This paper presents a control strategy of demand pulled spare parts inventory. It establishes a spare part demand prognosticating model based on reliability analysis. Through parts reliability data the model gets the...This paper presents a control strategy of demand pulled spare parts inventory. It establishes a spare part demand prognosticating model based on reliability analysis. Through parts reliability data the model gets the reliable life function of spare parts and determines parts demand time, depending on part life at given reliabilities. Moreover, a case study is taken to illuminate the demand prognostication and inventory control of on condition maintenance rotables.展开更多
BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are p...BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.展开更多
Based on the El Nino event data sequence from 1854 to 1993, the nature of sequences was de-termined by using statistical normal and independent tests, etc. With the Markov random process and first order auto-regressio...Based on the El Nino event data sequence from 1854 to 1993, the nature of sequences was de-termined by using statistical normal and independent tests, etc. With the Markov random process and first order auto-regression predictive model, we set up the prognostication mode and give the time limit of the occurrence of next El Nino event, which probably occurs around 2002.The occurring probability for 2001 is 44 %, and it is 61 % for 2002.展开更多
The systemic response to tissue injury, regardless of cause is characterized by a cytokine-mediated alteration in the hepatic synthesis of a number of different plasma proteins,known collectively as 'acute pha... The systemic response to tissue injury, regardless of cause is characterized by a cytokine-mediated alteration in the hepatic synthesis of a number of different plasma proteins,known collectively as 'acute phase reactants'. These proteins include C-reactive protein, serum amyloid A protein, alphal glycoprotein, ceruloplasmin, alpha macroglobulins, complement components (C1-C4, factor B, C9, C11), alpha1antitrypsin, alpha1 antichymotrypsin, fibrinogen, prothrombin,factor Ⅷ, plasminogen, haptoglobin, ferritin, immunoglobulins and lipoproteins. The initiation of the acute phase response is linked to the production of hormone-like polypeptide mediators now called cytokines, namedly, interleukin 1(IL-1),tumor necrosis factor, interferon gamma, interleukin 6 (IL-6),leukemia inhibitory factor, ciliary neurotropic factor, oncostatin M, and interleukin 11 (IL- 11).……展开更多
Background:Liver resection and local ablation are the only curative treatment for non-cirrhotic hepatocellular carcinoma(HCC).Few data exist concerning the prognosis of patients resected for non-cirrhotic HCC.The obje...Background:Liver resection and local ablation are the only curative treatment for non-cirrhotic hepatocellular carcinoma(HCC).Few data exist concerning the prognosis of patients resected for non-cirrhotic HCC.The objectives of this study were to determine the prognostic factors of recurrence-free survival(RFS)and overall survival(OS)and to develop a prognostication algorithm for non-cirrhotic HCC.Methods:French multicenter retrospective study including HCC patients with non-cirrhotic liver without underlying viral hepatitis:F0,F1 or F2 fibrosis.Results:A total of 467 patients were included in 11 centers from 2010 to 2018.Non-cirrhotic liver had a fibrosis score of F0(n=237,50.7%),F1(n=127,27.2%)or F2(n=103,22.1%).OS and RFS at 5 years were 59.2%and 34.5%,respectively.In multivariate analysis,microvascular invasion and HCC differentiation were prognostic factors of OS and RFS and the number and size were prognostic factors of RFS(P<0.005).Stratification based on RFS provided an algorithm based on size(P=0.013)and number(P<0.001):2 HCC with the largest nodule≤10 cm(n=271,Group 1);2 HCC with a nodule>10 cm(n=176,Group 2);>2 HCC regardless of size Conclusions:We developed a prognostication algorithm based on the number(≤or>2)and size(≤or>10 cm),which could be used as a treatment decision support concerning the need for perioperative therapy.In case of bifocal HCC,surgery should not be a contraindication.展开更多
This commentary critically appraises the study by Li et al which pioneered the exploration of the triglyceride-glucose(TyG)index as a prognostic marker in hepatitis B virus-related advanced hepatocellular carcinoma pa...This commentary critically appraises the study by Li et al which pioneered the exploration of the triglyceride-glucose(TyG)index as a prognostic marker in hepatitis B virus-related advanced hepatocellular carcinoma patients undergoing combined camrelizumab and lenvatinib therapy.While we acknowledge the study’s clinical relevance in proposing an easily accessible metabolic biomarker,we delve into the mechanistic plausibility linking insulin resistance to immunotherapy response and angiogenic inhibition.We further critically examine the methodological limitations,including the retrospective design,the populationspecific TyG cut-off value,and unaddressed metabolic confounders.We highlight the imperative for future research to validate its utility across diverse etiologies and treatment settings,and to unravel the underlying immunometabolic pathways.展开更多
BACKGROUND Breast cancer is one of the most prevalent malignancies affecting women worldwide,with approximately 2.3 million new cases diagnosed annually.Breast cancer stem cells(BCSCs)play pivotal roles in tumor initi...BACKGROUND Breast cancer is one of the most prevalent malignancies affecting women worldwide,with approximately 2.3 million new cases diagnosed annually.Breast cancer stem cells(BCSCs)play pivotal roles in tumor initiation,progression,metastasis,therapeutic resistance,and disease recurrence.Cancer stem cells possess selfrenewal capacity,multipotent differentiation potential,and enhanced tumorigenic activity,but their molecular characteristics and regulatory mechanisms require further investigation.AIM To comprehensively characterize the molecular features of BCSCs through multiomics approaches,construct a prognostic prediction model based on stem cellrelated genes,reveal cell-cell communication networks within the tumor microenvironment,and provide theoretical foundation for personalized treatment strategies.METHODS Flow cytometry was employed to detect the expression of BCSC surface markers(CD34,CD45,CD29,CD90,CD105).Transcriptomic analysis was performed to identify differentially expressed genes.Least absolute shrinkage and selection operator regression analysis was utilized to screen key prognostic genes and construct a risk scoring model.Single-cell RNA sequencing and spatial transcriptomics were applied to analyze tumor heterogeneity and spatial gene expression patterns.Cell-cell communication network analysis was conducted to reveal interactions between stem cells and the microenvironment.RESULTS Flow cytometric analysis revealed the highest expression of CD105(96.30%),followed by CD90(68.43%)and CD34(62.64%),while CD29 showed lower expression(7.16%)and CD45 exhibited the lowest expression(1.19%).Transcriptomic analysis identified 3837 significantly differentially expressed genes(1478 upregulated and 2359 downregulated).Least absolute shrinkage and selection operator regression analysis selected 10 key prognostic genes,and the constructed risk scoring model effectively distinguished between high-risk and low-risk patient groups(P<0.001).Single-cell analysis revealed tumor cellular heterogeneity,and spatial transcriptomics demonstrated distinct spatial expression gradients of stem cell-related genes.MED18 gene showed significantly higher expression in malignant tissues(P<0.001)and occupied a central position in cell-cell communication networks,exhibiting significant correlations with tumor cells,macrophages,fibroblasts,and endothelial cells.CONCLUSION This study comprehensively characterized the molecular features of BCSCs through multi-omics approaches,identified reliable surface markers and key regulatory genes,and constructed a prognostic prediction model with clinical application value.展开更多
Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including ...Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.展开更多
Background:Currently,surgical resection is the mainstay for colorectal liver metastases(CRLM)management and the only potentially curative treatment modality.Prognostication tools can support patient selection for surg...Background:Currently,surgical resection is the mainstay for colorectal liver metastases(CRLM)management and the only potentially curative treatment modality.Prognostication tools can support patient selection for surgical resection to maximize therapeutic benefit.This study aimed to develop a survival prediction model using machine learning based on a multicenter patient sample in Hong Kong.Methods:Patients who underwent hepatectomy for CRLM between 1 January 2009 and 31 December 2018 in four hospitals in Hong Kong were included in the study.Survival analysis was performed using Cox proportional hazards(CPH).A stepwise selection on Cox multivariable models with Least Absolute Shrinkage and Selection Operator(LASSO)regression was applied to a multiply-imputed dataset to build a prediction model.The model was validated in the validation set,and its performance was compared with that of Fong Clinical Risk Score(CRS)using concordance index.Results:A total of 572 patients were included with a median follow-up of 3.6 years.The full models for overall survival(OS)and recurrence-free survival(RFS)consist of the same 8 established and novel variables,namely colorectal cancer nodal stage,CRLM neoadjuvant treatment,Charlson Comorbidity Score,pre-hepatectomy bilirubin and carcinoembryonic antigen(CEA)levels,CRLM largest tumor diameter,extrahepatic metastasis detected on positron emission-tomography(PET)-scan as well as KRAS status.Our CRLM Machine-learning Algorithm Prognostication model(CMAP)demonstrated better ability to predict OS(C-index=0.651),compared with the Fong CRS for 1-year(C-index=0.571)and 5-year OS(C-index=0.574).It also achieved a C-index of 0.651 for RFS.Conclusions:We present a promising machine learning algorithm to individualize prognostications for patients following resection of CRLM with good discriminative ability.展开更多
Prognostication of compensated advanced chronic liver disease(cACLD)is of paramount importance for the physician-and-patient communication and for rational clinical decisions.The paper published by Dallio et al report...Prognostication of compensated advanced chronic liver disease(cACLD)is of paramount importance for the physician-and-patient communication and for rational clinical decisions.The paper published by Dallio et al reports on red cell distribution width(RDW)/platelet ratio(RPR)as a non-invasive biomarker in predicting decompensation of metabolic dysfunction-associated steatotic liver disease(MASLD)-related cACLD.Differently from other biomarkers and algorithms,RPR is inexpensive and widely available,based on parameters which are included in a complete blood count.RPR is computed on the grounds of two different items,one of which,RDW,mirrors the host’s response to a variety of disease stimuli and is non-specific.The second parameter involved in RPR,platelet count,is more specific and has been used in the hepatological clinic to discriminate cirrhotic from non-cirrhotic chronic liver disease for decades.Cardiovascular disease is the primary cause of mortality among MASLD subjects,followed by extra-hepatic cancers and liver-related mortality.Therefore,MASLD biomarkers should be validated not only in terms of liver-related events but also in the prediction of major adverse cardiovascular events and cardiovascular mortality and extra-hepatic cancers.Adequately sized multi-ethnic confirmatory investigation is required to define the role and significance of RPR in the stratification of MASLD-cACLD.展开更多
Detection and treatment of colorectal cancer(CRC)at an early stage is vital for long-term survival.Liquid biopsy has emerged as a promising new avenue for non-invasive screening of CRC as well as prognostication and s...Detection and treatment of colorectal cancer(CRC)at an early stage is vital for long-term survival.Liquid biopsy has emerged as a promising new avenue for non-invasive screening of CRC as well as prognostication and surveillance of minimal residual disease.Cell free DNA(cfDNA)is a promising liquid biopsy analyte and has been approved for use in clinical practice.Here,we explore the current challenges of utilizing cfDNA in the screening and prognostication of CRC but also for detecting driver mutations in healthy,presymptomatic patients with normal colonic crypts.CfDNA for the detection of cancerous or premalignant colonic lesions has already been extensively explored,however few have considered utilizing cfDNA in the detection of driver mutations in healthy patients.Theoretically,this would allow us to detect patients who are at a higher risk of tumorigenesis decades in advance of established malignancy and stratify them into higher risk groups for early-intervention screening programs.We also explore the solutions necessary to overcome the challenges that prevent liquid biopsy from entering mainstream clinical use.The potential for liquid biopsy is immense if these challenges are successfully circumvented,and can dramatically reduce CRC rates as well as improve survival in patients.展开更多
Background:Spontaneous intracerebral hemorrhage(ICH)is a severe neurological emergency with high morbidity and mortality.The effectiveness of surgical intervention remains controversial,partly due to significant heter...Background:Spontaneous intracerebral hemorrhage(ICH)is a severe neurological emergency with high morbidity and mortality.The effectiveness of surgical intervention remains controversial,partly due to significant heterogeneity among patients.Traditional clinical criteria often fail to identify those most likely to benefit from surgery.Methods:This nationwide retrospective study in China included 2,167 ICH patients from 31 hospitals.Using machine learning techniques,we integrated clinical and radiomic data to perform unsupervised clustering and identify distinct phenogroups.Dimensionality reduction and cross-validation were applied to minimize overfitting.External validation was conducted using data from the INTERACT3 trial,and a prospective cohort was used to assess real-world applicability.Results:Three phenogroups were identified.Among them,only Phenogroup 1-characterized by older age,moderate hematoma volume,and intermediate Glasgow Coma Scale scores-showed significant benefit from early surgical intervention,with a 42%reduction in 3-month mortality and improved functional outcomes.In contrast,surgery did not significantly affect outcomes in Phenogroups 0 and 2.These findings were consistent across multiple machine learning models and validated externally.Conclusion:Machine learning-driven phenotypic stratification can effectively identify ICH patients who are most likely to benefit from surgical treatment.This approach supports personalized treatment strategies and may improve clinical decision-making in ICH management.Further validation in diverse populations is warranted.展开更多
