Microvascular invasion(MVI)is a critical factor in hepatocellular carcinoma(HCC)prognosis,particularly in hepatitis B virus(HBV)-related cases.This editorial examines a recent study by Xu et al who developed models to...Microvascular invasion(MVI)is a critical factor in hepatocellular carcinoma(HCC)prognosis,particularly in hepatitis B virus(HBV)-related cases.This editorial examines a recent study by Xu et al who developed models to predict MVI and high-risk(M2)status in HBV-related HCC using contrast-enhanced computed tomography(CECT)radiomics and clinicoradiological factors.The study analyzed 270 patients,creating models that achieved an area under the curve values of 0.841 and 0.768 for MVI prediction,and 0.865 and 0.798 for M2 status prediction in training and validation datasets,respectively.These results are comparable to previous radiomics-based approaches,which reinforces the potential of this method in MVI prediction.The strengths of the study include its focus on HBV-related HCC and the use of widely accessible CECT imaging.However,limitations,such as retrospective design and manual segmentation,highlight areas for improvement.The editorial discusses the implications of the study including the need for standardized radiomics approaches and the potential impact on personalized treatment strategies.It also suggests future research directions,such as exploring mechanistic links between radiomics features and MVI,as well as integrating additional biomarkers or imaging modalities.Overall,this study contributes significantly to HCC management,paving the way for more accurate,personalized treatment approaches in the era of precision oncology.展开更多
BACKGROUND Tumor deposits(TDs)are an independent predictor of poor prognosis in colorec-tal cancer(CRC)patients.Enhanced follow-up and treatment monitoring for TD+patients may improve survival rates and quality of lif...BACKGROUND Tumor deposits(TDs)are an independent predictor of poor prognosis in colorec-tal cancer(CRC)patients.Enhanced follow-up and treatment monitoring for TD+patients may improve survival rates and quality of life.However,the detection of TDs relies primarily on postoperative pathological examination,which may have a low detection rate due to sampling limitations.AIM To evaluate the spectral computed tomography(CT)parameters of primary tu-mors and the largest regional lymph nodes(LNs),to determine their value in predicting TDs in CRC.METHODS A retrospective analysis was conducted which included 121 patients with CRC whose complete spectral CT data were available.Patients were divided into the TDs+group and the TDs-group on the basis of their pathological results.Spectral CT parameters of the primary CRC lesion and the largest regional LNs were measured,including the normalized iodine concentration(NIC)in both the arte-rial and venous phases,and the LN-to-primary tumor ratio was calculated.Stati-stical methods were used to evaluate the diagnostic efficacy of each spectral para-meter.RESULTS Among the 121 CRC patients,33(27.2%)were confirmed to be TDs+.The risk of TDs positivity was greater in patients with positive LN metastasis,higher N stage and elevated carcinoembryonic antigen and cancer antigen 19-9 levels.The NIC(LNs in both the arterial and venous phases),NIC(primary tumors in the venous phase),and the LN-to-primary tumor ratio in both the arterial and venous phases were associated with TDs(P<0.05).In mul-tivariate logistic regression analysis,the arterial phase LN-to-primary tumor ratio was identified as an independent predictor of TDs,demonstrating the highest diagnostic performance(area under the curve:0.812,sensitivity:0.879,specificity:0.648,cutoff value:1.145).CONCLUSION The spectral CT parameters of the primary colorectal tumor and the largest regional LNs,especially the LN-to-primary tumor ratio,have significant clinical value in predicting TDs in CRC.展开更多
AlphaFold[1]has turned everyone into a structural biologist.No need for knowledge of Fourier transforms or spectral density,driven by artificial intelligence(AI),all one needs to do is enter the primary structure of a...AlphaFold[1]has turned everyone into a structural biologist.No need for knowledge of Fourier transforms or spectral density,driven by artificial intelligence(AI),all one needs to do is enter the primary structure of a folded protein,and out pops a tertiary structure nearly as good as one from an experiment-based structure.展开更多
Permeable roads generally exhibit inferior mechanical properties and shorter service life than traditional dense-graded/impermeable roads.Furthermore,the incorporation of recycled aggregates in their construction may ...Permeable roads generally exhibit inferior mechanical properties and shorter service life than traditional dense-graded/impermeable roads.Furthermore,the incorporation of recycled aggregates in their construction may exacerbate these limitations.To address these issues,this study introduced a novel cement-stabilized permeable recycled aggregate material.A total of 162 beam specimens prepared with nine different levels of cement-aggregate ratio were tested to evaluate their permeability,bending load,and bending fatigue life.The experimental results indicate that increasing the content of recycled aggregates led to a reduction in both permeability and bending load.Additionally,the inclusion of recycled aggregates diminished the energy dissipation capacity of the specimens.These findings were used to establish a robust relationship between the initial damage in cement-stabilized permeable recycled aggregate material specimens and their fatigue life,and to propose a predictive model for their fatigue performance.Further,a method for assessing fatigue damage based on the evolution of fatigue-induced strain and energy dissipation was developed.The findings of this study provide valuable insights into the mechanical behavior and fatigue performance of cement-stabilized permeable recycled aggregate materials,offering guidance for the design of low-carbon-emission,permeable,and durable roadways incorporating recycled aggregates.展开更多
In most agricultural areas in the semi-arid region of the southern United States, wheat (Triticum aestivum L.) production is a primary economic activity. This region is drought-prone and projected to have a drier clim...In most agricultural areas in the semi-arid region of the southern United States, wheat (Triticum aestivum L.) production is a primary economic activity. This region is drought-prone and projected to have a drier climate in the future. Predicting the yield loss due to an anticipated drought is crucial for wheat growers. A reliable way for predicting the drought-induced yield loss is to use a plant physiology-based drought index, such as Agricultural Reference Index for Drought (ARID). Since different wheat cultivars exhibit varying levels of sensitivity to water stress, the impact of drought could be different on the cultivars belonging to different drought sensitivity groups. The objective of this study was to develop the cultivar drought sensitivity (CDS) group-specific, ARID-based models for predicting the drought-induced yield loss of winter wheat in the Llano Estacado region in the southern United States by accounting for the phenological phase-specific sensitivity to drought. For the study, the historical (1947-2021) winter wheat grain yield and daily weather data of two locations in the region (Bushland, TX and Clovis, NM) were used. The logical values of the drought sensitivity parameters of the yield models, especially for the moderately-sensitive and highly-sensitive CDS groups, indicated that the yield models reflected the phenomenon of water stress decreasing the winter wheat yields in this region satisfactorily. The reasonable values of the Nash-Sutcliffe Index (0.65 and 0.72), the Willmott Index (0.88 and 0.92), and the percentage error (23 and 22) for the moderately-sensitive and highly-sensitive CDS groups, respectively, indicated that the yield models for these groups performed reasonably well. These models could be useful for predicting the drought-induced yield losses and scheduling irrigation allocation based on the phenological phase-specific drought sensitivity as influenced by cultivar genotype.展开更多
Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down,and the drug products are accumulated in living organisms.The ability of a drug to dissolve ...Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down,and the drug products are accumulated in living organisms.The ability of a drug to dissolve in water(i.e.,LogS)is an important parameter for assessing a drug’s environmental fate,biovailability,and toxicity.LogS is typically measured in a laboratory setting,which can be costly and time-consuming,and does not provide the opportunity to conduct large-scale analyses.This research develops and evaluates machine learning models that can produce LogS estimates and may improve the environmental risk assessments of toxic pharmaceutical pollutants.We used a dataset from the ChEMBL database that contained 8832 molecular compounds.Various data preprocessing and cleaning techniques were applied(i.e.,removing the missing values),we then recorded chemical properties by normalizing and,even,using some feature selection techniques.We evaluated logS with a total of several machine learning and deep learning models,including;linear regression,random forests(RF),support vector machines(SVM),gradient boosting(GBM),and artificial neural networks(ANNs).We assessed model performance using a series of metrics,including root mean square error(RMSE)and mean absolute error(MAE),as well as the coefficient of determination(R^(2)).The findings show that the Least Angle Regression(LAR)model performed the best with an R^(2) value close to 1.0000,confirming high predictive accuracy.The OMP model performed well with good accuracy(R^(2)=0.8727)while remaining computationally cheap,while other models(e.g.,neural networks,random forests)performed well but were too computationally expensive.Finally,to assess the robustness of the results,an error analysis indicated that residuals were evenly distributed around zero,confirming the results from the LAR model.The current research illustrates the potential of AI in anticipating drug solubility,providing support for green pharmaceutical design and environmental risk assessment.Future work should extend predictions to include degradation and toxicity to enhance predictive power and applicability.展开更多
BACKGROUND The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China,with the disease's intricate and varied characteristics further amplifying its health impact.Precise fore...BACKGROUND The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China,with the disease's intricate and varied characteristics further amplifying its health impact.Precise forecasting of overall survival(OS)is of paramount importance for the clinical management of individuals afflicted with this malignancy.AIM To develop and validate a nomogram model that provides precise gastric cancer prevention and treatment guidance and more accurate survival outcome prediction for patients with gastric carcinoma.METHODS Data analysis was conducted on samples collected from hospitalized gastric cancer patients between 2018 and 2020.Least absolute shrinkage and selection operator,univariate,and multivariate Cox regression analyses were employed to identify independent prognostic factors.A nomogram model was developed to predict gastric cancer patient outcomes.The model's predictability and discriminative ability were evaluated via receiver operating characteristic curves.To evaluate the clinical utility of the model,Kaplan-Meier and decision curve analyses were performed.RESULTS A total of ten independent prognostic factors were identified,including body mass index,tumor-node-metastasis(TNM)stage,radiation,chemotherapy,surgery,albumin,globulin,neutrophil count,lactate dehydrogenase,and platelet-to-lymphocyte ratio.The area under the curve(AUC)values for the 1-,3-,and 5-year survival prediction in the training set were 0.843,0.850,and 0.821,respectively.The AUC values were 0.864,0.820,and 0.786 for the 1-,3-,and 5-year survival prediction in the validation set,respectively.The model exhibited strong discriminative ability,with both the time AUC and time C-index exceeding 0.75.Compared with TNM staging,the model demonstrated superior clinical utility.Ultimately,a nomogram was developed via a web-based interface.CONCLUSION This study established and validated a novel nomogram model for predicting the OS of gastric cancer patients,which demonstrated strong predictive ability.Based on these findings,this model can aid clinicians in implementing personalized interventions for patients with gastric cancer.展开更多
Due to its synergistic effects and reduced side effects,combination therapy has become an important strategy for treating complex diseases.In traditional Chinese medicine(TCM),the“monarch,minister,assistant,envoy”co...Due to its synergistic effects and reduced side effects,combination therapy has become an important strategy for treating complex diseases.In traditional Chinese medicine(TCM),the“monarch,minister,assistant,envoy”compatibilities theory provides a systematic framework for drug compatibility and has guided the formation of a large number of classic formulas.However,due to the complex compositions and diverse mechanisms of action of TCM,it is difficult to comprehensively reveal its potential synergistic patterns using traditional methods.Synergistic prediction based on molecular compatibility theory provides new ideas for identifying combinations of active compounds in TCM.Compared to resource-intensive traditional experimental methods,artificial intelligence possesses the ability to mine synergistic patterns from multi-omics and structural data,providing an efficient means for modeling and optimizing TCM combinations.This paper systematically reviews the application progress of AI in the synergistic prediction of TCM active compounds and explores the challenges and prospects of its application in modeling combination relationships,thereby contributing to the modernization of TCM theory and methodological innovation.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)surveillance is crucial for patients with compensated cirrhosis(CC)and decompensated cirrhosis(DC).Increasing evidence has revealed a connection between thyroid hormone(TH)and H...BACKGROUND Hepatocellular carcinoma(HCC)surveillance is crucial for patients with compensated cirrhosis(CC)and decompensated cirrhosis(DC).Increasing evidence has revealed a connection between thyroid hormone(TH)and HCC,although this relationship remains contentious.Complements and immunoglobulin(Ig),which serve as surrogates of cirrhosis-associated immune dysfunc-tion,are associated with the severity and outcomes of liver cirrhosis(LC).To date,there is a lack of evidence supporting the recommendation of TH,Ig,and com-plement tests in patients at high risk of HCC.AIM To assess the predictive value of TH,Ig,and complements for HCC development.METHODS Data from 142 patients,comprising 72 patients with CC and 70 patients with DC,were analysed as a training set.Among them,100 patients who underwent complement and Ig tests were considered for internal validation.Logistic regression was employed to identify independent risk factors for HCC development.RESULTS The median follow-up duration was 32(24-37 months)months.The incidence of HCC was significantly higher in the DC group(16/70,22.9%)compared to the CC group(3/72,4.2%)(χ^(2)=10.698,P<0.01).Patients with DC exhibited lower total tetraiodothyronine(TT4),total triiodothyronine(TT3),free triiodothyronine,complement C3,and C4(all P<0.01),and higher IgA and IgG(both P<0.01).In both CC and DC patients,TT3 and TT4 positively correlated with alanine transaminase(ALT),aspartate transaminase(AST),and gamma-glutamyl transpeptidase(GGT).IgG positively correlated with IgM,IgA,ALT,and AST,while it negatively correlated with C3 and C4.Multivariable analysis indicated that age,DC status,and GGT were independent risk factors for HCC development.CONCLUSION The predictive value of TH,Ig,and complements for HCC development is suboptimal.Age,DC,and GGT emerge as more significant factors during HCC surveillance in hepatitis B virus-related LC.展开更多
Predicting the productivity of multistage fractured horizontal wells plays an important role in exploiting unconventional resources.In recent years,machine learning(ML)models have emerged as a new approach for such st...Predicting the productivity of multistage fractured horizontal wells plays an important role in exploiting unconventional resources.In recent years,machine learning(ML)models have emerged as a new approach for such studies.However,the scarcity of sufficient real data for model training often leads to imprecise predictions,even though the models trained with real data better characterize geological and engineering features.To tackle this issue,we propose an ML model that can obtain reliable results even with a small amount of data samples.Our model integrates the synthetic minority oversampling technique(SMOTE)to expand the data volume,the support vector machine(SVM)for model training,and the particle swarm optimization(PSO)algorithm for optimizing hyperparameters.To enhance the model performance,we conduct feature fusion and dimensionality reduction.Additionally,we examine the influences of different sample sizes and ML models for training.The proposed model demonstrates higher prediction accuracy and generalization ability,achieving a predicted R^(2)value of up to 0.9 for the test set,compared to the traditional ML techniques with an R^(2)of 0.13.This model accurately predicts the production of fractured horizontal wells even with limited samples,supplying an efficient tool for optimizing the production of unconventional resources.Importantly,the model holds the potential applicability to address similar challenges in other fields constrained by scarce data samples.展开更多
