Traditional psychiatric diagnosis relies on subjective symptom assessment,lacking objective biomarkers that hinder early detection and personalized treatment.Plasma proteins and polygenic risk score(PRS),as potential ...Traditional psychiatric diagnosis relies on subjective symptom assessment,lacking objective biomarkers that hinder early detection and personalized treatment.Plasma proteins and polygenic risk score(PRS),as potential predictive tools,hold promise for advancing early diagnosis of mental disorders.This study aims to evaluate the predictive potential of proteomic features and PRS in multiple mental illnesses(depression,schizophrenia,and post-traumatic stress disorder(PTSD)).Using participant data from the UK Biobank-Pharma Proteomics Project,we screen protein associations with mental disorders through least absolute shrinkage and selection operator(LASSO)analysis and construct a Cox regression risk prediction model by integrating the PRS.Additionally,we evaluate predictive performance using 6 machine learning methods and Kaplan-Meier survival curves.Our findings reveal distinct predictive patterns across dis-orders.For depression,integrating plasma proteins with PRS significantly improves prediction beyond the clinical model(C-index=0.6322).For schizophrenia,adding plasma proteins enhances predictive performance,whereas PRS provides no significant improvement.For PTSD,neither plasma proteins nor PRS add substantial predictive value beyond clinical variables.Risk stratification analysis demonstrat that all three mental disorders models can clearly distinguish high-risk from low-risk groups(depression:HR=2.34,P<0.001;schizophrenia:HR=5.47,P<0.001;PTSD:HR=3.02,P<0.001).Al-though it shows good performance in short-term prediction,its long-term prediction ability has decreased,and it needs to be further optimized in the future.This study underscores the differential utility of biomarkers across mental disorders and provides a rationale for disorder-specific predictive modeling in precision psychiatry.展开更多
Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,espe...Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,especially for first-time mothers.Computational childbirth plays an essential role in the prediction and prevention of these traumas,but fast personalization of the pelvis and floor muscles is challenging due to their anatomical complexity.This study introduces a novel shape-prediction-based personalization of the pelvis and floor muscles for perineal tearing management and childbirth simulation.300 subjects were selected from public Computed Tomography(CT)databases.The pelvic bone nmjmeshes were generated using a coarse-to-fine non-rigid mesh alignment procedure.The floor muscle meshes were personalized using the bone mesh deformation information.A feature-to-pelvic structure reconstruction pipeline was proposed,incorporating various strategies.Ten-fold cross-validation helped determine the optimal reconstruction strategy,regression method,and feature sizes.The mesh-to-mesh distance metric was employed for evaluating.The statistical shape relation-based strategy,coupled with multi-output ridge regression,was the optimal approach for pelvic structure reconstruction.With a feature set ranging from 3 to 38,the mean errors were 2.672 to 1.613 mm,and 3.237 to 1.415 mm in muscle attachment regions.The best-and worst-case predictions had errors of 1.227±0.959 mm and 2.900±2.309 mm,respectively.This study provides a novel approach to achieving fast personalized childbirth modeling and simulation for perineal tearing management.展开更多
This article proposes the maximum test for a sequence of quadratic form statistics about score test in logistic regression model which can be applied to genetic and medicine fields.Theoretical properties about the max...This article proposes the maximum test for a sequence of quadratic form statistics about score test in logistic regression model which can be applied to genetic and medicine fields.Theoretical properties about the maximum test are derived.Extensive simulation studies are conducted to testify powers robustness of the maximum test compared to other two existed test.We also apply the maximum test to a real dataset about multiple gene variables association analysis.展开更多
Average credit scores for people in the United States(US)differ from state to state.Some states have high,and some states have low average credit scores.Since lenders and employers use credit scores to make loan and e...Average credit scores for people in the United States(US)differ from state to state.Some states have high,and some states have low average credit scores.Since lenders and employers use credit scores to make loan and employment decisions,people living in states where average credit scores are high should experience the benefits of living where credit scores tend to allow more favorable loan and employment decisions.Although credit scores are the direct result of credit histories,credit histories may be impacted by demographic factors.If the demographic factors that impact credit histories are identified,ways to improve credit scores are likely to be discovered and available to people and state government policymakers.This study looks for demographic factors to indirectly explain the average credit scores for people living in each state of the US.The methodology includes statistical analyses and geographic information systems(GIS)mapping.Statistical analyses provide evidence to suggest that state average credit scores are explained by the demographic factors of education,family,income,and health.GIS mapping reveals clusters of states with similar demographics and credit scores.展开更多
The basic inference function of mathematical statistics, the score function, is a vector function. The author has introduced the scalar score, a scalar inference function, which reflects main features of a continuous ...The basic inference function of mathematical statistics, the score function, is a vector function. The author has introduced the scalar score, a scalar inference function, which reflects main features of a continuous probability distribution and which is simple. Its simplicity makes it possible to introduce new relevant numerical characteristics of continuous distributions. The t-mean and score variance are descriptions of distributions without the drawbacks of the mean and variance, which may not exist even in cases of regular distributions. Their sample counterparts appear to be alternative descriptions of the observed data. The scalar score itself appears to be a new mathematical tool, which could be used in solving traditional statistical problems for models far from the normal one, skewed and heavy-tailed.展开更多