Perioperative morbidity of esophagectomy significantly affects the surgical outcome,like any major gastrointestinal procedure.Despite introduction of minimally invasive esophagectomy,the morbidity is still close to 30...Perioperative morbidity of esophagectomy significantly affects the surgical outcome,like any major gastrointestinal procedure.Despite introduction of minimally invasive esophagectomy,the morbidity is still close to 30%-40%.The common complications following esophagectomy are pulmonary infections,cardiac events,anastomotic leakage,bleeding,chylous leak,and recurrent laryngeal nerve palsy which in turn lead to longer hospital stay,increased treatment cost and poor quality of life.A nomographic model comprising preoperative(patient,disease and treatment related)and intraoperative factors in combination with Artificial Intelligence may accurately identify the patients at higher risk of morbidity.This will aid in optimizing the modifiable risk factors preoperatively,and closely monitor these patients post operatively for early identification of complications and to initiate early corrective measures to improve the surgical outcome.展开更多
BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized p...BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.展开更多
BACKGROUND Emerging evidence implicates Candida albicans(C.albicans)in human oncogenesis.Notably,studies have supported its involvement in regulating outcomes in colorectal cancer(CRC).This study investigated the para...BACKGROUND Emerging evidence implicates Candida albicans(C.albicans)in human oncogenesis.Notably,studies have supported its involvement in regulating outcomes in colorectal cancer(CRC).This study investigated the paradoxical role of C.albicans in CRC,aiming to determine whether it promotes or suppresses tumor development,with a focus on the mechanistic basis linked to its metabolic profile.AIM To investigate the dual role of C.albicans in the development and progression of CRC through metabolite profiling and to establish a prognostic model that integrates the microbial and metabolic interactions in CRC,providing insights into potential therapeutic strategies and clinical outcomes.METHODSA prognostic model integrating C. albicans with CRC was developed, incorporating enrichment analysis, immuneinfiltration profiling, survival analysis, Mendelian randomization, single-cell sequencing, and spatial transcriptomics.The effects of the C. albicans metabolite mixture on CRC cells were subsequently validated in vitro. Theprimary metabolite composition was characterized using liquid chromatography-mass spectrometry.RESULTSA prognostic model based on five specific mRNA markers, EHD4, LIME1, GADD45B, TIMP1, and FDFT1, wasestablished. The C. albicans metabolite mixture significantly reduced CRC cell viability. Post-treatment analysisrevealed a significant decrease in gene expression in HT29 cells, while the expression levels of TIMP1, EHD4, andGADD45B were significantly elevated in HCT116 cells. Conversely, LIME1 expression and that of other CRC celllines showed reductions. In normal colonic epithelial cells (NCM460), GADD45B, TIMP1, and FDFT1 expressionlevels were significantly increased, while LIME1 and EHD4 levels were markedly reduced. Following metabolitetreatment, the invasive and migratory capabilities of NCM460, HT29, and HCT116 cells were reduced. Quantitativeanalysis of extracellular ATP post-treatment showed a significant elevation (P < 0.01). The C. albicans metabolitemixture had no effect on reactive oxygen species accumulation in CRC cells but led to a reduction in mitochondrialmembrane potential, increased intracellular lipid peroxidation, and induced apoptosis. Metabolomic profilingrevealed significant alterations, with 516 metabolites upregulated and 531 downregulated.CONCLUSIONThis study introduced a novel prognostic model for CRC risk assessment. The findings suggested that the C.albicans metabolite mixture exerted an inhibitory effect on CRC initiation.展开更多
Rectal neuroendocrine tumors(r-NETs)are the second most common type of neuroendocrine tumor in the gastrointestinal tract,with an increase in incidence in the last decades.They are low-grade tumors and,given their low...Rectal neuroendocrine tumors(r-NETs)are the second most common type of neuroendocrine tumor in the gastrointestinal tract,with an increase in incidence in the last decades.They are low-grade tumors and,given their low risk of meta-stasis,current guidelines recommend endoscopic resection for small lesions.The GATIS predicting score,proposed by Zeng et al,represents an innovative model designed to predict individualized survival outcomes for patients with r-NETs,analyzing the relationship between clinicopathological features and patient prog-noses.The authors identified tumor grade,T stage,tumor size,age,and progno-stic nutritional index as key prognostic factors,demonstrating that the GATIS Score provides a more accurate prognosis assessment compared to the World Health Organization classification or the tumor-node-metastasis staging system.Nevertheless,further larger prospective studies are necessary,and the scientific community's efforts in this context should be directed toward developing interna-tional multicentric prospective studies,with the ultimate aim of accurately de-fining and understanding the behavior of these conditions.展开更多
BACKGROUND Acute-on-chronic liver failure(ACLF)is a liver disease based on chronic liver disease,which is significantly influenced by clinical treatment regimen and disease status,and despite the existence of multiple...BACKGROUND Acute-on-chronic liver failure(ACLF)is a liver disease based on chronic liver disease,which is significantly influenced by clinical treatment regimen and disease status,and despite the existence of multiple prognostic assessment indicators for ACLF,the overall sensitivity and accuracy are relatively low.AIM To investigate the prognostic value of the combined detection of alpha-fetoprotein(AFP),plasma prothrombin activity(PTA),and serum prealbumin(PA)in ACLF.METHODS This retrospective study included 87 patients with ACLF admitted from February 2021 to February 2023 and categorized them into the survival(n=47)and death(n=40)groups according to their clinical outcomes 3 months posttreatment.All the participants underwent AFP,PTA,and PA level measurements upon admission.Baseline data,as well as AFP,PTA,and PA levels,were comparatively analyzed.Pearson correlation coefficients were utilized to analyze the correlations of AFP,PTA,and PA with different survival outcomes in patients with ACLF.Receiver operating characteristic(ROC)curves and areas under the curves were used to evaluate the predictive value of AFP,PTA,and PA for ACLF prognosis.RESULTS AFP,PTA,and PA levels were markedly decreased in the death group than in the survival group(P<0.05).Pearson analysis indicated a positive association of the AFP,PTA,and PA levels with the survival of patients with ACLF(P<0.05).ROC curve analysis determined the sensitivity and specificity of the combined diagnosis at 91.24%and 100.00%,respectively,both of which were notably increased compared to the single-index diagnosis.The ROC of their combined diagnosis was 0.989,significantly surpassing 0.907,0.849,and 0.853 of AFP,PTA,and PA,respectively.No statistically significant variance was determined in the sensitivity and specificity of the combined diagnosis vs the single detection(P>0.05).CONCLUSION The combined detection of AFP,PTA,and PA levels demonstrates favorable diagnostic value for the short-term prognosis of patients with ACLF,featuring high sensitivity and specificity.展开更多