BACKGROUND Few studies have specifically modeled the risk of venous thromboembolism(VTE)for postoperative hepatocellular carcinoma(HCC)patients,although HCC is the third leading cause of cancer death worldwide.This st...BACKGROUND Few studies have specifically modeled the risk of venous thromboembolism(VTE)for postoperative hepatocellular carcinoma(HCC)patients,although HCC is the third leading cause of cancer death worldwide.This study aimed to develop and validate a nomogram that accurately predicts the risk of VTE in patients after HCC surgery.AIM To develop and validate a nomogram to accurately predict the risk of VTE in postoperative HCC patients by integrating clinical and laboratory risk factors.The model seeks to provide a user-friendly tool for identifying high-risk individuals who may benefit from targeted anticoagulation therapy,thereby improving clinical decision-making and patient outcomes.METHODS Data from patients who underwent HCC surgery at Chongqing University Cancer Hospital in China were analyzed.Through univariate and multivariate logistic regression analyses,independent risk factors for VTE were identified and integrated into a nomogram.The predictive performance of the nomogram was assessed via receiver operating characteristic curves,calibration curves,decision curve analysis and other relevant metrics.RESULTS Of 905 postoperative HCC patients were included in the study.The nomogram incorporated eight independent risk factors for VTE:Karnofsky Performance Scale,base disease,cancer stage(tumor-node-metastasis),chemotherapy,D-dimer concentration,white blood cell count,hemoglobin,and fibrinogen.The C-index for the nomogram model was 0.825 in the training cohort and 0.820 in the validation cohort,indicating good discriminative ability.Calibration plots of the model revealed high concordance between the predicted probabilities and observed outcomes.CONCLUSION We developed and validated a novel nomogram that can accurately estimate the risk of VTE in individual postoperative HCC patients.This model can identify high-risk patients who may benefit from targeted anticoagulation therapy.展开更多
High dropout rates in short-term job skills training programs hinder workforce development.This study applies machine learning to predict program completion while addressing class imbalance challenges.A dataset of6548...High dropout rates in short-term job skills training programs hinder workforce development.This study applies machine learning to predict program completion while addressing class imbalance challenges.A dataset of6548 records with 24 demographic,educational,program-specific,and employment-related features was analyzed.Data preprocessing involved cleaning,encoding categorical variables,and balancing the dataset using the Synthetic Minority Oversampling Technique(SMOTE),as only 15.9% of participants were dropouts.six machine learning models-Logistic Regression,Random Forest,SupportVector Machine,K-Nearest Neighbors,Naive Bayes,and XGBoost-were evaluated on both balanced and unbalanced datasets using an 80-20 train-test split.Performance was assessed using Accuracy,Precision,Recall,F1-score,and ROC-AUC.XGBoost achieved the highest performance on the balanced dataset,with an F1-score of 0.9200 and aROC-AUC of0.9684,followed by Random Forest.These findings highlight the potential of machine learning for early identification of dropout trainees,aiding in retention strategies for workforce training.The results support the integration of predictive analytics to optimize intervention efforts in short-term training programs.展开更多
Objective To develop an onset risk prediction nomogram for patients with homocysteine-type(H-type)hypertension(HTH)based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH.Methods Pat...Objective To develop an onset risk prediction nomogram for patients with homocysteine-type(H-type)hypertension(HTH)based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH.Methods Patients diagnosed with essential hypertension and admitted to Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Shang-hai Hospital of Traditional Chinese Medicine,and Shanghai Hospital of Integrated Tradition-al Chinese and Western Medicine from July 6th 2020 to June 16th 2021,and from August 11th 2023 to January 22nd 2024,were enrolled in this retrospective research.The baselines and clinical biochemical indicators of patients were collected.The SMART-I TCM pulse instru-ment was applied to gather pulse diagram parameters.Multivariate logistic regression was adopted to analyze the risk factors for HTH.RStudio was employed to construct the nomo-gram model,receiver operating characteristic(ROC)curve,and calibration curve(bootstrap self-sampling 200 times),and clinical decision curve were drawn to evaluate the model’s dis-crimination and clinical effectiveness.Results A total of 168 hospitalized patients with essential hypertension were selected and di-vided into non-HTH group(n=29)and HTH group(n=139).Compared with non-HTH group,HTH group had a lower body mass index(BMI),and higher proportions of male pa-tients and drinkers(P<0.05).The ventricular wall thickening(VWT)could not be deter-mined.The proportions of left common carotid intima-media wall thickness(LCCIMWT)and serum creatinine(SCR)were higher in HTH group(P<0.05).The pulse diagram parameter As was significantly higher,and H4/H1 and T1/T were lower in HTH group(P<0.05).Gender,al-cohol consumption,serum creatinine,and the pulse diagram parameter H4/H1 were identi-fied as independent risk factors for HTH(P<0.05).The nomogram’s area under the ROC curve(AUC)was 0.795[95%confidence interval(CI):(0.7066,0.8828)],with a specificity of 0.724 and sensitivity of 0.799.After 200 times repeated bootstrap self-samplings,the calibra-tion curve showed that the simulated curve fits well with the actual curve(x^(2)=9.5002,P=0.3019).The clinical decision curve indicated that the nomogram’s applicability was optimal when the threshold for predicting HTH was between 0.38 and 1.00.Conclusion The nomogram model could be valuable for predicting the onset risk of HTH and pulse diagram parameters can facilitate early screening and prevention of HTH.展开更多
Prediction of weaning success from invasive mechanical ventilation remains a challenge in everyday clinical practice.Several prediction scores have been developed to guide success during spontaneous breathing trials t...Prediction of weaning success from invasive mechanical ventilation remains a challenge in everyday clinical practice.Several prediction scores have been developed to guide success during spontaneous breathing trials to help with weaning decisions.These scores aim to provide a structured framework to support clinical judgment.However,their effectiveness varies across patient populations,and their predictive accuracy remains inconsistent.In this review,we aim to identify the strengths and limitations of commonly used clinical prediction tools in assessing readiness for ventilator liberation.While scores such as the Rapid Shallow Breathing Index and the Integrative Weaning Index are widely adopted,their sensitivity and specificity often fall short in complex clinical settings.Factors such as underlying disease pathophysiology,patient characteristics,and clinician subjectivity impact score performance and reliability.Moreover,disparities in validation across diverse populations limit generalizability.With growing interest in artificial intelligence(AI)and machine learning,there is potential for enhanced prediction models that integrate multidimensional data and adapt to individual patient profiles.However,current AI approaches face challenges related to interpretability,bias,and ethical implementation.This paper underscores the need for more robust,individualized,and transparent prediction systems and advocates for careful integration of emerging technologies into clinical workflows to optimize weaning success and patient outcomes.展开更多
