Objective To explore novel long non-coding RNA(lncRNA)molecular markers related to bladder cancer prognosis and to construct a prognostic prediction model for bladder cancer patients.Methods LncRNA expression data of ...Objective To explore novel long non-coding RNA(lncRNA)molecular markers related to bladder cancer prognosis and to construct a prognostic prediction model for bladder cancer patients.Methods LncRNA expression data of patients with bladder cancer were downloaded from TCGA database.Univariate Cox regression and likelihood-based survival analysis were used to discover prognosis related lncRNAs.Functional studies of prognosis related lncRNAs were conducted by co-expression analysis and pathway enrichment analysis.Multivariate Cox regression analysis was used to establish risk score model,and Receiver Operating Characteristic analysis was used to determine the optimal cut-off point of the model.The risk score model was validated through Kaplan Meier estimation method and log-rank test.Results Seven prognosis related lncRNAs(OCIAD1-AS1,RP11-111 J6.2,AC079354.3,RP11-553 A21.3,RP11-598 F7.3,CYP4 F35 P and RP11-113 K21.4)which can predict survival of bladder cancer patient were discovered.Co-expression analysis and pathway analysis of these novel lncRNA signature and their target genes further revealed that these lncRNAs play important roles in the occurrence and development of bladder cancer.Additionally,a seven-lncRNA signature based risk score model for prognostic prediction of bladder cancer patients was established and validated.Notably,we identified the potential significance of two tumor-related antisense lncRNAs(OCIAD1-AS1 and RP11-553 A21.3)in the prognosis of bladder cancer.Conclusion Our results suggest that these lncRNA markers may serve as potential prognosis predictors for bladder cancer and deserve further functional verification studies.展开更多
As the fundamental and key technique to ensure the safe and reliable operation of vital systems,prognostics with an emphasis on the remaining useful life(RUL)prediction has attracted great attention in the last decade...As the fundamental and key technique to ensure the safe and reliable operation of vital systems,prognostics with an emphasis on the remaining useful life(RUL)prediction has attracted great attention in the last decades.In this paper,we briefly discuss the general idea and advances of various prognostics and RUL prediction methods for machinery,mainly including data-driven methods,physics-based methods,hybrid methods,etc.Based on the observations fromthe state of the art,we provide comprehensive discussions on the possible opportunities and challenges of prognostics and RUL prediction of machinery so as to steer the future development.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
Background: Studies of gastrointestinal (GIT) cancers have shown that circZFR could be involved in the development and progression of various GIT cancers. However, small sample sizes limit the clinical significance of...Background: Studies of gastrointestinal (GIT) cancers have shown that circZFR could be involved in the development and progression of various GIT cancers. However, small sample sizes limit the clinical significance of these studies. Here, a meta-analysis was conducted to ascertain the actual involvement of circZFR in the development and prognosis of GIT cancers. Methods: PubMed, Embase, Web of Science, and the Cochrane Library were searched up to December 31, 2023. Hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs) were pooled to evaluate the association between circZFR expression and overall survival (OS). Publication bias was measured using the funnel plot and Egger’s test. Results: 10 studies having 659 participants were enrolled for meta-analysis. High circZFR expression was associated with poor OS (HR = 1.4, 95% CI: 1.20, 1.70). High circZFR expression also predicted larger tumor size (OR = 4.38, 95% CI 2.65, 7.25), advanced clinical stage (OR = 5.33, 95% CI 3.10, 9.16), and tendency for distant metastasis (OR = 2.89, 95% CI: 1.62, 5.11), but was not related to age, gender, and histological grade. Conclusions: In summary, high circZFR expression was associated with poor OS, larger tumor size, advanced stage cancer and tendency for distant metastasis. These findings suggested that circZFR could be a prognostic marker for GIT cancers.展开更多
In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is di...In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.展开更多