Cardiovascular diseases(CVDs)remain the leading cause of morbidity and mortality worldwide,necessitating innovative diagnostic and prognostic strategies.Traditional biomarkers like C-reactive protein,uric acid,troponi...Cardiovascular diseases(CVDs)remain the leading cause of morbidity and mortality worldwide,necessitating innovative diagnostic and prognostic strategies.Traditional biomarkers like C-reactive protein,uric acid,troponin,and natriuretic peptides play crucial roles in CVD management,yet they are often limited by sensitivity and specificity constraints.This narrative review critically examines the emerging landscape of cardiac biomarkers and advocates for a multiple-marker approach to enhance early detection,prognosis,and risk stratification of CVD.In recent years,several novel biomarkers have shown promise in revolutionizing CVD diagnostics.Gamma-glutamyltransferase,microRNAs,endothelial microparticles,placental growth factor,trimethylamine N-oxide,retinol-binding protein 4,copeptin,heart-type fatty acid-binding protein,galectin-3,growth differentiation factor-15,soluble suppression of tumorigenicity 2,fibroblast growth factor 23,and adrenomedullin have emerged as significant indicators of CV health.These biomarkers provide insights into various pathophysiological processes,such as oxidative stress,endothelial dysfunction,inflammation,metabolic disturbances,and myocardial injury.The integration of these novel biomarkers with traditional ones offers a more comprehensive understanding of CVD mechanisms.This multiple-marker approach can improve diagnostic accuracy,allowing for better risk stratification and more personalized treatment strategies.This review underscores the need for continued research to validate the clinical utility of these biomarkers and their potential incorporation into routine clinical practice.By leveraging the strengths of both traditional and novel biomarkers,precise therapeutic plans can be developed,thereby improving the management and prognosis of patients with CVDs.The ongoing exploration and validation of these biomarkers are crucial for advancing CV care and addressing the limitations of current diagnostic tools.展开更多
BACKGROUND Anastomotic leakage(AL)is a serious complication following rectal cancer surgery and is associated with increased recurrence,mortality,extended hospital stays,and delayed chemotherapy.The Onodera prognostic...BACKGROUND Anastomotic leakage(AL)is a serious complication following rectal cancer surgery and is associated with increased recurrence,mortality,extended hospital stays,and delayed chemotherapy.The Onodera prognostic nutritional index(OPNI)and inflammation-related biomarkers,such as the neutrophil-lymphocyte ratio(NLR)and platelet-to-lymphocyte ratio(PLR),have been studied in the context of cancer prognosis,but their combined efficacy in predicting AL remains unclear.AIM To investigate the relationships between AL and these markers and developed a predictive model for AL.METHODS A retrospective cohort study analyzed the outcomes of 434 patients who had undergone surgery for rectal cancer at a tertiary cancer center from 2016 to 2023.The patients were divided into two groups on the basis of the occurrence of AL:One group consisted of patients who experienced AL(n=49),and the other group did not(n=385).The investigation applied logistic regression to develop a risk prediction model utilizing clinical,pathological,and laboratory data.The efficacy of this model was then evaluated through receiver operating characteristic curve analysis.RESULTS In the present study,11.28%of the participants(49 out of 434 participants)suffered from AL.Multivariate analysis revealed that preoperative levels of the OPNI,NLR,and PLR emerged as independent risk factors for AL,with odds ratios of 0.705(95%CI:0.641-0.775,P=0.012),1.628(95%CI:1.221-2.172,P=0.024),and 0.994(95%CI:0.989-0.999,P=0.031),respectively.These findings suggest that these biomarkers could effectively predict AL risk.Furthermore,the proposed predictive model has superior discriminative ability,as demonstrated by an area under the curve of 0.910,a sensitivity of 0.898,and a specificity of 0.826,reflecting its high level of accuracy.CONCLUSION The risk of AL in rectal cancer surgery patients can be effectively predicted by assessing the preoperative levels of serum nutritional biomarkers and inflammatory indicators,emphasizing their importance in the preoperative evaluation process.展开更多
Objective ZW10 interacting kinetochore protein(ZWINT)has been demonstrated to play a pivotal role in the growth,invasion,and migration of cancers.Nevertheless,whether the expression levels of ZWINT are significantly c...Objective ZW10 interacting kinetochore protein(ZWINT)has been demonstrated to play a pivotal role in the growth,invasion,and migration of cancers.Nevertheless,whether the expression levels of ZWINT are significantly correlated with clinicopathological characteristics and prognostic outcomes of patients with breast cancer remains elusive.This study systematically investigated the clinical significance of ZWINT expression in breast cancer through integrated molecular subtyping and survival analysis.Methods We systematically characterized the spatial expression pattern of ZWINT across various breast cancer subtypes and assessed its prognostic significance using an integrated bioinformatics approach that involved multi-omics analysis.The approach included the Breast Cancer Gene-Expression Miner v5.1(bc-GenExMiner v5.1),TNMplot,MuTarget,PrognoScan database,and Database for Annotation,Visualization,and Integrated Discovery(DAVID).Results Our analysis revealed consistent upregulation of ZWINT mRNA and protein expression across distinct clinicopathological subtypes of breast cancer.ZWINT overexpression demonstrated significant co-occurrence with truncating mutations in cadherin 1(CDH1)and tumor protein p53(TP53),suggesting potential functional crosstalk in tumor progression pathways.The overexpression of ZWINT correlated with adverse clinical outcomes,showing 48%increased mortality risk(overall survival:HR 1.48,95%CI 1.23–1.79),66%higher recurrence probability(relapse-free survival:1.66,95%CI 1.50–1.84),and 63%elevated metastasis risk(distant metastasis-free survival:HR 1.63,95%CI 1.39–1.90).Multivariate Cox regression incorporating TNM staging and molecular subtypes confirmed ZWINT as an independent prognostic determinant(P<0.001,Harrell’s C-index=0.7827),which was validated through bootstrap resampling(1000 iterations).Conclusion ZWINT may serve as a potential biomarker for prognosis and a possible therapeutic target alongside TP53/CDH1 in breast cancer.展开更多
文摘This paper presents a control strategy of demand pulled spare parts inventory. It establishes a spare part demand prognosticating model based on reliability analysis. Through parts reliability data the model gets the reliable life function of spare parts and determines parts demand time, depending on part life at given reliabilities. Moreover, a case study is taken to illuminate the demand prognostication and inventory control of on condition maintenance rotables.