BACKGROUND Emphysematous pyelonephritis(EPN)is a life-threatening necrotizing renal parenchyma infection characterized by gas formation due to severe bacterial infection,predominantly affecting diabetic and immunocomp...BACKGROUND Emphysematous pyelonephritis(EPN)is a life-threatening necrotizing renal parenchyma infection characterized by gas formation due to severe bacterial infection,predominantly affecting diabetic and immunocompromised patients.It carries high morbidity and mortality,requiring early diagnosis and timely intervention.Various prognostic scoring systems help in triaging critically ill patients.The National Early Warning Score 2(NEWS 2)scoring system is a widely used physiological assessment tool that evaluates clinical deterioration based on vital parameters,but its standard form lacks specificity for risk stratification in EPN,necessitating modifications to improve treatment decisionmaking and prognostic accuracy in this critical condition.AIM To highlight the need to modify the NEWS 2 score to enable more intense monitoring and better treatment outcomes.METHODS This prospective study was done on all EPN patients admitted to our hospital over the past 12 years.A weighted average risk-stratification index was calculated for each of the three groups,mortality risk was calculated for each of the NEWS 2 scores,and the need for intervention for each of the three groups was calculated.The NEWS 2 score was subsequently modified with 0-6,7-14 and 15-20 scores included in groups 1,2 and 3,respectively.RESULTS A total of 171 patients with EPN were included in the study,with a predominant association with diabetes(90.6%)and a female-to-male ratio of 1.5:1.The combined prognostic scoring of the three groups was 10.7,13.0,and 21.9,respectively(P<0.01).All patients managed conservatively belonged to group 1(P<0.01).Eight patients underwent early nephrectomy,with six from group 3(P<0.01).Overall mortality was 8(4.7%),with seven from group 3(87.5%).The cutoff NEWS 2 score for mortality was identified to be 15,with a sensitivity of 87.5%,specificity of 96.9%,and an overall accuracy rate of 96.5%.The area under the curve to predict mortality based on the NEWS 2 score was 0.98,with a confidence interval of(0.97,1.0)and P<0.001.CONCLUSION Modified NEWS 2(mNEWS 2)score dramatically aids in the appropriate assessment of treatment-related outcomes.MNEWS 2 scores should become the practice standard to reduce the morbidity and mortality associated with this dreaded illness.展开更多
BACKGROUND Post-hepatectomy liver failure(PHLF)after liver resection is one of the main complications causing postoperative death in patients with hepatocellular carcinoma(HCC).It is crucial to help clinicians identif...BACKGROUND Post-hepatectomy liver failure(PHLF)after liver resection is one of the main complications causing postoperative death in patients with hepatocellular carcinoma(HCC).It is crucial to help clinicians identify potential high-risk PHLF patients as early as possible through preoperative evaluation.AIM To identify risk factors for PHLF and develop a prediction model.METHODS This study included 248 patients with HCC at The Second Affiliated Hospital of Air Force Medical University between January 2014 and December 2023;these patients were divided into a training group(n=164)and a validation group(n=84)via random sampling.The independent variables for the occurrence of PHLF were identified by univariate and multivariate analyses and visualized as nomograms.Ultimately,comparisons were made with traditional models via receiver operating characteristic(ROC)curves,calibration curves,and decision curve analysis(DCA).RESULTS In this study,portal vein width[odds ratio(OR)=1.603,95%CI:1.288-1.994,P≤0.001],the preoperative neutrophil-to-lymphocyte ratio(NLR)(OR=1.495,95%CI:1.126-1.984,P=0.005),and the albumin-bilirubin(ALBI)score(OR=8.868,95%CI:2.144-36.678,P=0.003)were independent risk factors for PHLF.A nomogram prediction model was developed using these factors.ROC and DCA analyses revealed that the predictive efficacy and clinical value of this model were better than those of traditional models.CONCLUSION A new Nomogram model for predicting PHLF in HCC patients was successfully established based on portal vein width,the NLR,and the ALBI score,which outperforms the traditional model.展开更多
OBJECTIVE:To investigate the clinical efficacy of using a Jiedu formula(解毒方) as an adjunctive therapy in patients with hepatocellular carcinoma(HCC) after hepatectomy.METHODS:In total,354 patients were included in ...OBJECTIVE:To investigate the clinical efficacy of using a Jiedu formula(解毒方) as an adjunctive therapy in patients with hepatocellular carcinoma(HCC) after hepatectomy.METHODS:In total,354 patients were included in this study.All patients were categorized into the traditional herbal medicine(THM) group(n = 115) or the non-THM treatment(nTHM) group(n = 239),with the Jiedu formula administered twice a day to the patients in the THM group.The primary outcome was recurrence-free survival(RFS).Univariate and multivariate Cox regression analyses were performed to identify the prognostic factors associated with RFS.Then,the high risk of recurrence among patients was identified,and propensity score matching(PSM) and RFS analysis were performed to analyze the prognostic factors for the outcomes of patients at a high risk of recurrence in different groups.RESULTS:The one,two,three,and five-year RFS rates of the THM and nTHM groups were 76.4% vs 66.1%,65.5% vs 48.8%,57.9% vs 39.9%,and 43.9% vs 29.2%,respectively.The results of the Multivariate Cox analysis showed that giant tumors [hazard ratio(HR),1.54,P = 0.04],poor degree of differentiation,microsatellite,or microvascular invasion(HR,1.29,P = 0.09) increased the risk of recurrence.In the population with a high risk of recurrence,after PSM,the one,two,three,and five-year survival rates were 70.6% vs 68.0%,63.0% vs 43.1%,59.6% vs 33.3%,and 41.9% vs 26.4%,respectively.CONCLUSION:In this study,THM was found to be an effective agent for adjuvant therapy for HCC to prevent early recurrence of HCC after hepatic resection.展开更多
Objective We aimed to investigate the patterns of fasting blood glucose(FBG)trajectories and analyze the relationship between various occupational hazard factors and FBG trajectories in male steelworkers.Methods The s...Objective We aimed to investigate the patterns of fasting blood glucose(FBG)trajectories and analyze the relationship between various occupational hazard factors and FBG trajectories in male steelworkers.Methods The study cohort included 3,728 workers who met the selection criteria for the Tanggang Occupational Cohort(TGOC)between 2017 and 2022.A group-based trajectory model was used to identify the FBG trajectories.Environmental risk scores(ERS)were constructed using regression coefficients from the occupational hazard model as weights.Univariate and multivariate logistic regression analyses were performed to explore the effects of occupational hazard factors using the ERS on FBG trajectories.Results FBG trajectories were categorized into three groups.An association was observed between high temperature,noise exposure,and FBG trajectory(P<0.05).Using the first quartile group of ERS1 as a reference,the fourth quartile group of ERS1 had an increased risk of medium and high FBG by 1.90and 2.21 times,respectively(odds ratio[OR]=1.90,95%confidence interval[CI]:1.17–3.10;OR=2.21,95%CI:1.09–4.45).Conclusion An association was observed between occupational hazards based on ERS and FBG trajectories.The risk of FBG trajectory levels increase with an increase in ERS.展开更多
Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,su...Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.展开更多
Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit signifi...Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit significant biases and inter-model differences in simulating ENSO,underscoring the need for alternative modeling approaches.The Regional Ocean Modeling System(ROMS)is a sophisticated ocean model widely used for regional studies and has been coupled with various atmospheric models.However,its application in simulating ENSO processes on a basin scale in the tropical Pacific has not been explored.For the first time,this study presents the development of a basin-scale hybrid coupled model(HCM)for the tropical Pacific,integrating ROMS with a statistical atmospheric model that captures the interannual relationships between sea surface temperature(SST)and wind stress anomalies.The HCM is evaluated for its capability to simulate the annual mean,seasonal,and interannual variations of the oceanic state in the tropical Pacific.Results demonstrate that the model effectively reproduces the ENSO cycle,with a dominant oscillation period of approximately two years.The ROMS-based HCM developed here offers an efficient and robust tool for investigating climate variability in the tropical Pacific.展开更多
Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experime...Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experimental testing,digital core technology,and theoretical modelling.Two CRL types with contrasting mesostructures were characterized across three scales.Macroscopically,CRL-I and CRL-II exhibited mean compressive strengths of 8.46 and 5.17 MPa,respectively.Mesoscopically,CRL-I featured small-scale highly interconnected pores,whilst CRL-II developed larger stratified pores with diminished connectivity.Microscopically,both CRL matrices demonstrated remarkable similarity in mineral composition and mechanical properties.A novel voxel average-based digital core scaling methodology was developed to facilitate numerical simulation of cross-scale damage processes,revealing network-progressive failure in CRL-I versus directional-brittle failure in CRL-II.Furthermore,a damage statistical constitutive model based on digital core technology and mesoscopic homogenisation theory established quantitative relationships between microelement strength distribution and macroscopic mechanical behavior.These findings illuminate the fundamental mechanisms through which mesoscopic structure governs the macroscopic mechanical properties of CRL.展开更多
BACKGROUND Clinical predictors of dengue fever are crucial for guiding timely management and avoiding life-threatening complications.While prognostic scores are available,a systematic evaluation of these tools is lack...BACKGROUND Clinical predictors of dengue fever are crucial for guiding timely management and avoiding life-threatening complications.While prognostic scores are available,a systematic evaluation of these tools is lacking.AIM To evaluate the performance and accuracy of various proposed dengue clinical prognostic scores.METHODS Three databases,PubMed,EMBASE and Cochrane,were searched for peer-reviewed studies published from inception to 4 September 2023.Studies either developing or validating a prognostic model relevant to dengue fever were included.A total of 29 studies(n=17910)were included.RESULTS Most commonly studied outcomes were severe dengue(15 models)and mortality(8 models).For the paediatric population,Bedside Dengue Severity Score by Gayathri et al(specificity=0.98)and the nomogram model by Nguyen et al(sensitivity=0.87)performed better.For the adult population,the most specific model was reported by Leo et al(specificity=0.98).The most sensitive score is shared between Warning Signs for Severe Dengue as reported by Leo et al and Model 2 by Lee et al(sensitivity=1.00).CONCLUSION While several models demonstrated precision and reliability in predicting severe dengue and mortality,broader application across diverse geographic settings is needed to assess their external validity.展开更多
BACKGROUND Chronic liver disease is a growing global health problem,leading to hepatic decompensation characterized by an array of clinical and biochemical complic-ations.Several scoring systems have been introduced i...BACKGROUND Chronic liver disease is a growing global health problem,leading to hepatic decompensation characterized by an array of clinical and biochemical complic-ations.Several scoring systems have been introduced in assessing the severity of hepatic decompensation with the most frequent ones are Child-Pugh score,model of end-stage liver disease(MELD)score,and MELD-Na score.Anemia is frequently observed in cirrhotic patients and is linked to worsened clinical outcomes.Although studies have explored anemia in liver disease,few have investigated the correlation of hemoglobin level with the severity of hepatic decompensation.AIM To determine the relationship between hemoglobin levels and the severity of decompensated liver disease and comparing the strength of this correlation using the Child-Pugh,MELD,and MELD-Na scores.METHODS This cross-sectional study was conducted at a tertiary care hospital with 652 decompensated liver disease patients enrolled in the study.Data was collected on demographics,clinical history,and laboratory findings,including hemoglobin levels,bilirubin,albumin,prothrombin time(international normalized ratio),sodium,and creatinine.The Child-Pugh,MELD,and MELD-Na scores were calculated.Statistical analysis was performed using Statistical Package for the Social Sciences version 26,and correlations between hemoglobin levels and severity scores were assessed using Spearman's correlation coefficient.RESULTS The study included 405 males(62.1%)and 247 females(37.9%)with an average age of 58.8 years.Significant inverse correlations were found between hemoglobin levels and Child-Pugh,MELD,and MELD-Na scores(P<0.01),with the MELD scoring system being the strongest correlator among all.One-way analysis of variance revealed significant differences in hemoglobin levels across the severity groups of each scoring system(P=0.001).Tukey's post hoc analysis confirmed significant internal differences among each severity group.CONCLUSION Understanding the correlation between hemoglobin and liver disease severity can improve patient management by offering insights into prognosis and guiding treatment decisions.展开更多
Background Renal and liver dysfunction,which are common complications in infectious diseases,are associated with poor clinical outcomes.This study aimed to evaluate the prognostic value of the Model for End-Stage Live...Background Renal and liver dysfunction,which are common complications in infectious diseases,are associated with poor clinical outcomes.This study aimed to evaluate the prognostic value of the Model for End-Stage Liver Disease Excluding International Normalized Ratio(MELD-XI)score for predicting short-term mortality in patients with infective endocarditis(IE)complicated by sepsis.Methods A total of 496 consecutive IE patients complicated with sepsis at Guangdong Provincial People's Hospital were enrolled and divided into three groups according to the tertiles of MELD-XI score:<7.9(n=164),7.9-14.6(n=168),and>14.6(n=164).Major adverse clinical events(MACE)were composite endpoints that included acute heart failure,renal dialysis,stroke,and death during hospitalization.Multivariate analysis was used to explore the prognostic value of MELD-XI score.Results In-hospital and 6-month mortality were 14.3%and 21.5%,respectively.In-hospital mortality and the incidence of MACE rose significantly with higher MELD-XI scores(mortality:8.5%vs.12.5%vs.14.3%,P=0.002;Incidence of MACE:24.4%vs.31%vs.51.2%,P<0.001).Receiver operating characteristic(ROC)curve analysis showed that the optimal cutoff value of MELD-XI score was 15.7[area under the curve(AUC):0.648,95%CI:0.578-0.718,P<0.001].Multivariate regression analysis revealed that MELD-XI score>15.7 was a significantly independent risk factor for both in-hospital[adjusted odds ratio(OR):2.27,95%CI:1.28-4.05,P=0.005]and 6-month mortality[adjusted hazard ratio(HR):1.69,95%CI:1.13-2.53,P=0.011].Conclusions MELD-XI score>15.7 was independently associated with short-term mortality in IE patients complicated with sepsis,suggesting its potential value as a prognostic biomarker for risk stratification in this population.展开更多