Predicting cross-immunity between viral strains is vital for public health surveillance and vaccine development.Traditional neural network methods,such as BiLSTM,could be ineffective due to the lack of lab data for mo...Predicting cross-immunity between viral strains is vital for public health surveillance and vaccine development.Traditional neural network methods,such as BiLSTM,could be ineffective due to the lack of lab data for model training and the overshadowing of crucial features within sequence concatenation.The current work proposes a less data-consuming model incorporating a pre-trained gene sequence model and a mutual information inference operator.Our methodology utilizes gene alignment and deduplication algorithms to preprocess gene sequences,enhancing the model’s capacity to discern and focus on distinctions among input gene pairs.The model,i.e.,DNA Pretrained Cross-Immunity Protection Inference model(DPCIPI),outperforms state-of-theart(SOTA)models in predicting hemagglutination inhibition titer from influenza viral gene sequences only.Improvement in binary cross-immunity prediction is 1.58%in F1,2.34%in precision,1.57%in recall,and 1.57%in Accuracy.For multilevel cross-immunity improvements,the improvement is 2.12%in F1,3.50%in precision,2.19%in recall,and 2.19%in Accuracy.Our study showcases the potential of pre-trained gene models to improve predictions of antigenic variation and cross-immunity.With expanding gene data and advancements in pre-trained models,this approach promises significant impacts on vaccine development and public health.展开更多
BACKGROUND Gastric cancer(GC)has a poor prognosis,and the accurate prediction of patient survival remains a significant challenge in oncology.Machine learning(ML)has emerged as a promising tool for survival prediction...BACKGROUND Gastric cancer(GC)has a poor prognosis,and the accurate prediction of patient survival remains a significant challenge in oncology.Machine learning(ML)has emerged as a promising tool for survival prediction,though concerns regarding model interpretability,reliance on retrospective data,and variability in performance persist.AIM To evaluate ML applications in predicting GC survival and to highlight key limitations in current methods.METHODS A comprehensive search of PubMed and Web of Science in November 2024 identified 16 relevant studies published after 2019.The most frequently used ML models were deep learning(37.5%),random forests(37.5%),support vector machines(31.25%),and ensemble methods(18.75%).The dataset sizes varied from 134 to 14177 patients,with nine studies incorporating external validation.RESULTS The reported area under the curve values were 0.669–0.980 for overall survival,0.920–0.960 for cancer-specific survival,and 0.710–0.856 for disease-free survival.These results highlight the potential of ML-based models to improve clinical practice by enabling personalized treatment planning and risk stratification.CONCLUSION Despite challenges concerning retrospective studies and a lack of interpretability,ML models show promise;prospective trials and multidimensional data integration are recommended for improving their clinical applicability.展开更多
Colorectal cancer(CRC)is a prevalent malignancy,with surgery playing a key role in its treatment.However,perioperative complications,such as anastomotic leaks,infections,and mortality,can significantly affect surgical...Colorectal cancer(CRC)is a prevalent malignancy,with surgery playing a key role in its treatment.However,perioperative complications,such as anastomotic leaks,infections,and mortality,can significantly affect surgical outcomes,extend hospital stays,and increase healthcare costs.Traditional risk prediction models often lack precision,leading to increased interest in artificial intelligence(AI)for improving risk stratification.This review examines the application of AI,particularly machine learning and deep learning,in predicting perioperative complications in CRC surgery.AI models have been employed to predict a variety of postoperative complications,including readmissions,surgical-site infections,anastomotic leakage,and mortality,by analyzing diverse data sources such as electronic health records,medical imaging,and preoperative markers.Despite the promising results,several challenges remain,including data quality,model generalizability,the complexity of clinical data,and ethical and regulatory concerns.The review emphasizes the need for multicenter,diverse datasets and the integration of AI into clinical workflows to improve model performance and adoption.Future efforts should focus on enhancing the transparency and interpretability of AI models to ensure their successful implementation in clinical practice,ultimately improving patient outcomes and surgical decision-making in CRC surgery.展开更多
The machine learning model developed by Shi et al for predicting colorectal polyp recurrence after endoscopic mucosal resection represents a significant advancement in the field of clinical gastroenterology.By integra...The machine learning model developed by Shi et al for predicting colorectal polyp recurrence after endoscopic mucosal resection represents a significant advancement in the field of clinical gastroenterology.By integrating patient-specific factors,such as age,smoking history,and Helicobacter pylori infection,the eXtreme Gradient Boosting algorithm enables precise personalised colonoscopy follow-up planning and risk assessment.This predictive tool offers substantial benefits by optimising surveillance intervals and directing healthcare resources more efficiently toward high-risk individuals.However,real-world implementation requires consideration of the generalisability of our findings across diverse patient populations and clinician training backgrounds.展开更多
In this article,we discuss the study by Cheng et al,published in the World Journal of Gastroenterology,focusing on predictive methods for post-hepatectomy liver failure(PHLF).PHLF is a common and serious complication,...In this article,we discuss the study by Cheng et al,published in the World Journal of Gastroenterology,focusing on predictive methods for post-hepatectomy liver failure(PHLF).PHLF is a common and serious complication,and accurate prediction is critical for clinical management.The study examines the potential of ultrasound elastography and splenic size in predicting PHLF.Ultrasound elastography reflects liver functional reserve,while splenic size provides additional predictive value.By integrating these factors with serological markers,we developed a comprehensive prediction model that effectively stratifies patient risk and supports personalized clinical decisions.This approach offers new insights into predicting PHLF.These methods not only assist clinicians in identifying high-risk patients earlier but also provide scientific support for personalized treatment strategies.Future research will aim to validate the model's accuracy with larger sample sizes,further enhancing the clinical application of these non-invasive indicators.展开更多
Higher education institutions are becoming increasingly concerned with the retention of their students.This work is motivated by the interest in predicting and reducing student dropout,and consequently in reducing the...Higher education institutions are becoming increasingly concerned with the retention of their students.This work is motivated by the interest in predicting and reducing student dropout,and consequently in reducing the financial losses of said institutions.Based on the characterization of the dropout problem and the application of a knowledge discovery process,an ensemble model is proposed to improve dropout prediction.The ensemble model combines the results of three models:logistic regression,neural networks,and decision tree.As a result,the model can correctly classify 89%of the students as enrolled or dropped and accurately identify 98.1%of dropouts.When compared with the Random Forest ensemble method,the proposed model demonstrates desirable characteristics to assist management in proposing actions to retain students.展开更多
文摘Microvascular invasion(MVI)is a critical factor in hepatocellular carcinoma(HCC)prognosis,particularly in hepatitis B virus(HBV)-related cases.This editorial examines a recent study by Xu et al who developed models to predict MVI and high-risk(M2)status in HBV-related HCC using contrast-enhanced computed tomography(CECT)radiomics and clinicoradiological factors.The study analyzed 270 patients,creating models that achieved an area under the curve values of 0.841 and 0.768 for MVI prediction,and 0.865 and 0.798 for M2 status prediction in training and validation datasets,respectively.These results are comparable to previous radiomics-based approaches,which reinforces the potential of this method in MVI prediction.The strengths of the study include its focus on HBV-related HCC and the use of widely accessible CECT imaging.However,limitations,such as retrospective design and manual segmentation,highlight areas for improvement.The editorial discusses the implications of the study including the need for standardized radiomics approaches and the potential impact on personalized treatment strategies.It also suggests future research directions,such as exploring mechanistic links between radiomics features and MVI,as well as integrating additional biomarkers or imaging modalities.Overall,this study contributes significantly to HCC management,paving the way for more accurate,personalized treatment approaches in the era of precision oncology.