文摘BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.
基金Research project of meteorological science and technology in China (96-908-05-03)
文摘Based on the El Nino event data sequence from 1854 to 1993, the nature of sequences was de-termined by using statistical normal and independent tests, etc. With the Markov random process and first order auto-regression predictive model, we set up the prognostication mode and give the time limit of the occurrence of next El Nino event, which probably occurs around 2002.The occurring probability for 2001 is 44 %, and it is 61 % for 2002.
文摘 The systemic response to tissue injury, regardless of cause is characterized by a cytokine-mediated alteration in the hepatic synthesis of a number of different plasma proteins,known collectively as 'acute phase reactants'. These proteins include C-reactive protein, serum amyloid A protein, alphal glycoprotein, ceruloplasmin, alpha macroglobulins, complement components (C1-C4, factor B, C9, C11), alpha1antitrypsin, alpha1 antichymotrypsin, fibrinogen, prothrombin,factor Ⅷ, plasminogen, haptoglobin, ferritin, immunoglobulins and lipoproteins. The initiation of the acute phase response is linked to the production of hormone-like polypeptide mediators now called cytokines, namedly, interleukin 1(IL-1),tumor necrosis factor, interferon gamma, interleukin 6 (IL-6),leukemia inhibitory factor, ciliary neurotropic factor, oncostatin M, and interleukin 11 (IL- 11).……
文摘Background:Liver resection and local ablation are the only curative treatment for non-cirrhotic hepatocellular carcinoma(HCC).Few data exist concerning the prognosis of patients resected for non-cirrhotic HCC.The objectives of this study were to determine the prognostic factors of recurrence-free survival(RFS)and overall survival(OS)and to develop a prognostication algorithm for non-cirrhotic HCC.Methods:French multicenter retrospective study including HCC patients with non-cirrhotic liver without underlying viral hepatitis:F0,F1 or F2 fibrosis.Results:A total of 467 patients were included in 11 centers from 2010 to 2018.Non-cirrhotic liver had a fibrosis score of F0(n=237,50.7%),F1(n=127,27.2%)or F2(n=103,22.1%).OS and RFS at 5 years were 59.2%and 34.5%,respectively.In multivariate analysis,microvascular invasion and HCC differentiation were prognostic factors of OS and RFS and the number and size were prognostic factors of RFS(P<0.005).Stratification based on RFS provided an algorithm based on size(P=0.013)and number(P<0.001):2 HCC with the largest nodule≤10 cm(n=271,Group 1);2 HCC with a nodule>10 cm(n=176,Group 2);>2 HCC regardless of size Conclusions:We developed a prognostication algorithm based on the number(≤or>2)and size(≤or>10 cm),which could be used as a treatment decision support concerning the need for perioperative therapy.In case of bifocal HCC,surgery should not be a contraindication.
文摘This commentary critically appraises the study by Li et al which pioneered the exploration of the triglyceride-glucose(TyG)index as a prognostic marker in hepatitis B virus-related advanced hepatocellular carcinoma patients undergoing combined camrelizumab and lenvatinib therapy.While we acknowledge the study’s clinical relevance in proposing an easily accessible metabolic biomarker,we delve into the mechanistic plausibility linking insulin resistance to immunotherapy response and angiogenic inhibition.We further critically examine the methodological limitations,including the retrospective design,the populationspecific TyG cut-off value,and unaddressed metabolic confounders.We highlight the imperative for future research to validate its utility across diverse etiologies and treatment settings,and to unravel the underlying immunometabolic pathways.
基金the Natural Science Foundation of Yongchuan District,No.2023yc-jckx20021.
文摘BACKGROUND Breast cancer is one of the most prevalent malignancies affecting women worldwide,with approximately 2.3 million new cases diagnosed annually.Breast cancer stem cells(BCSCs)play pivotal roles in tumor initiation,progression,metastasis,therapeutic resistance,and disease recurrence.Cancer stem cells possess selfrenewal capacity,multipotent differentiation potential,and enhanced tumorigenic activity,but their molecular characteristics and regulatory mechanisms require further investigation.AIM To comprehensively characterize the molecular features of BCSCs through multiomics approaches,construct a prognostic prediction model based on stem cellrelated genes,reveal cell-cell communication networks within the tumor microenvironment,and provide theoretical foundation for personalized treatment strategies.METHODS Flow cytometry was employed to detect the expression of BCSC surface markers(CD34,CD45,CD29,CD90,CD105).Transcriptomic analysis was performed to identify differentially expressed genes.Least absolute shrinkage and selection operator regression analysis was utilized to screen key prognostic genes and construct a risk scoring model.Single-cell RNA sequencing and spatial transcriptomics were applied to analyze tumor heterogeneity and spatial gene expression patterns.Cell-cell communication network analysis was conducted to reveal interactions between stem cells and the microenvironment.RESULTS Flow cytometric analysis revealed the highest expression of CD105(96.30%),followed by CD90(68.43%)and CD34(62.64%),while CD29 showed lower expression(7.16%)and CD45 exhibited the lowest expression(1.19%).Transcriptomic analysis identified 3837 significantly differentially expressed genes(1478 upregulated and 2359 downregulated).Least absolute shrinkage and selection operator regression analysis selected 10 key prognostic genes,and the constructed risk scoring model effectively distinguished between high-risk and low-risk patient groups(P<0.001).Single-cell analysis revealed tumor cellular heterogeneity,and spatial transcriptomics demonstrated distinct spatial expression gradients of stem cell-related genes.MED18 gene showed significantly higher expression in malignant tissues(P<0.001)and occupied a central position in cell-cell communication networks,exhibiting significant correlations with tumor cells,macrophages,fibroblasts,and endothelial cells.CONCLUSION This study comprehensively characterized the molecular features of BCSCs through multi-omics approaches,identified reliable surface markers and key regulatory genes,and constructed a prognostic prediction model with clinical application value.