Objective:Neuroblastoma is the most common extracranial solid tumor in children and has complex genetic underpinnings.Previous genome-wide association studies(GWASs)have identified many loci associated with neuroblast...Objective:Neuroblastoma is the most common extracranial solid tumor in children and has complex genetic underpinnings.Previous genome-wide association studies(GWASs)have identified many loci associated with neuroblastoma susceptibility;however,their application in risk prediction for Chinese children has not been systematically explored.This study seeks to enhance neuroblastoma risk prediction by validating these loci and evaluating their performance in polygenic risk models.Methods:We validated 35 GWAS-identified neuroblastoma susceptibility loci in a cohort of Chinese children,consisting of 402 neuroblastoma patients and 473 healthy controls.Genotyping these polymorphisms was conducted via the TaqMan method.Univariable and multivariable logistic regression analyses revealed the genetic loci significantly associated with neuroblastoma risk.We constructed polygenic risk models by combining these loci and assessed their predictive performance via area under the curve(AUC)analysis.We also established a polygenic risk scoring(PRS)model for risk prediction by adopting the PLINK method.Results:Fourteen loci,including ten protective polymorphisms from CASC15,BARD1,LMO1,HSD17B12,and HACE1,and four risk variants from BARD1,RSRC1,CPZ and MMP20 were significantly associated with neuroblastoma risk.Compared with single-gene model,the 8-gene model(AUC=0.72)and 13-gene model(AUC=0.73)demonstrated superior predictive performance.Additionally,a PRS incorporating six significant loci achieved an AUC of 0.66,effectively stratifying individuals into distinct risk categories regarding neuroblastoma susceptibility.A higher PRS was significantly associated with advanced International Neuroblastoma Staging System(INSS)stages,suggesting its potential for clinical risk stratification.Conclusions:Our findings validate multiple loci as neuroblastoma risk factors in Chinese children and demonstrate the utility of polygenic risk models,particularly the PRS,in improving risk prediction.These results suggest that integrating multiple genetic variants into a PRS can enhance neuroblastoma risk stratification and potentially improve early diagnosis by guiding targeted screening programs for high-risk children.展开更多
Background Biomarkers-based prediction of long-term risk of acute coronary syndrome(ACS)is scarce.We aim to develop a risk score integrating clinical routine information(C)and plasma biomarkers(B)for predicting long-t...Background Biomarkers-based prediction of long-term risk of acute coronary syndrome(ACS)is scarce.We aim to develop a risk score integrating clinical routine information(C)and plasma biomarkers(B)for predicting long-term risk of ACS patients.Methods We included 2729 ACS patients from the OCEA(Observation of cardiovascular events in ACS patients).The earlier admitted 1910 patients were enrolled as development cohort;and the subsequently admitted 819 subjects were treated as valida-tion cohort.We investigated 10-year risk of cardiovascular(CV)death,myocardial infarction(MI)and all cause death in these pa-tients.Potential variables contributing to risk of clinical events were assessed using Cox regression models and a score was de-rived using main part of these variables.Results During 16,110 person-years of follow-up,there were 238 CV death/MI in the development cohort.The 7 most import-ant predictors including in the final model were NT-proBNP,D-dimer,GDF-15,peripheral artery disease(PAD),Fibrinogen,ST-segment elevated MI(STEMI),left ventricular ejection fraction(LVEF),termed as CB-ACS score.C-index of the score for predica-tion of cardiovascular events was 0.79(95%CI:0.76-0.82)in development cohort and 0.77(95%CI:0.76-0.78)in the validation co-hort(5832 person-years of follow-up),which outperformed GRACE 2.0 and ABC-ACS risk score.The CB-ACS score was also well calibrated in development and validation cohort(Greenwood-Nam-D’Agostino:P=0.70 and P=0.07,respectively).Conclusions CB-ACS risk score provides a useful tool for long-term prediction of CV events in patients with ACS.This model outperforms GRACE 2.0 and ABC-ACS ischemic risk score.展开更多
基金The National Natural Science Foundation of China-Regional Science“Identification of novel drug targets for lung cancer via Mendelian randomization analysis based on blood proteomics”(62362062)The 2025 Xinjiang University Excellent Graduate Innovation Project“Research on identification of therapeutic targets and predictive factors for mental disorders based on proteomics”(XJDX2025YJS151)。
文摘Traditional psychiatric diagnosis relies on subjective symptom assessment,lacking objective biomarkers that hinder early detection and personalized treatment.Plasma proteins and polygenic risk score(PRS),as potential predictive tools,hold promise for advancing early diagnosis of mental disorders.This study aims to evaluate the predictive potential of proteomic features and PRS in multiple mental illnesses(depression,schizophrenia,and post-traumatic stress disorder(PTSD)).Using participant data from the UK Biobank-Pharma Proteomics Project,we screen protein associations with mental disorders through least absolute shrinkage and selection operator(LASSO)analysis and construct a Cox regression risk prediction model by integrating the PRS.Additionally,we evaluate predictive performance using 6 machine learning methods and Kaplan-Meier survival curves.Our findings reveal distinct predictive patterns across dis-orders.For depression,integrating plasma proteins with PRS significantly improves prediction beyond the clinical model(C-index=0.6322).For schizophrenia,adding plasma proteins enhances predictive performance,whereas PRS provides no significant improvement.For PTSD,neither plasma proteins nor PRS add substantial predictive value beyond clinical variables.Risk stratification analysis demonstrat that all three mental disorders models can clearly distinguish high-risk from low-risk groups(depression:HR=2.34,P<0.001;schizophrenia:HR=5.47,P<0.001;PTSD:HR=3.02,P<0.001).Al-though it shows good performance in short-term prediction,its long-term prediction ability has decreased,and it needs to be further optimized in the future.This study underscores the differential utility of biomarkers across mental disorders and provides a rationale for disorder-specific predictive modeling in precision psychiatry.