A recently published modeling approach for the penetration into adobe and previous approaches implicitly criticized are reviewed and discussed.This article contains a note on the paper titled“Ballistic model for the ...A recently published modeling approach for the penetration into adobe and previous approaches implicitly criticized are reviewed and discussed.This article contains a note on the paper titled“Ballistic model for the prediction of penetration depth and residual velocity in adobe:A new interpretation of the ballistic resistance of earthen masonry”(DOI:https://doi.org/10.1016/j.dt.2018.07.017).Reply to the Note from Li Piani et al is linked to this article.展开更多
BACKGROUND Duodenal neuroendocrine tumours(DNETs)are rare neoplasms.However,the incidence of DNETs has been increasing in recent years,especially as an incidental finding during endoscopic studies.Regrettably,there is...BACKGROUND Duodenal neuroendocrine tumours(DNETs)are rare neoplasms.However,the incidence of DNETs has been increasing in recent years,especially as an incidental finding during endoscopic studies.Regrettably,there is no consensus regarding the ideal treatment of DNETs.Even there are few studies on the clinical features and survival analysis of DNETs.AIM To analyze the clinical characteristics and prognostic factors of patients with duodenal neuroendocrine tumours.METHODS The clinical data of DNETs diagnosed in the First Affiliated Hospital of Air Force Military Medical University from June 2011 to July 2022 were collected.Neuroen-docrine tumours located in the ampulla area of the duodenum were divided into the ampullary region group;neuroendocrine tumours in any part of the duo-denum outside the ampullary area were divided into the nonampullary region group.Using a retrospective study,the clinical characteristics of the two groups and risk factors affecting the survival of DNET patients were analysed.RESULTS Twenty-nine DNET patients were screened.The male to female ratio was 1:1.9,and females comprised the majority.The ampullary region group accounted for 24.1%(7/29),while the nonampullary region group accounted for 75.9%(22/29).When diagnosed,the clinical symptoms of the ampullary region group were mainly abdominal pain(85.7%),while those of the nonampullary region groups were mainly abdominal distension(59.1%).There were differences in the composition of staging of tumours between the two groups(Fisher's exact probability method,P=0.001),with nonampullary stage II tumours(68.2%)being the main stage(P<0.05).After the diagnosis of DNETs,the survival rate of the ampullary region group was 14.3%(1/7),which was lower than that of 72.7%(16/22)in the nonampullary region group(Fisher's exact probability method,P=0.011).The survival time of the ampullary region group was shorter than that of the nonampullary region group(P<0.000).The median survival time of the ampullary region group was 10.0 months and that of the nonampullary region group was 451.0 months.Multivariate analysis showed that tumours in the ampulla region and no surgical treatment after diagnosis were independent risk factors for the survival of DNET patients(HR=0.029,95%CI 0.004-0.199,P<0.000;HR=12.609,95%CI:2.889-55.037,P=0.001).Further analysis of nonampullary DNET patients showed that the survival time of patients with a tumour diameter<2 cm was longer than that of patients with a tumour diameter≥2 cm(t=7.243,P=0.048).As of follow-up,6 patients who died of nonampullary DNETs had a tumour diameter that was≥2 cm,and 3 patients in stage IV had liver metastasis.Patients with a tumour diameter<2 cm underwent surgical treatment,and all survived after surgery.CONCLUSION Surgical treatment is a protective factor for prolonging the survival of DNET patients.Compared to DNETs in the ampullary region,patients in the nonampullary region group had a longer survival period.The liver is the organ most susceptible to distant metastasis of nonampullary DNETs.展开更多
OBJECTIVES To establish a scoring system combining the ACEF score and the quantitative blood flow ratio(QFR) to improve the long-term risk prediction of patients undergoing percutaneous coronary intervention(PCI).METH...OBJECTIVES To establish a scoring system combining the ACEF score and the quantitative blood flow ratio(QFR) to improve the long-term risk prediction of patients undergoing percutaneous coronary intervention(PCI).METHODS In this population-based cohort study, a total of 46 features, including patient clinical and coronary lesion characteristics, were assessed for analysis through machine learning models. The ACEF-QFR scoring system was developed using 1263consecutive cases of CAD patients after PCI in PANDA Ⅲ trial database. The newly developed score was then validated on the other remaining 542 patients in the cohort.RESULTS In both the Random Forest Model and the Deep Surv Model, age, renal function(creatinine), cardiac function(LVEF)and post-PCI coronary physiological index(QFR) were identified and confirmed to be significant predictive factors for 2-year adverse cardiac events. The ACEF-QFR score was constructed based on the developmental dataset and computed as age(years)/EF(%) + 