基金Supported by Xuhui District Health Commission,No.SHXH202214.
文摘Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.
文摘Background:Currently,surgical resection is the mainstay for colorectal liver metastases(CRLM)management and the only potentially curative treatment modality.Prognostication tools can support patient selection for surgical resection to maximize therapeutic benefit.This study aimed to develop a survival prediction model using machine learning based on a multicenter patient sample in Hong Kong.Methods:Patients who underwent hepatectomy for CRLM between 1 January 2009 and 31 December 2018 in four hospitals in Hong Kong were included in the study.Survival analysis was performed using Cox proportional hazards(CPH).A stepwise selection on Cox multivariable models with Least Absolute Shrinkage and Selection Operator(LASSO)regression was applied to a multiply-imputed dataset to build a prediction model.The model was validated in the validation set,and its performance was compared with that of Fong Clinical Risk Score(CRS)using concordance index.Results:A total of 572 patients were included with a median follow-up of 3.6 years.The full models for overall survival(OS)and recurrence-free survival(RFS)consist of the same 8 established and novel variables,namely colorectal cancer nodal stage,CRLM neoadjuvant treatment,Charlson Comorbidity Score,pre-hepatectomy bilirubin and carcinoembryonic antigen(CEA)levels,CRLM largest tumor diameter,extrahepatic metastasis detected on positron emission-tomography(PET)-scan as well as KRAS status.Our CRLM Machine-learning Algorithm Prognostication model(CMAP)demonstrated better ability to predict OS(C-index=0.651),compared with the Fong CRS for 1-year(C-index=0.571)and 5-year OS(C-index=0.574).It also achieved a C-index of 0.651 for RFS.Conclusions:We present a promising machine learning algorithm to individualize prognostications for patients following resection of CRLM with good discriminative ability.
文摘Prognostication of compensated advanced chronic liver disease(cACLD)is of paramount importance for the physician-and-patient communication and for rational clinical decisions.The paper published by Dallio et al reports on red cell distribution width(RDW)/platelet ratio(RPR)as a non-invasive biomarker in predicting decompensation of metabolic dysfunction-associated steatotic liver disease(MASLD)-related cACLD.Differently from other biomarkers and algorithms,RPR is inexpensive and widely available,based on parameters which are included in a complete blood count.RPR is computed on the grounds of two different items,one of which,RDW,mirrors the host’s response to a variety of disease stimuli and is non-specific.The second parameter involved in RPR,platelet count,is more specific and has been used in the hepatological clinic to discriminate cirrhotic from non-cirrhotic chronic liver disease for decades.Cardiovascular disease is the primary cause of mortality among MASLD subjects,followed by extra-hepatic cancers and liver-related mortality.Therefore,MASLD biomarkers should be validated not only in terms of liver-related events but also in the prediction of major adverse cardiovascular events and cardiovascular mortality and extra-hepatic cancers.Adequately sized multi-ethnic confirmatory investigation is required to define the role and significance of RPR in the stratification of MASLD-cACLD.
文摘Detection and treatment of colorectal cancer(CRC)at an early stage is vital for long-term survival.Liquid biopsy has emerged as a promising new avenue for non-invasive screening of CRC as well as prognostication and surveillance of minimal residual disease.Cell free DNA(cfDNA)is a promising liquid biopsy analyte and has been approved for use in clinical practice.Here,we explore the current challenges of utilizing cfDNA in the screening and prognostication of CRC but also for detecting driver mutations in healthy,presymptomatic patients with normal colonic crypts.CfDNA for the detection of cancerous or premalignant colonic lesions has already been extensively explored,however few have considered utilizing cfDNA in the detection of driver mutations in healthy patients.Theoretically,this would allow us to detect patients who are at a higher risk of tumorigenesis decades in advance of established malignancy and stratify them into higher risk groups for early-intervention screening programs.We also explore the solutions necessary to overcome the challenges that prevent liquid biopsy from entering mainstream clinical use.The potential for liquid biopsy is immense if these challenges are successfully circumvented,and can dramatically reduce CRC rates as well as improve survival in patients.
基金supported by the Shanghai Municipal Health Commission(201840063,201801075)the Science and Technology Commission of Shanghai Municipality(18441903300).
文摘Background:Spontaneous intracerebral hemorrhage(ICH)is a severe neurological emergency with high morbidity and mortality.The effectiveness of surgical intervention remains controversial,partly due to significant heterogeneity among patients.Traditional clinical criteria often fail to identify those most likely to benefit from surgery.Methods:This nationwide retrospective study in China included 2,167 ICH patients from 31 hospitals.Using machine learning techniques,we integrated clinical and radiomic data to perform unsupervised clustering and identify distinct phenogroups.Dimensionality reduction and cross-validation were applied to minimize overfitting.External validation was conducted using data from the INTERACT3 trial,and a prospective cohort was used to assess real-world applicability.Results:Three phenogroups were identified.Among them,only Phenogroup 1-characterized by older age,moderate hematoma volume,and intermediate Glasgow Coma Scale scores-showed significant benefit from early surgical intervention,with a 42%reduction in 3-month mortality and improved functional outcomes.In contrast,surgery did not significantly affect outcomes in Phenogroups 0 and 2.These findings were consistent across multiple machine learning models and validated externally.Conclusion:Machine learning-driven phenotypic stratification can effectively identify ICH patients who are most likely to benefit from surgical treatment.This approach supports personalized treatment strategies and may improve clinical decision-making in ICH management.Further validation in diverse populations is warranted.