基金funded by Vietnam National University Ho Chi Minh City(VNU-HCM)under grant number DS.C2025-28-06.
文摘Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,especially for first-time mothers.Computational childbirth plays an essential role in the prediction and prevention of these traumas,but fast personalization of the pelvis and floor muscles is challenging due to their anatomical complexity.This study introduces a novel shape-prediction-based personalization of the pelvis and floor muscles for perineal tearing management and childbirth simulation.300 subjects were selected from public Computed Tomography(CT)databases.The pelvic bone nmjmeshes were generated using a coarse-to-fine non-rigid mesh alignment procedure.The floor muscle meshes were personalized using the bone mesh deformation information.A feature-to-pelvic structure reconstruction pipeline was proposed,incorporating various strategies.Ten-fold cross-validation helped determine the optimal reconstruction strategy,regression method,and feature sizes.The mesh-to-mesh distance metric was employed for evaluating.The statistical shape relation-based strategy,coupled with multi-output ridge regression,was the optimal approach for pelvic structure reconstruction.With a feature set ranging from 3 to 38,the mean errors were 2.672 to 1.613 mm,and 3.237 to 1.415 mm in muscle attachment regions.The best-and worst-case predictions had errors of 1.227±0.959 mm and 2.900±2.309 mm,respectively.This study provides a novel approach to achieving fast personalized childbirth modeling and simulation for perineal tearing management.
基金This work of Jiayan Zhu is partially supported by seeding project funding(2019ZZX026)scientific research project funding of talent recruitment,and start up funding for scientific research of Hubei University of Chinese MedicineThis work of Zhengbang Li is partially supported by self-determined research funds of Central China Normal University from colleges'basic research of MOE(CCNU18QN031).
文摘This article proposes the maximum test for a sequence of quadratic form statistics about score test in logistic regression model which can be applied to genetic and medicine fields.Theoretical properties about the maximum test are derived.Extensive simulation studies are conducted to testify powers robustness of the maximum test compared to other two existed test.We also apply the maximum test to a real dataset about multiple gene variables association analysis.
文摘Average credit scores for people in the United States(US)differ from state to state.Some states have high,and some states have low average credit scores.Since lenders and employers use credit scores to make loan and employment decisions,people living in states where average credit scores are high should experience the benefits of living where credit scores tend to allow more favorable loan and employment decisions.Although credit scores are the direct result of credit histories,credit histories may be impacted by demographic factors.If the demographic factors that impact credit histories are identified,ways to improve credit scores are likely to be discovered and available to people and state government policymakers.This study looks for demographic factors to indirectly explain the average credit scores for people living in each state of the US.The methodology includes statistical analyses and geographic information systems(GIS)mapping.Statistical analyses provide evidence to suggest that state average credit scores are explained by the demographic factors of education,family,income,and health.GIS mapping reveals clusters of states with similar demographics and credit scores.
文摘The basic inference function of mathematical statistics, the score function, is a vector function. The author has introduced the scalar score, a scalar inference function, which reflects main features of a continuous probability distribution and which is simple. Its simplicity makes it possible to introduce new relevant numerical characteristics of continuous distributions. The t-mean and score variance are descriptions of distributions without the drawbacks of the mean and variance, which may not exist even in cases of regular distributions. Their sample counterparts appear to be alternative descriptions of the observed data. The scalar score itself appears to be a new mathematical tool, which could be used in solving traditional statistical problems for models far from the normal one, skewed and heavy-tailed.