文摘BACKGROUND Tumor deposits(TDs)are an independent predictor of poor prognosis in colorec-tal cancer(CRC)patients.Enhanced follow-up and treatment monitoring for TD+patients may improve survival rates and quality of life.However,the detection of TDs relies primarily on postoperative pathological examination,which may have a low detection rate due to sampling limitations.AIM To evaluate the spectral computed tomography(CT)parameters of primary tu-mors and the largest regional lymph nodes(LNs),to determine their value in predicting TDs in CRC.METHODS A retrospective analysis was conducted which included 121 patients with CRC whose complete spectral CT data were available.Patients were divided into the TDs+group and the TDs-group on the basis of their pathological results.Spectral CT parameters of the primary CRC lesion and the largest regional LNs were measured,including the normalized iodine concentration(NIC)in both the arte-rial and venous phases,and the LN-to-primary tumor ratio was calculated.Stati-stical methods were used to evaluate the diagnostic efficacy of each spectral para-meter.RESULTS Among the 121 CRC patients,33(27.2%)were confirmed to be TDs+.The risk of TDs positivity was greater in patients with positive LN metastasis,higher N stage and elevated carcinoembryonic antigen and cancer antigen 19-9 levels.The NIC(LNs in both the arterial and venous phases),NIC(primary tumors in the venous phase),and the LN-to-primary tumor ratio in both the arterial and venous phases were associated with TDs(P<0.05).In mul-tivariate logistic regression analysis,the arterial phase LN-to-primary tumor ratio was identified as an independent predictor of TDs,demonstrating the highest diagnostic performance(area under the curve:0.812,sensitivity:0.879,specificity:0.648,cutoff value:1.145).CONCLUSION The spectral CT parameters of the primary colorectal tumor and the largest regional LNs,especially the LN-to-primary tumor ratio,have significant clinical value in predicting TDs in CRC.
基金supported by the U.S.National Natural Science Foundation(CHE-2203505 and MCB-2335137).
文摘AlphaFold[1]has turned everyone into a structural biologist.No need for knowledge of Fourier transforms or spectral density,driven by artificial intelligence(AI),all one needs to do is enter the primary structure of a folded protein,and out pops a tertiary structure nearly as good as one from an experiment-based structure.
基金Project(2024JJ2073)supported by the Science Fund for Distinguished Young Scholars of Hunan Province,ChinaProjects(2023YFC3807205,2019YFC1904704)+4 种基金supported by the National Key R&D Program of ChinaProject(52178443)supported by the National Natural Science Foundation of ChinaProject(2024ZZTS0109)supported by Fundamental Research Funds for the Central Universities of Central South University,China。
文摘Permeable roads generally exhibit inferior mechanical properties and shorter service life than traditional dense-graded/impermeable roads.Furthermore,the incorporation of recycled aggregates in their construction may exacerbate these limitations.To address these issues,this study introduced a novel cement-stabilized permeable recycled aggregate material.A total of 162 beam specimens prepared with nine different levels of cement-aggregate ratio were tested to evaluate their permeability,bending load,and bending fatigue life.The experimental results indicate that increasing the content of recycled aggregates led to a reduction in both permeability and bending load.Additionally,the inclusion of recycled aggregates diminished the energy dissipation capacity of the specimens.These findings were used to establish a robust relationship between the initial damage in cement-stabilized permeable recycled aggregate material specimens and their fatigue life,and to propose a predictive model for their fatigue performance.Further,a method for assessing fatigue damage based on the evolution of fatigue-induced strain and energy dissipation was developed.The findings of this study provide valuable insights into the mechanical behavior and fatigue performance of cement-stabilized permeable recycled aggregate materials,offering guidance for the design of low-carbon-emission,permeable,and durable roadways incorporating recycled aggregates.
文摘In most agricultural areas in the semi-arid region of the southern United States, wheat (Triticum aestivum L.) production is a primary economic activity. This region is drought-prone and projected to have a drier climate in the future. Predicting the yield loss due to an anticipated drought is crucial for wheat growers. A reliable way for predicting the drought-induced yield loss is to use a plant physiology-based drought index, such as Agricultural Reference Index for Drought (ARID). Since different wheat cultivars exhibit varying levels of sensitivity to water stress, the impact of drought could be different on the cultivars belonging to different drought sensitivity groups. The objective of this study was to develop the cultivar drought sensitivity (CDS) group-specific, ARID-based models for predicting the drought-induced yield loss of winter wheat in the Llano Estacado region in the southern United States by accounting for the phenological phase-specific sensitivity to drought. For the study, the historical (1947-2021) winter wheat grain yield and daily weather data of two locations in the region (Bushland, TX and Clovis, NM) were used. The logical values of the drought sensitivity parameters of the yield models, especially for the moderately-sensitive and highly-sensitive CDS groups, indicated that the yield models reflected the phenomenon of water stress decreasing the winter wheat yields in this region satisfactorily. The reasonable values of the Nash-Sutcliffe Index (0.65 and 0.72), the Willmott Index (0.88 and 0.92), and the percentage error (23 and 22) for the moderately-sensitive and highly-sensitive CDS groups, respectively, indicated that the yield models for these groups performed reasonably well. These models could be useful for predicting the drought-induced yield losses and scheduling irrigation allocation based on the phenological phase-specific drought sensitivity as influenced by cultivar genotype.