文摘Perioperative morbidity of esophagectomy significantly affects the surgical outcome,like any major gastrointestinal procedure.Despite introduction of minimally invasive esophagectomy,the morbidity is still close to 30%-40%.The common complications following esophagectomy are pulmonary infections,cardiac events,anastomotic leakage,bleeding,chylous leak,and recurrent laryngeal nerve palsy which in turn lead to longer hospital stay,increased treatment cost and poor quality of life.A nomographic model comprising preoperative(patient,disease and treatment related)and intraoperative factors in combination with Artificial Intelligence may accurately identify the patients at higher risk of morbidity.This will aid in optimizing the modifiable risk factors preoperatively,and closely monitor these patients post operatively for early identification of complications and to initiate early corrective measures to improve the surgical outcome.
基金Supported by National Natural Science Foundation of China,No.81874390 and No.81573948Shanghai Natural Science Foundation,No.21ZR1464100+1 种基金Science and Technology Innovation Action Plan of Shanghai Science and Technology Commission,No.22S11901700the Shanghai Key Specialty of Traditional Chinese Clinical Medicine,No.shslczdzk01201.
文摘BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.
基金Supported by Gansu Province Joint Fund General Program,No.24JRRA878Gansu Provincial Science and Technology Program Project,No.24JRRA1020+2 种基金Gansu Province Key Talent Program,No.2025RCXM006Teaching Research and Reform Program for Postgraduate Education at Gansu University of Traditional Chinese Medicine(GUSTCM),No.YBXM-202406Special Fund for Mentors of“Qihuang Talents”in the First-Level Discipline of Chinese Medicine,No.ZYXKBD-202415。
文摘BACKGROUND Emerging evidence implicates Candida albicans(C.albicans)in human oncogenesis.Notably,studies have supported its involvement in regulating outcomes in colorectal cancer(CRC).This study investigated the paradoxical role of C.albicans in CRC,aiming to determine whether it promotes or suppresses tumor development,with a focus on the mechanistic basis linked to its metabolic profile.AIM To investigate the dual role of C.albicans in the development and progression of CRC through metabolite profiling and to establish a prognostic model that integrates the microbial and metabolic interactions in CRC,providing insights into potential therapeutic strategies and clinical outcomes.METHODSA prognostic model integrating C. albicans with CRC was developed, incorporating enrichment analysis, immuneinfiltration profiling, survival analysis, Mendelian randomization, single-cell sequencing, and spatial transcriptomics.The effects of the C. albicans metabolite mixture on CRC cells were subsequently validated in vitro. Theprimary metabolite composition was characterized using liquid chromatography-mass spectrometry.RESULTSA prognostic model based on five specific mRNA markers, EHD4, LIME1, GADD45B, TIMP1, and FDFT1, wasestablished. The C. albicans metabolite mixture significantly reduced CRC cell viability. Post-treatment analysisrevealed a significant decrease in gene expression in HT29 cells, while the expression levels of TIMP1, EHD4, andGADD45B were significantly elevated in HCT116 cells. Conversely, LIME1 expression and that of other CRC celllines showed reductions. In normal colonic epithelial cells (NCM460), GADD45B, TIMP1, and FDFT1 expressionlevels were significantly increased, while LIME1 and EHD4 levels were markedly reduced. Following metabolitetreatment, the invasive and migratory capabilities of NCM460, HT29, and HCT116 cells were reduced. Quantitativeanalysis of extracellular ATP post-treatment showed a significant elevation (P < 0.01). The C. albicans metabolitemixture had no effect on reactive oxygen species accumulation in CRC cells but led to a reduction in mitochondrialmembrane potential, increased intracellular lipid peroxidation, and induced apoptosis. Metabolomic profilingrevealed significant alterations, with 516 metabolites upregulated and 531 downregulated.CONCLUSIONThis study introduced a novel prognostic model for CRC risk assessment. The findings suggested that the C.albicans metabolite mixture exerted an inhibitory effect on CRC initiation.
文摘Rectal neuroendocrine tumors(r-NETs)are the second most common type of neuroendocrine tumor in the gastrointestinal tract,with an increase in incidence in the last decades.They are low-grade tumors and,given their low risk of meta-stasis,current guidelines recommend endoscopic resection for small lesions.The GATIS predicting score,proposed by Zeng et al,represents an innovative model designed to predict individualized survival outcomes for patients with r-NETs,analyzing the relationship between clinicopathological features and patient prog-noses.The authors identified tumor grade,T stage,tumor size,age,and progno-stic nutritional index as key prognostic factors,demonstrating that the GATIS Score provides a more accurate prognosis assessment compared to the World Health Organization classification or the tumor-node-metastasis staging system.Nevertheless,further larger prospective studies are necessary,and the scientific community's efforts in this context should be directed toward developing interna-tional multicentric prospective studies,with the ultimate aim of accurately de-fining and understanding the behavior of these conditions.
文摘BACKGROUND Acute-on-chronic liver failure(ACLF)is a liver disease based on chronic liver disease,which is significantly influenced by clinical treatment regimen and disease status,and despite the existence of multiple prognostic assessment indicators for ACLF,the overall sensitivity and accuracy are relatively low.AIM To investigate the prognostic value of the combined detection of alpha-fetoprotein(AFP),plasma prothrombin activity(PTA),and serum prealbumin(PA)in ACLF.METHODS This retrospective study included 87 patients with ACLF admitted from February 2021 to February 2023 and categorized them into the survival(n=47)and death(n=40)groups according to their clinical outcomes 3 months posttreatment.All the participants underwent AFP,PTA,and PA level measurements upon admission.Baseline data,as well as AFP,PTA,and PA levels,were comparatively analyzed.Pearson correlation coefficients were utilized to analyze the correlations of AFP,PTA,and PA with different survival outcomes in patients with ACLF.Receiver operating characteristic(ROC)curves and areas under the curves were used to evaluate the predictive value of AFP,PTA,and PA for ACLF prognosis.RESULTS AFP,PTA,and PA levels were markedly decreased in the death group than in the survival group(P<0.05).Pearson analysis indicated a positive association of the AFP,PTA,and PA levels with the survival of patients with ACLF(P<0.05).ROC curve analysis determined the sensitivity and specificity of the combined diagnosis at 91.24%and 100.00%,respectively,both of which were notably increased compared to the single-index diagnosis.The ROC of their combined diagnosis was 0.989,significantly surpassing 0.907,0.849,and 0.853 of AFP,PTA,and PA,respectively.No statistically significant variance was determined in the sensitivity and specificity of the combined diagnosis vs the single detection(P>0.05).CONCLUSION The combined detection of AFP,PTA,and PA levels demonstrates favorable diagnostic value for the short-term prognosis of patients with ACLF,featuring high sensitivity and specificity.