1(if creatinine ≥ 2.0 mg/d L) + 1(if post-PCI QFR ≤ 0.92). The performance of the ACEF-QFR scoring system was preliminarily evaluated in the developmental dataset, and then further explored in the validation dataset. The ACEF-QFR score showed superior discrimination(C-statistic = 0.651;95% CI: 0.611-0.691, P < 0.05 versus post-PCI physiological index and other commonly used risk scores) and excellent calibration(Hosmer–Lemeshow χ^(2)= 7.070;P = 0.529) for predicting 2-year patient-oriented composite endpoint(POCE). The good prognostic value of the ACEF-QFR score was further validated by multivariable Cox regression and Kaplan–Meier analysis(adjusted HR = 1.89;95% CI: 1.18–3.04;log-rank P < 0.01) after stratified the patients into high-risk group and low-risk group.CONCLUSIONS An improved scoring system combining clinical and coronary lesion-based functional variables(ACEF-QFR)was developed, and its ability for prognostic prediction in patients with PCI was further validated to be significantly better than the post-PCI physiological index and other commonly used risk scores.展开更多
The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication e...The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.展开更多
Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The ris...Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The rising federated learning provides us with a promising solution to this problem,which enables mobile devices to collaboratively learn a shared prediction model while keeping all the training data on the device,decoupling the ability to do machine learning from the need to store the data in the cloud.However,existing federated learningbased methods either do not provide privacy guarantees or have vulnerability in terms of privacy leakage.In this paper,we combine the techniques of data perturbation and model perturbation mechanisms and propose a privacy-preserving mobility prediction algorithm,where we add noise to the transmitted model and the raw data collaboratively to protect user privacy and keep the mobility prediction performance.Extensive experimental results show that our proposed method significantly outperforms the existing stateof-the-art mobility prediction method in terms of defensive performance against practical attacks while having comparable mobility prediction performance,demonstrating its effectiveness.展开更多
BACKGROUND Duodenal cancer is one of the most common subtypes of small intestinal cancer,and distant metastasis(DM)in this type of cancer still leads to poor prognosis.Although nomograms have recently been used in tum...BACKGROUND Duodenal cancer is one of the most common subtypes of small intestinal cancer,and distant metastasis(DM)in this type of cancer still leads to poor prognosis.Although nomograms have recently been used in tumor areas,no studies have focused on the diagnostic and prognostic evaluation of DM in patients with primary duodenal cancer.AIM To develop and evaluate nomograms for predicting the risk of DM and person-alized prognosis in patients with duodenal cancer.METHODS Data on duodenal cancer patients diagnosed between 2010 and 2019 were extracted from the Surveillance,Epidemiology,and End Results database.Univariate and multivariate logistic regression analyses were used to identify independent risk factors for DM in patients with duodenal cancer,and univariate and multivariate Cox proportional hazards regression analyses were used to determine independent prognostic factors in duodenal cancer patients with DM.Two novel nomograms were established,and the results were evaluated by receiver operating characteristic(ROC)curves,calibration curves,and decision curve analysis(DCA).RESULTS A total of 2603 patients with duodenal cancer were included,of whom 457 cases(17.56%)had DM at the time of diagnosis.Logistic analysis revealed independent risk factors for DM in duodenal cancer patients,including gender,grade,tumor size,T stage,and N stage(P<0.05).Univariate and multivariate COX analyses further identified independent prognostic factors for duodenal cancer patients with DM,including age,histological type,T stage,tumor grade,tumor size,bone metastasis,chemotherapy,and surgery(P<0.05).The accuracy of the nomograms was validated in the training set,validation set,and expanded testing set using ROC curves,calibration curves,and DCA curves.The results of Kaplan-Meier survival curves(P<0.001)indicated that both nomograms accurately predicted the occurrence and prognosis of DM in patients with duodenal cancer.CONCLUSION The two nomograms are expected as effective tools for predicting DM risk in duodenal cancer patients and offering personalized prognosis predictions for those with DM,potentially enhancing clinical decision-making.展开更多