文摘Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down,and the drug products are accumulated in living organisms.The ability of a drug to dissolve in water(i.e.,LogS)is an important parameter for assessing a drug’s environmental fate,biovailability,and toxicity.LogS is typically measured in a laboratory setting,which can be costly and time-consuming,and does not provide the opportunity to conduct large-scale analyses.This research develops and evaluates machine learning models that can produce LogS estimates and may improve the environmental risk assessments of toxic pharmaceutical pollutants.We used a dataset from the ChEMBL database that contained 8832 molecular compounds.Various data preprocessing and cleaning techniques were applied(i.e.,removing the missing values),we then recorded chemical properties by normalizing and,even,using some feature selection techniques.We evaluated logS with a total of several machine learning and deep learning models,including;linear regression,random forests(RF),support vector machines(SVM),gradient boosting(GBM),and artificial neural networks(ANNs).We assessed model performance using a series of metrics,including root mean square error(RMSE)and mean absolute error(MAE),as well as the coefficient of determination(R^(2)).The findings show that the Least Angle Regression(LAR)model performed the best with an R^(2) value close to 1.0000,confirming high predictive accuracy.The OMP model performed well with good accuracy(R^(2)=0.8727)while remaining computationally cheap,while other models(e.g.,neural networks,random forests)performed well but were too computationally expensive.Finally,to assess the robustness of the results,an error analysis indicated that residuals were evenly distributed around zero,confirming the results from the LAR model.The current research illustrates the potential of AI in anticipating drug solubility,providing support for green pharmaceutical design and environmental risk assessment.Future work should extend predictions to include degradation and toxicity to enhance predictive power and applicability.
文摘BACKGROUND The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China,with the disease's intricate and varied characteristics further amplifying its health impact.Precise forecasting of overall survival(OS)is of paramount importance for the clinical management of individuals afflicted with this malignancy.AIM To develop and validate a nomogram model that provides precise gastric cancer prevention and treatment guidance and more accurate survival outcome prediction for patients with gastric carcinoma.METHODS Data analysis was conducted on samples collected from hospitalized gastric cancer patients between 2018 and 2020.Least absolute shrinkage and selection operator,univariate,and multivariate Cox regression analyses were employed to identify independent prognostic factors.A nomogram model was developed to predict gastric cancer patient outcomes.The model's predictability and discriminative ability were evaluated via receiver operating characteristic curves.To evaluate the clinical utility of the model,Kaplan-Meier and decision curve analyses were performed.RESULTS A total of ten independent prognostic factors were identified,including body mass index,tumor-node-metastasis(TNM)stage,radiation,chemotherapy,surgery,albumin,globulin,neutrophil count,lactate dehydrogenase,and platelet-to-lymphocyte ratio.The area under the curve(AUC)values for the 1-,3-,and 5-year survival prediction in the training set were 0.843,0.850,and 0.821,respectively.The AUC values were 0.864,0.820,and 0.786 for the 1-,3-,and 5-year survival prediction in the validation set,respectively.The model exhibited strong discriminative ability,with both the time AUC and time C-index exceeding 0.75.Compared with TNM staging,the model demonstrated superior clinical utility.Ultimately,a nomogram was developed via a web-based interface.CONCLUSION This study established and validated a novel nomogram model for predicting the OS of gastric cancer patients,which demonstrated strong predictive ability.Based on these findings,this model can aid clinicians in implementing personalized interventions for patients with gastric cancer.
基金supported by the National Key Research and Development Program of China(No.2024YFC3506900)Science and Technology Program of Tianjin(No.24ZXZSSS00460)Special Project for Technological Innovation in New Productive Forces of Modern Chinese Medicines(No.24ZXZKSY00010)。
文摘Due to its synergistic effects and reduced side effects,combination therapy has become an important strategy for treating complex diseases.In traditional Chinese medicine(TCM),the“monarch,minister,assistant,envoy”compatibilities theory provides a systematic framework for drug compatibility and has guided the formation of a large number of classic formulas.However,due to the complex compositions and diverse mechanisms of action of TCM,it is difficult to comprehensively reveal its potential synergistic patterns using traditional methods.Synergistic prediction based on molecular compatibility theory provides new ideas for identifying combinations of active compounds in TCM.Compared to resource-intensive traditional experimental methods,artificial intelligence possesses the ability to mine synergistic patterns from multi-omics and structural data,providing an efficient means for modeling and optimizing TCM combinations.This paper systematically reviews the application progress of AI in the synergistic prediction of TCM active compounds and explores the challenges and prospects of its application in modeling combination relationships,thereby contributing to the modernization of TCM theory and methodological innovation.
基金Supported by The Research Foundation of Jiangsu Province Administration of Traditional Chinese Medicine,No.MS2023088The Science and Technology Project of Changzhou,No.CE20225040+1 种基金The Research Foundation of Nanjing Medical University Changzhou Medical Center,No.CMCC202311Leading Talent of Changzhou“The 14th Five-Year Plan”High-Level Health Talents Training Project,No.2022CZLJ021.
文摘BACKGROUND Hepatocellular carcinoma(HCC)surveillance is crucial for patients with compensated cirrhosis(CC)and decompensated cirrhosis(DC).Increasing evidence has revealed a connection between thyroid hormone(TH)and HCC,although this relationship remains contentious.Complements and immunoglobulin(Ig),which serve as surrogates of cirrhosis-associated immune dysfunc-tion,are associated with the severity and outcomes of liver cirrhosis(LC).To date,there is a lack of evidence supporting the recommendation of TH,Ig,and com-plement tests in patients at high risk of HCC.AIM To assess the predictive value of TH,Ig,and complements for HCC development.METHODS Data from 142 patients,comprising 72 patients with CC and 70 patients with DC,were analysed as a training set.Among them,100 patients who underwent complement and Ig tests were considered for internal validation.Logistic regression was employed to identify independent risk factors for HCC development.RESULTS The median follow-up duration was 32(24-37 months)months.The incidence of HCC was significantly higher in the DC group(16/70,22.9%)compared to the CC group(3/72,4.2%)(χ^(2)=10.698,P<0.01).Patients with DC exhibited lower total tetraiodothyronine(TT4),total triiodothyronine(TT3),free triiodothyronine,complement C3,and C4(all P<0.01),and higher IgA and IgG(both P<0.01).In both CC and DC patients,TT3 and TT4 positively correlated with alanine transaminase(ALT),aspartate transaminase(AST),and gamma-glutamyl transpeptidase(GGT).IgG positively correlated with IgM,IgA,ALT,and AST,while it negatively correlated with C3 and C4.Multivariable analysis indicated that age,DC status,and GGT were independent risk factors for HCC development.CONCLUSION The predictive value of TH,Ig,and complements for HCC development is suboptimal.Age,DC,and GGT emerge as more significant factors during HCC surveillance in hepatitis B virus-related LC.
