criteria were(1)GCS score<9 or AIS score≥3 for TBI,(2)confirmed infection,infectious disease,or high infection risk,(3)pregnancy,(4)severe primary diseases affecting survival,(5)recent use of immunosuppressive or cytotoxic drugs within the past 6 months,(6)psychiatric patients,(7)participation in other clinical trials within the past 30 days,(8)patients with incomplete data or missing blood samples.Admission serum inflammatory cytokines and pathophysiological data were analyzed to develop machine learning models predicting MODS within 7 days.LR,DR,RF,SVM,NB,and XGBoost were evaluated based on the area under the AUROC.The SHAP method was used to interpret results.Results:This study enrolled 79 patients with major trauma,and the median(Q1,Q3)age was 51(35,59)years(52 males,65.8%).The inflammatory cytokine data were collected for all participants.Among these patients,35(44.3%)developed MODS,and 44(55.7%)did not.Additionally,2 patients(2.5%)from the MODS group succumbed.The logistic regression model showed strong performance in predicting MODS.Ten key cytokines,IL-18,Eotaxin,MCP-4,IP-10,CXCL12,MIP-3α,MCP-1,IL-1RA,Cystatin C,and MRP8/14 were identified as critical to the trauma-induced cytokine storm and MODS development.Early elevation of these cytokines achieved high predictive accuracy,with an AUROC of 0.887(95%CI 0.813-0.976).Conclusion:Trauma-induced cytokine storms are strongly associated with MODS.Early identification of inflammatory cytokine changes enables better prediction and timely interventions to improve outcomes.
基金supported by The National Key Research and Development Program of China(No.2021YFA1100500)Key Research&Development Program of Zhejiang Province(No.2022C03108)+1 种基金Leading Innovation Team of Hangzhou Medical College:Clinical Research in Organ Transplantation(No.CXLJ202401)The Major Research Plan of the National Natural Science Foundation(No.92159202).
文摘Background:The impact of donor-recipient sex-matching patterns on the outcomes of liver transplantation(LT)remains controversial.We aim to compare the long-term graft and recipient survival of LT among four donor-recipient sex-matching patterns using United Network of Organ Sharing(UNOS)database,and develop a grading system for prognostic stratification.Methods:Adult primary LT recipients from February,2002 to June,2022 in the UNOS database were divided into four donor-recipient sex groups,including female donor-female recipient(F-F)group,female donor-male recipient(F-M)group,male donor-female recipient(M-F)group and male donor-male recipient(M-M)group.Propensity score matching(PSM)analysis,inverse probability treatment weighting and multivariable adjustment were used to minimize between-group imbalances.Univariate and multivariate Cox regression analyses were performed to determine risk factors.Results:A total of 107,183 recipients were included,consisting of 46,037(43.0%)recipients in M-M group,25,699(24.0%)in F-M group,18,340(17.1%)in M-F group and 17,107(15.9%)in F-F group.After PSM,there were significant differences in the graft survival(GS)and overall survival(OS)among four groups(both P<0.001).F-F group showed best prognosis,with 1-,5-and 10-year GS of 89.1%,76.3%and 63.1%,respectively,while F-M group showed poor prognosis,with 1-,5-and 10-year GS of 87.4%,73.6%and 58.4%,respectively.In F-M group,three subgroups with distinctive prognosis were precisely stratified based on a grading system incorporating six independent risk factors.Conclusions:Donor-recipient sex-matching patterns significantly influence long-term LT outcomes.The grading system provides potential value to amend allocation process and optimize organ utility.
文摘Predicting the progression from Mild Cognitive Impairment(MCI)to Alzheimer's Disease(AD)is a critical challenge for enabling early intervention and improving patient outcomes.While longitudinal multi-modal neuroimaging data holds immense potential for capturing the spatio-temporal dynamics of disease progression,its effective analysis is hampered by significant challenges:temporal heterogeneity(irregularly sampled scans),multi-modal misalignment,and the propensity of deep learning models to learn spurious,noncausal correlations.We propose CASCADE-Net,a novel end-to-end pipeline for robust and interpretable MCI-to-AD progression prediction.Our architecture introduces a Dynamic Temporal Alignment Module that employs a Neural Ordinary Differential Equation(Neural ODE)to model the continuous,underlying progression of pathology from irregularly sampled scans,effectively mapping heterogeneous patient data to a unified latent timeline.This aligned,noise-reduced spatio-temporal data is then processed by a predictive model featuring a novel Causal Spatial Attention mechanism.This mechanism not only identifies the critical brain regions and their evolution predictive of conversion but also incorporates a counterfactual constraint during training.This constraint ensures the learned features are causally linked to AD pathology by encouraging invariance to non-causal,confounder-based changes.Extensive experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that CASCADE-Net significantly outperforms state-of-the-art sequential models in prognostic accuracy.Furthermore,our model provides highly interpretable,causally-grounded attention maps,offering valuable insights into the disease progression process and fostering greater clinical trust.