文摘BACKGROUND Emphysematous pyelonephritis(EPN)is a life-threatening necrotizing renal parenchyma infection characterized by gas formation due to severe bacterial infection,predominantly affecting diabetic and immunocompromised patients.It carries high morbidity and mortality,requiring early diagnosis and timely intervention.Various prognostic scoring systems help in triaging critically ill patients.The National Early Warning Score 2(NEWS 2)scoring system is a widely used physiological assessment tool that evaluates clinical deterioration based on vital parameters,but its standard form lacks specificity for risk stratification in EPN,necessitating modifications to improve treatment decisionmaking and prognostic accuracy in this critical condition.AIM To highlight the need to modify the NEWS 2 score to enable more intense monitoring and better treatment outcomes.METHODS This prospective study was done on all EPN patients admitted to our hospital over the past 12 years.A weighted average risk-stratification index was calculated for each of the three groups,mortality risk was calculated for each of the NEWS 2 scores,and the need for intervention for each of the three groups was calculated.The NEWS 2 score was subsequently modified with 0-6,7-14 and 15-20 scores included in groups 1,2 and 3,respectively.RESULTS A total of 171 patients with EPN were included in the study,with a predominant association with diabetes(90.6%)and a female-to-male ratio of 1.5:1.The combined prognostic scoring of the three groups was 10.7,13.0,and 21.9,respectively(P<0.01).All patients managed conservatively belonged to group 1(P<0.01).Eight patients underwent early nephrectomy,with six from group 3(P<0.01).Overall mortality was 8(4.7%),with seven from group 3(87.5%).The cutoff NEWS 2 score for mortality was identified to be 15,with a sensitivity of 87.5%,specificity of 96.9%,and an overall accuracy rate of 96.5%.The area under the curve to predict mortality based on the NEWS 2 score was 0.98,with a confidence interval of(0.97,1.0)and P<0.001.CONCLUSION Modified NEWS 2(mNEWS 2)score dramatically aids in the appropriate assessment of treatment-related outcomes.MNEWS 2 scores should become the practice standard to reduce the morbidity and mortality associated with this dreaded illness.
基金Supported by Shaanxi Provincial Social Development Fund,No.2024SF-YBXM-140.
文摘BACKGROUND Post-hepatectomy liver failure(PHLF)after liver resection is one of the main complications causing postoperative death in patients with hepatocellular carcinoma(HCC).It is crucial to help clinicians identify potential high-risk PHLF patients as early as possible through preoperative evaluation.AIM To identify risk factors for PHLF and develop a prediction model.METHODS This study included 248 patients with HCC at The Second Affiliated Hospital of Air Force Medical University between January 2014 and December 2023;these patients were divided into a training group(n=164)and a validation group(n=84)via random sampling.The independent variables for the occurrence of PHLF were identified by univariate and multivariate analyses and visualized as nomograms.Ultimately,comparisons were made with traditional models via receiver operating characteristic(ROC)curves,calibration curves,and decision curve analysis(DCA).RESULTS In this study,portal vein width[odds ratio(OR)=1.603,95%CI:1.288-1.994,P≤0.001],the preoperative neutrophil-to-lymphocyte ratio(NLR)(OR=1.495,95%CI:1.126-1.984,P=0.005),and the albumin-bilirubin(ALBI)score(OR=8.868,95%CI:2.144-36.678,P=0.003)were independent risk factors for PHLF.A nomogram prediction model was developed using these factors.ROC and DCA analyses revealed that the predictive efficacy and clinical value of this model were better than those of traditional models.CONCLUSION A new Nomogram model for predicting PHLF in HCC patients was successfully established based on portal vein width,the NLR,and the ALBI score,which outperforms the traditional model.
基金Natural Science Foundation-funded Project:Mechanism of Action of Detoxification Formula to Inhibit Hypoxia-Inducible Factor 1 Alpha-Exosomal MicroRNA-130b-3p-Sterile Alpha Motif Domain-Containing Protein 90-mediated Macrophage M2-type Polarisation to Improve the Immunosuppressive Microenvironment in Hepatocellular Carcinoma (No.82374540)Medical Innovation Research Project of Shanghai Science and Technology Commission:a Multicenter Prospective Randomized Controlled Study of “Arsenic Target” Combination Therapy for Unresectable Hepatocellular Carcinoma (No.22Y11921200)。
文摘OBJECTIVE:To investigate the clinical efficacy of using a Jiedu formula(解毒方) as an adjunctive therapy in patients with hepatocellular carcinoma(HCC) after hepatectomy.METHODS:In total,354 patients were included in this study.All patients were categorized into the traditional herbal medicine(THM) group(n = 115) or the non-THM treatment(nTHM) group(n = 239),with the Jiedu formula administered twice a day to the patients in the THM group.The primary outcome was recurrence-free survival(RFS).Univariate and multivariate Cox regression analyses were performed to identify the prognostic factors associated with RFS.Then,the high risk of recurrence among patients was identified,and propensity score matching(PSM) and RFS analysis were performed to analyze the prognostic factors for the outcomes of patients at a high risk of recurrence in different groups.RESULTS:The one,two,three,and five-year RFS rates of the THM and nTHM groups were 76.4% vs 66.1%,65.5% vs 48.8%,57.9% vs 39.9%,and 43.9% vs 29.2%,respectively.The results of the Multivariate Cox analysis showed that giant tumors [hazard ratio(HR),1.54,P = 0.04],poor degree of differentiation,microsatellite,or microvascular invasion(HR,1.29,P = 0.09) increased the risk of recurrence.In the population with a high risk of recurrence,after PSM,the one,two,three,and five-year survival rates were 70.6% vs 68.0%,63.0% vs 43.1%,59.6% vs 33.3%,and 41.9% vs 26.4%,respectively.CONCLUSION:In this study,THM was found to be an effective agent for adjuvant therapy for HCC to prevent early recurrence of HCC after hepatic resection.
基金supported by the Key Research and Development Program of the Ministry of Science and Technology of China(grant number:2016YF0900605)the Key Research and Development Program of Hebei Province(grant number:192777129D)+1 种基金the Joint Fund for Iron and Steel of the Natural Science Foundation of Hebei Province(grant number:H2016209058)the National Natural Science Foundation for Regional Joint Fund of China(grant number:U22A20364)。
文摘Objective We aimed to investigate the patterns of fasting blood glucose(FBG)trajectories and analyze the relationship between various occupational hazard factors and FBG trajectories in male steelworkers.Methods The study cohort included 3,728 workers who met the selection criteria for the Tanggang Occupational Cohort(TGOC)between 2017 and 2022.A group-based trajectory model was used to identify the FBG trajectories.Environmental risk scores(ERS)were constructed using regression coefficients from the occupational hazard model as weights.Univariate and multivariate logistic regression analyses were performed to explore the effects of occupational hazard factors using the ERS on FBG trajectories.Results FBG trajectories were categorized into three groups.An association was observed between high temperature,noise exposure,and FBG trajectory(P<0.05).Using the first quartile group of ERS1 as a reference,the fourth quartile group of ERS1 had an increased risk of medium and high FBG by 1.90and 2.21 times,respectively(odds ratio[OR]=1.90,95%confidence interval[CI]:1.17–3.10;OR=2.21,95%CI:1.09–4.45).Conclusion An association was observed between occupational hazards based on ERS and FBG trajectories.The risk of FBG trajectory levels increase with an increase in ERS.