基金supported by the National Natural Science Foundation of China(52274055)the Shandong Provincial Natural Science Foundation(ZR2022YQ50)the Taishan Scholar Program of Shandong Province(tsqn202408088)。
文摘Predicting the productivity of multistage fractured horizontal wells plays an important role in exploiting unconventional resources.In recent years,machine learning(ML)models have emerged as a new approach for such studies.However,the scarcity of sufficient real data for model training often leads to imprecise predictions,even though the models trained with real data better characterize geological and engineering features.To tackle this issue,we propose an ML model that can obtain reliable results even with a small amount of data samples.Our model integrates the synthetic minority oversampling technique(SMOTE)to expand the data volume,the support vector machine(SVM)for model training,and the particle swarm optimization(PSO)algorithm for optimizing hyperparameters.To enhance the model performance,we conduct feature fusion and dimensionality reduction.Additionally,we examine the influences of different sample sizes and ML models for training.The proposed model demonstrates higher prediction accuracy and generalization ability,achieving a predicted R^(2)value of up to 0.9 for the test set,compared to the traditional ML techniques with an R^(2)of 0.13.This model accurately predicts the production of fractured horizontal wells even with limited samples,supplying an efficient tool for optimizing the production of unconventional resources.Importantly,the model holds the potential applicability to address similar challenges in other fields constrained by scarce data samples.
文摘BACKGROUND Few studies have specifically modeled the risk of venous thromboembolism(VTE)for postoperative hepatocellular carcinoma(HCC)patients,although HCC is the third leading cause of cancer death worldwide.This study aimed to develop and validate a nomogram that accurately predicts the risk of VTE in patients after HCC surgery.AIM To develop and validate a nomogram to accurately predict the risk of VTE in postoperative HCC patients by integrating clinical and laboratory risk factors.The model seeks to provide a user-friendly tool for identifying high-risk individuals who may benefit from targeted anticoagulation therapy,thereby improving clinical decision-making and patient outcomes.METHODS Data from patients who underwent HCC surgery at Chongqing University Cancer Hospital in China were analyzed.Through univariate and multivariate logistic regression analyses,independent risk factors for VTE were identified and integrated into a nomogram.The predictive performance of the nomogram was assessed via receiver operating characteristic curves,calibration curves,decision curve analysis and other relevant metrics.RESULTS Of 905 postoperative HCC patients were included in the study.The nomogram incorporated eight independent risk factors for VTE:Karnofsky Performance Scale,base disease,cancer stage(tumor-node-metastasis),chemotherapy,D-dimer concentration,white blood cell count,hemoglobin,and fibrinogen.The C-index for the nomogram model was 0.825 in the training cohort and 0.820 in the validation cohort,indicating good discriminative ability.Calibration plots of the model revealed high concordance between the predicted probabilities and observed outcomes.CONCLUSION We developed and validated a novel nomogram that can accurately estimate the risk of VTE in individual postoperative HCC patients.This model can identify high-risk patients who may benefit from targeted anticoagulation therapy.
文摘High dropout rates in short-term job skills training programs hinder workforce development.This study applies machine learning to predict program completion while addressing class imbalance challenges.A dataset of6548 records with 24 demographic,educational,program-specific,and employment-related features was analyzed.Data preprocessing involved cleaning,encoding categorical variables,and balancing the dataset using the Synthetic Minority Oversampling Technique(SMOTE),as only 15.9% of participants were dropouts.six machine learning models-Logistic Regression,Random Forest,SupportVector Machine,K-Nearest Neighbors,Naive Bayes,and XGBoost-were evaluated on both balanced and unbalanced datasets using an 80-20 train-test split.Performance was assessed using Accuracy,Precision,Recall,F1-score,and ROC-AUC.XGBoost achieved the highest performance on the balanced dataset,with an F1-score of 0.9200 and aROC-AUC of0.9684,followed by Random Forest.These findings highlight the potential of machine learning for early identification of dropout trainees,aiding in retention strategies for workforce training.The results support the integration of predictive analytics to optimize intervention efforts in short-term training programs.
基金National Natural Science Foundation of China (81973749 and 8143594)State Administration of Traditional Chinese Medicine High-level Chinese Medicine Key Discipline Construction Project (zyyzdxk-2023069)。
文摘Objective To develop an onset risk prediction nomogram for patients with homocysteine-type(H-type)hypertension(HTH)based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH.Methods Patients diagnosed with essential hypertension and admitted to Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Shang-hai Hospital of Traditional Chinese Medicine,and Shanghai Hospital of Integrated Tradition-al Chinese and Western Medicine from July 6th 2020 to June 16th 2021,and from August 11th 2023 to January 22nd 2024,were enrolled in this retrospective research.The baselines and clinical biochemical indicators of patients were collected.The SMART-I TCM pulse instru-ment was applied to gather pulse diagram parameters.Multivariate logistic regression was adopted to analyze the risk factors for HTH.RStudio was employed to construct the nomo-gram model,receiver operating characteristic(ROC)curve,and calibration curve(bootstrap self-sampling 200 times),and clinical decision curve were drawn to evaluate the model’s dis-crimination and clinical effectiveness.Results A total of 168 hospitalized patients with essential hypertension were selected and di-vided into non-HTH group(n=29)and HTH group(n=139).Compared with non-HTH group,HTH group had a lower body mass index(BMI),and higher proportions of male pa-tients and drinkers(P<0.05).The ventricular wall thickening(VWT)could not be deter-mined.The proportions of left common carotid intima-media wall thickness(LCCIMWT)and serum creatinine(SCR)were higher in HTH group(P<0.05).The pulse diagram parameter As was significantly higher,and H4/H1 and T1/T were lower in HTH group(P<0.05).Gender,al-cohol consumption,serum creatinine,and the pulse diagram parameter H4/H1 were identi-fied as independent risk factors for HTH(P<0.05).The nomogram’s area under the ROC curve(AUC)was 0.795[95%confidence interval(CI):(0.7066,0.8828)],with a specificity of 0.724 and sensitivity of 0.799.After 200 times repeated bootstrap self-samplings,the calibra-tion curve showed that the simulated curve fits well with the actual curve(x^(2)=9.5002,P=0.3019).The clinical decision curve indicated that the nomogram’s applicability was optimal when the threshold for predicting HTH was between 0.38 and 1.00.Conclusion The nomogram model could be valuable for predicting the onset risk of HTH and pulse diagram parameters can facilitate early screening and prevention of HTH.