文摘Cardiovascular diseases(CVDs)remain the leading cause of morbidity and mortality worldwide,necessitating innovative diagnostic and prognostic strategies.Traditional biomarkers like C-reactive protein,uric acid,troponin,and natriuretic peptides play crucial roles in CVD management,yet they are often limited by sensitivity and specificity constraints.This narrative review critically examines the emerging landscape of cardiac biomarkers and advocates for a multiple-marker approach to enhance early detection,prognosis,and risk stratification of CVD.In recent years,several novel biomarkers have shown promise in revolutionizing CVD diagnostics.Gamma-glutamyltransferase,microRNAs,endothelial microparticles,placental growth factor,trimethylamine N-oxide,retinol-binding protein 4,copeptin,heart-type fatty acid-binding protein,galectin-3,growth differentiation factor-15,soluble suppression of tumorigenicity 2,fibroblast growth factor 23,and adrenomedullin have emerged as significant indicators of CV health.These biomarkers provide insights into various pathophysiological processes,such as oxidative stress,endothelial dysfunction,inflammation,metabolic disturbances,and myocardial injury.The integration of these novel biomarkers with traditional ones offers a more comprehensive understanding of CVD mechanisms.This multiple-marker approach can improve diagnostic accuracy,allowing for better risk stratification and more personalized treatment strategies.This review underscores the need for continued research to validate the clinical utility of these biomarkers and their potential incorporation into routine clinical practice.By leveraging the strengths of both traditional and novel biomarkers,precise therapeutic plans can be developed,thereby improving the management and prognosis of patients with CVDs.The ongoing exploration and validation of these biomarkers are crucial for advancing CV care and addressing the limitations of current diagnostic tools.
基金Supported by Natural Science Foundation of Xinjiang Uygur Autonomous Region,No.2022D01C297.
文摘BACKGROUND Anastomotic leakage(AL)is a serious complication following rectal cancer surgery and is associated with increased recurrence,mortality,extended hospital stays,and delayed chemotherapy.The Onodera prognostic nutritional index(OPNI)and inflammation-related biomarkers,such as the neutrophil-lymphocyte ratio(NLR)and platelet-to-lymphocyte ratio(PLR),have been studied in the context of cancer prognosis,but their combined efficacy in predicting AL remains unclear.AIM To investigate the relationships between AL and these markers and developed a predictive model for AL.METHODS A retrospective cohort study analyzed the outcomes of 434 patients who had undergone surgery for rectal cancer at a tertiary cancer center from 2016 to 2023.The patients were divided into two groups on the basis of the occurrence of AL:One group consisted of patients who experienced AL(n=49),and the other group did not(n=385).The investigation applied logistic regression to develop a risk prediction model utilizing clinical,pathological,and laboratory data.The efficacy of this model was then evaluated through receiver operating characteristic curve analysis.RESULTS In the present study,11.28%of the participants(49 out of 434 participants)suffered from AL.Multivariate analysis revealed that preoperative levels of the OPNI,NLR,and PLR emerged as independent risk factors for AL,with odds ratios of 0.705(95%CI:0.641-0.775,P=0.012),1.628(95%CI:1.221-2.172,P=0.024),and 0.994(95%CI:0.989-0.999,P=0.031),respectively.These findings suggest that these biomarkers could effectively predict AL risk.Furthermore,the proposed predictive model has superior discriminative ability,as demonstrated by an area under the curve of 0.910,a sensitivity of 0.898,and a specificity of 0.826,reflecting its high level of accuracy.CONCLUSION The risk of AL in rectal cancer surgery patients can be effectively predicted by assessing the preoperative levels of serum nutritional biomarkers and inflammatory indicators,emphasizing their importance in the preoperative evaluation process.
基金supported by the Research Project of Maternal and Child Health Hospital of Hubei Province(No.2023SFYM008)Key Project of Hubei Provincial Natural Science Foundation(No.JCZRLH202500304).
文摘Objective ZW10 interacting kinetochore protein(ZWINT)has been demonstrated to play a pivotal role in the growth,invasion,and migration of cancers.Nevertheless,whether the expression levels of ZWINT are significantly correlated with clinicopathological characteristics and prognostic outcomes of patients with breast cancer remains elusive.This study systematically investigated the clinical significance of ZWINT expression in breast cancer through integrated molecular subtyping and survival analysis.Methods We systematically characterized the spatial expression pattern of ZWINT across various breast cancer subtypes and assessed its prognostic significance using an integrated bioinformatics approach that involved multi-omics analysis.The approach included the Breast Cancer Gene-Expression Miner v5.1(bc-GenExMiner v5.1),TNMplot,MuTarget,PrognoScan database,and Database for Annotation,Visualization,and Integrated Discovery(DAVID).Results Our analysis revealed consistent upregulation of ZWINT mRNA and protein expression across distinct clinicopathological subtypes of breast cancer.ZWINT overexpression demonstrated significant co-occurrence with truncating mutations in cadherin 1(CDH1)and tumor protein p53(TP53),suggesting potential functional crosstalk in tumor progression pathways.The overexpression of ZWINT correlated with adverse clinical outcomes,showing 48%increased mortality risk(overall survival:HR 1.48,95%CI 1.23–1.79),66%higher recurrence probability(relapse-free survival:1.66,95%CI 1.50–1.84),and 63%elevated metastasis risk(distant metastasis-free survival:HR 1.63,95%CI 1.39–1.90).Multivariate Cox regression incorporating TNM staging and molecular subtypes confirmed ZWINT as an independent prognostic determinant(P<0.001,Harrell’s C-index=0.7827),which was validated through bootstrap resampling(1000 iterations).Conclusion ZWINT may serve as a potential biomarker for prognosis and a possible therapeutic target alongside TP53/CDH1 in breast cancer.