基金funded through India Meteorological Department,New Delhi,India under the Forecasting Agricultural output using Space,Agrometeorol ogy and Land based observations(FASAL)project and fund number:No.ASC/FASAL/KT-11/01/HQ-2010.
文摘Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.
基金Supported by the Laoshan Laboratory(No.LSKJ 202202404)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB 42000000)+1 种基金the National Natural Science Foundation of China(NSFC)(No.42030410)the Startup Foundation for Introducing Talent of NUIST,and the Jiangsu Innovation Research Group(No.JSSCTD 202346)。
文摘Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit significant biases and inter-model differences in simulating ENSO,underscoring the need for alternative modeling approaches.The Regional Ocean Modeling System(ROMS)is a sophisticated ocean model widely used for regional studies and has been coupled with various atmospheric models.However,its application in simulating ENSO processes on a basin scale in the tropical Pacific has not been explored.For the first time,this study presents the development of a basin-scale hybrid coupled model(HCM)for the tropical Pacific,integrating ROMS with a statistical atmospheric model that captures the interannual relationships between sea surface temperature(SST)and wind stress anomalies.The HCM is evaluated for its capability to simulate the annual mean,seasonal,and interannual variations of the oceanic state in the tropical Pacific.Results demonstrate that the model effectively reproduces the ENSO cycle,with a dominant oscillation period of approximately two years.The ROMS-based HCM developed here offers an efficient and robust tool for investigating climate variability in the tropical Pacific.
基金National Key Research and Development Program of China (No.2021YFC3100800)the National Natural Science Foundation of China (Nos.42407235 and 42271026)+1 种基金the Project of Sanya Yazhou Bay Science and Technology City (No.SCKJ-JYRC-2023-54)supported by the Hefei advanced computing center
文摘Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experimental testing,digital core technology,and theoretical modelling.Two CRL types with contrasting mesostructures were characterized across three scales.Macroscopically,CRL-I and CRL-II exhibited mean compressive strengths of 8.46 and 5.17 MPa,respectively.Mesoscopically,CRL-I featured small-scale highly interconnected pores,whilst CRL-II developed larger stratified pores with diminished connectivity.Microscopically,both CRL matrices demonstrated remarkable similarity in mineral composition and mechanical properties.A novel voxel average-based digital core scaling methodology was developed to facilitate numerical simulation of cross-scale damage processes,revealing network-progressive failure in CRL-I versus directional-brittle failure in CRL-II.Furthermore,a damage statistical constitutive model based on digital core technology and mesoscopic homogenisation theory established quantitative relationships between microelement strength distribution and macroscopic mechanical behavior.These findings illuminate the fundamental mechanisms through which mesoscopic structure governs the macroscopic mechanical properties of CRL.
文摘BACKGROUND Clinical predictors of dengue fever are crucial for guiding timely management and avoiding life-threatening complications.While prognostic scores are available,a systematic evaluation of these tools is lacking.AIM To evaluate the performance and accuracy of various proposed dengue clinical prognostic scores.METHODS Three databases,PubMed,EMBASE and Cochrane,were searched for peer-reviewed studies published from inception to 4 September 2023.Studies either developing or validating a prognostic model relevant to dengue fever were included.A total of 29 studies(n=17910)were included.RESULTS Most commonly studied outcomes were severe dengue(15 models)and mortality(8 models).For the paediatric population,Bedside Dengue Severity Score by Gayathri et al(specificity=0.98)and the nomogram model by Nguyen et al(sensitivity=0.87)performed better.For the adult population,the most specific model was reported by Leo et al(specificity=0.98).The most sensitive score is shared between Warning Signs for Severe Dengue as reported by Leo et al and Model 2 by Lee et al(sensitivity=1.00).CONCLUSION While several models demonstrated precision and reliability in predicting severe dengue and mortality,broader application across diverse geographic settings is needed to assess their external validity.
文摘BACKGROUND Chronic liver disease is a growing global health problem,leading to hepatic decompensation characterized by an array of clinical and biochemical complic-ations.Several scoring systems have been introduced in assessing the severity of hepatic decompensation with the most frequent ones are Child-Pugh score,model of end-stage liver disease(MELD)score,and MELD-Na score.Anemia is frequently observed in cirrhotic patients and is linked to worsened clinical outcomes.Although studies have explored anemia in liver disease,few have investigated the correlation of hemoglobin level with the severity of hepatic decompensation.AIM To determine the relationship between hemoglobin levels and the severity of decompensated liver disease and comparing the strength of this correlation using the Child-Pugh,MELD,and MELD-Na scores.METHODS This cross-sectional study was conducted at a tertiary care hospital with 652 decompensated liver disease patients enrolled in the study.Data was collected on demographics,clinical history,and laboratory findings,including hemoglobin levels,bilirubin,albumin,prothrombin time(international normalized ratio),sodium,and creatinine.The Child-Pugh,MELD,and MELD-Na scores were calculated.Statistical analysis was performed using Statistical Package for the Social Sciences version 26,and correlations between hemoglobin levels and severity scores were assessed using Spearman's correlation coefficient.RESULTS The study included 405 males(62.1%)and 247 females(37.9%)with an average age of 58.8 years.Significant inverse correlations were found between hemoglobin levels and Child-Pugh,MELD,and MELD-Na scores(P<0.01),with the MELD scoring system being the strongest correlator among all.One-way analysis of variance revealed significant differences in hemoglobin levels across the severity groups of each scoring system(P=0.001).Tukey's post hoc analysis confirmed significant internal differences among each severity group.CONCLUSION Understanding the correlation between hemoglobin and liver disease severity can improve patient management by offering insights into prognosis and guiding treatment decisions.