文摘Prediction of weaning success from invasive mechanical ventilation remains a challenge in everyday clinical practice.Several prediction scores have been developed to guide success during spontaneous breathing trials to help with weaning decisions.These scores aim to provide a structured framework to support clinical judgment.However,their effectiveness varies across patient populations,and their predictive accuracy remains inconsistent.In this review,we aim to identify the strengths and limitations of commonly used clinical prediction tools in assessing readiness for ventilator liberation.While scores such as the Rapid Shallow Breathing Index and the Integrative Weaning Index are widely adopted,their sensitivity and specificity often fall short in complex clinical settings.Factors such as underlying disease pathophysiology,patient characteristics,and clinician subjectivity impact score performance and reliability.Moreover,disparities in validation across diverse populations limit generalizability.With growing interest in artificial intelligence(AI)and machine learning,there is potential for enhanced prediction models that integrate multidimensional data and adapt to individual patient profiles.However,current AI approaches face challenges related to interpretability,bias,and ethical implementation.This paper underscores the need for more robust,individualized,and transparent prediction systems and advocates for careful integration of emerging technologies into clinical workflows to optimize weaning success and patient outcomes.
基金supported by the Bill & Melinda Gates Foundation and the Minderoo Foundation
文摘Predicting cross-immunity between viral strains is vital for public health surveillance and vaccine development.Traditional neural network methods,such as BiLSTM,could be ineffective due to the lack of lab data for model training and the overshadowing of crucial features within sequence concatenation.The current work proposes a less data-consuming model incorporating a pre-trained gene sequence model and a mutual information inference operator.Our methodology utilizes gene alignment and deduplication algorithms to preprocess gene sequences,enhancing the model’s capacity to discern and focus on distinctions among input gene pairs.The model,i.e.,DNA Pretrained Cross-Immunity Protection Inference model(DPCIPI),outperforms state-of-theart(SOTA)models in predicting hemagglutination inhibition titer from influenza viral gene sequences only.Improvement in binary cross-immunity prediction is 1.58%in F1,2.34%in precision,1.57%in recall,and 1.57%in Accuracy.For multilevel cross-immunity improvements,the improvement is 2.12%in F1,3.50%in precision,2.19%in recall,and 2.19%in Accuracy.Our study showcases the potential of pre-trained gene models to improve predictions of antigenic variation and cross-immunity.With expanding gene data and advancements in pre-trained models,this approach promises significant impacts on vaccine development and public health.
文摘BACKGROUND Gastric cancer(GC)has a poor prognosis,and the accurate prediction of patient survival remains a significant challenge in oncology.Machine learning(ML)has emerged as a promising tool for survival prediction,though concerns regarding model interpretability,reliance on retrospective data,and variability in performance persist.AIM To evaluate ML applications in predicting GC survival and to highlight key limitations in current methods.METHODS A comprehensive search of PubMed and Web of Science in November 2024 identified 16 relevant studies published after 2019.The most frequently used ML models were deep learning(37.5%),random forests(37.5%),support vector machines(31.25%),and ensemble methods(18.75%).The dataset sizes varied from 134 to 14177 patients,with nine studies incorporating external validation.RESULTS The reported area under the curve values were 0.669–0.980 for overall survival,0.920–0.960 for cancer-specific survival,and 0.710–0.856 for disease-free survival.These results highlight the potential of ML-based models to improve clinical practice by enabling personalized treatment planning and risk stratification.CONCLUSION Despite challenges concerning retrospective studies and a lack of interpretability,ML models show promise;prospective trials and multidimensional data integration are recommended for improving their clinical applicability.
文摘Colorectal cancer(CRC)is a prevalent malignancy,with surgery playing a key role in its treatment.However,perioperative complications,such as anastomotic leaks,infections,and mortality,can significantly affect surgical outcomes,extend hospital stays,and increase healthcare costs.Traditional risk prediction models often lack precision,leading to increased interest in artificial intelligence(AI)for improving risk stratification.This review examines the application of AI,particularly machine learning and deep learning,in predicting perioperative complications in CRC surgery.AI models have been employed to predict a variety of postoperative complications,including readmissions,surgical-site infections,anastomotic leakage,and mortality,by analyzing diverse data sources such as electronic health records,medical imaging,and preoperative markers.Despite the promising results,several challenges remain,including data quality,model generalizability,the complexity of clinical data,and ethical and regulatory concerns.The review emphasizes the need for multicenter,diverse datasets and the integration of AI into clinical workflows to improve model performance and adoption.Future efforts should focus on enhancing the transparency and interpretability of AI models to ensure their successful implementation in clinical practice,ultimately improving patient outcomes and surgical decision-making in CRC surgery.
文摘The machine learning model developed by Shi et al for predicting colorectal polyp recurrence after endoscopic mucosal resection represents a significant advancement in the field of clinical gastroenterology.By integrating patient-specific factors,such as age,smoking history,and Helicobacter pylori infection,the eXtreme Gradient Boosting algorithm enables precise personalised colonoscopy follow-up planning and risk assessment.This predictive tool offers substantial benefits by optimising surveillance intervals and directing healthcare resources more efficiently toward high-risk individuals.However,real-world implementation requires consideration of the generalisability of our findings across diverse patient populations and clinician training backgrounds.
基金Sichuan Province Science and Technology Department Key Research and Development Project,No.2023YFS0473.
文摘In this article,we discuss the study by Cheng et al,published in the World Journal of Gastroenterology,focusing on predictive methods for post-hepatectomy liver failure(PHLF).PHLF is a common and serious complication,and accurate prediction is critical for clinical management.The study examines the potential of ultrasound elastography and splenic size in predicting PHLF.Ultrasound elastography reflects liver functional reserve,while splenic size provides additional predictive value.By integrating these factors with serological markers,we developed a comprehensive prediction model that effectively stratifies patient risk and supports personalized clinical decisions.This approach offers new insights into predicting PHLF.These methods not only assist clinicians in identifying high-risk patients earlier but also provide scientific support for personalized treatment strategies.Future research will aim to validate the model's accuracy with larger sample sizes,further enhancing the clinical application of these non-invasive indicators.
基金the National Council for Scientific and Technological Development of Brazil(CNPQ)the Coordination for the Improvement of Higher Education Personnel-Brazil(CAPES)(Grant PROAP 88887.842889/2023-00-PUC/MG,Grant PDPG 88887.708960/2022-00-PUC/MG-INFORMATICA and Finance Code 001)Minas Gerais State Research Support Foundation(FAPEMIG)under Grant No.:APQ-01929-22,and the Pontifical Catholic University of Minas Gerais,Brazil.
文摘Higher education institutions are becoming increasingly concerned with the retention of their students.This work is motivated by the interest in predicting and reducing student dropout,and consequently in reducing the financial losses of said institutions.Based on the characterization of the dropout problem and the application of a knowledge discovery process,an ensemble model is proposed to improve dropout prediction.The ensemble model combines the results of three models:logistic regression,neural networks,and decision tree.As a result,the model can correctly classify 89%of the students as enrolled or dropped and accurately identify 98.1%of dropouts.When compared with the Random Forest ensemble method,the proposed model demonstrates desirable characteristics to assist management in proposing actions to retain students.