文摘Background Renal and liver dysfunction,which are common complications in infectious diseases,are associated with poor clinical outcomes.This study aimed to evaluate the prognostic value of the Model for End-Stage Liver Disease Excluding International Normalized Ratio(MELD-XI)score for predicting short-term mortality in patients with infective endocarditis(IE)complicated by sepsis.Methods A total of 496 consecutive IE patients complicated with sepsis at Guangdong Provincial People's Hospital were enrolled and divided into three groups according to the tertiles of MELD-XI score:<7.9(n=164),7.9-14.6(n=168),and>14.6(n=164).Major adverse clinical events(MACE)were composite endpoints that included acute heart failure,renal dialysis,stroke,and death during hospitalization.Multivariate analysis was used to explore the prognostic value of MELD-XI score.Results In-hospital and 6-month mortality were 14.3%and 21.5%,respectively.In-hospital mortality and the incidence of MACE rose significantly with higher MELD-XI scores(mortality:8.5%vs.12.5%vs.14.3%,P=0.002;Incidence of MACE:24.4%vs.31%vs.51.2%,P<0.001).Receiver operating characteristic(ROC)curve analysis showed that the optimal cutoff value of MELD-XI score was 15.7[area under the curve(AUC):0.648,95%CI:0.578-0.718,P<0.001].Multivariate regression analysis revealed that MELD-XI score>15.7 was a significantly independent risk factor for both in-hospital[adjusted odds ratio(OR):2.27,95%CI:1.28-4.05,P=0.005]and 6-month mortality[adjusted hazard ratio(HR):1.69,95%CI:1.13-2.53,P=0.011].Conclusions MELD-XI score>15.7 was independently associated with short-term mortality in IE patients complicated with sepsis,suggesting its potential value as a prognostic biomarker for risk stratification in this population.
基金supported by grants from the National Natural Science Foundation of China(No.82173593,32300473)Guangzhou Science and Technology Project(No.2025A04J4537,2025A04J4696)+1 种基金Guangdong Basic and Applied Basic Research Foundation(No.2023A1515220053)Postdoctoral Science Foundation of Jiangsu Province(No.2021K524C).
文摘Objective:Neuroblastoma is the most common extracranial solid tumor in children and has complex genetic underpinnings.Previous genome-wide association studies(GWASs)have identified many loci associated with neuroblastoma susceptibility;however,their application in risk prediction for Chinese children has not been systematically explored.This study seeks to enhance neuroblastoma risk prediction by validating these loci and evaluating their performance in polygenic risk models.Methods:We validated 35 GWAS-identified neuroblastoma susceptibility loci in a cohort of Chinese children,consisting of 402 neuroblastoma patients and 473 healthy controls.Genotyping these polymorphisms was conducted via the TaqMan method.Univariable and multivariable logistic regression analyses revealed the genetic loci significantly associated with neuroblastoma risk.We constructed polygenic risk models by combining these loci and assessed their predictive performance via area under the curve(AUC)analysis.We also established a polygenic risk scoring(PRS)model for risk prediction by adopting the PLINK method.Results:Fourteen loci,including ten protective polymorphisms from CASC15,BARD1,LMO1,HSD17B12,and HACE1,and four risk variants from BARD1,RSRC1,CPZ and MMP20 were significantly associated with neuroblastoma risk.Compared with single-gene model,the 8-gene model(AUC=0.72)and 13-gene model(AUC=0.73)demonstrated superior predictive performance.Additionally,a PRS incorporating six significant loci achieved an AUC of 0.66,effectively stratifying individuals into distinct risk categories regarding neuroblastoma susceptibility.A higher PRS was significantly associated with advanced International Neuroblastoma Staging System(INSS)stages,suggesting its potential for clinical risk stratification.Conclusions:Our findings validate multiple loci as neuroblastoma risk factors in Chinese children and demonstrate the utility of polygenic risk models,particularly the PRS,in improving risk prediction.These results suggest that integrating multiple genetic variants into a PRS can enhance neuroblastoma risk stratification and potentially improve early diagnosis by guiding targeted screening programs for high-risk children.
基金funded,in part,by the National Natural Science Fund (NSFC,China) under award number 81900382supported,in part,by the Yang talents Program of Beijing (QML20200302)Beijing Municipal Natural Science Foundation (7222072).
文摘Background Biomarkers-based prediction of long-term risk of acute coronary syndrome(ACS)is scarce.We aim to develop a risk score integrating clinical routine information(C)and plasma biomarkers(B)for predicting long-term risk of ACS patients.Methods We included 2729 ACS patients from the OCEA(Observation of cardiovascular events in ACS patients).The earlier admitted 1910 patients were enrolled as development cohort;and the subsequently admitted 819 subjects were treated as valida-tion cohort.We investigated 10-year risk of cardiovascular(CV)death,myocardial infarction(MI)and all cause death in these pa-tients.Potential variables contributing to risk of clinical events were assessed using Cox regression models and a score was de-rived using main part of these variables.Results During 16,110 person-years of follow-up,there were 238 CV death/MI in the development cohort.The 7 most import-ant predictors including in the final model were NT-proBNP,D-dimer,GDF-15,peripheral artery disease(PAD),Fibrinogen,ST-segment elevated MI(STEMI),left ventricular ejection fraction(LVEF),termed as CB-ACS score.C-index of the score for predica-tion of cardiovascular events was 0.79(95%CI:0.76-0.82)in development cohort and 0.77(95%CI:0.76-0.78)in the validation co-hort(5832 person-years of follow-up),which outperformed GRACE 2.0 and ABC-ACS risk score.The CB-ACS score was also well calibrated in development and validation cohort(Greenwood-Nam-D’Agostino:P=0.70 and P=0.07,respectively).Conclusions CB-ACS risk score provides a useful tool for long-term prediction of CV events in patients with ACS.This model outperforms GRACE 2.0 and ABC-ACS ischemic risk score.