BACKGROUND Immunotherapy for advanced gastric cancer has attracted widespread attention in recent years.However,the adverse reactions of immunotherapy and its relationship with patient prognosis still need further stu...BACKGROUND Immunotherapy for advanced gastric cancer has attracted widespread attention in recent years.However,the adverse reactions of immunotherapy and its relationship with patient prognosis still need further study.In order to determine the association between adverse reaction factors and prognosis,the aim of this study was to conduct a systematic prognostic analysis.By comprehensively evaluating the clinical data of patients with advanced gastric cancer treated by immunotherapy,a nomogram model will be established to predict the survival status of patients more accurately.AIM To explore the characteristics and predictors of immune-related adverse reactions(irAEs)in advanced gastric cancer patients receiving immunotherapy with programmed death protein-1(PD-1)inhibitors and to analyze the correlation between irAEs and patient prognosis.METHODS A total of 140 patients with advanced gastric cancer who were treated with PD-1 inhibitors in our hospital from June 2021 to October 2023 were selected.Patients were divided into the irAEs group and the non-irAEs group according to whether or not irAEs occurred.Clinical features,manifestations,and prognosis of irAEs in the two groups were collected and analyzed.A multivariate logistic regression model was used to analyze the related factors affecting the occurrence of irAEs,and the prediction model of irAEs was established.The receiver operating characteristic(ROC)curve was used to evaluate the ability of different indicators to predict irAEs.A Kaplan-Meier survival curve was used to analyze the correlation between irAEs and prognosis.The Cox proportional risk model was used to analyze the related factors affecting the prognosis of patients.RESULTS A total of 132 patients were followed up,of whom 63(47.7%)developed irAEs.We looked at the two groups’clinical features and found that the two groups were statistically different in age≥65 years,Ki-67 index,white blood cell count,neutrophil count,and regulatory T cell(Treg)count(all P<0.05).Multivariate logistic regression analysis showed that Treg count was a protective factor affecting irAEs occurrence(P=0.030).The ROC curve indicated that Treg+Ki-67+age(≥65 years)combined could predict irAEs well(area under the curve=0.753,95%confidence interval:0.623-0.848,P=0.001).Results of the Kaplan-Meier survival curve showed that progressionfree survival(PFS)was longer in the irAEs group than in the non-irAEs group(P=0.001).Cox proportional hazard regression analysis suggested that the occurrence of irAEs was an independent factor for PFS(P=0.006).CONCLUSION The number of Treg cells is a separate factor that affects irAEs in advanced gastric cancer patients receiving PD-1 inhibitor immunotherapy.irAEs can affect the patients’PFS and result in longer PFS.Treg+Ki-67+age(≥65 years old)combined can better predict the occurrence of adverse reactions.展开更多
The scientific community recognizes the seriousness of rockbursts and the need for effective mitigation measures.The literature reports various successful applications of machine learning(ML)models for rockburst asses...The scientific community recognizes the seriousness of rockbursts and the need for effective mitigation measures.The literature reports various successful applications of machine learning(ML)models for rockburst assessment;however,a significant question remains unanswered:How reliable are these models,and at what confidence level are classifications made?Typically,ML models output single rockburst grade even in the face of intricate and out-of-distribution samples,without any associated confidence value.Given the susceptibility of ML models to errors,it becomes imperative to quantify their uncertainty to prevent consequential failures.To address this issue,we propose a conformal prediction(CP)framework built on traditional ML models(extreme gradient boosting and random forest)to generate valid classifications of rockburst while producing a measure of confidence for its output.The proposed framework guarantees marginal coverage and,in most cases,conditional coverage on the test dataset.The CP was evaluated on a rockburst case in the Sanshandao Gold Mine in China,where it achieved high coverage and efficiency at applicable confidence levels.Significantly,the CP identified several“confident”classifications from the traditional ML model as unreliable,necessitating expert verification for informed decision-making.The proposed framework improves the reliability and accuracy of rockburst assessments,with the potential to bolster user confidence.展开更多
Predicting students’academic achievements is an essential issue in education,which can benefit many stakeholders,for instance,students,teachers,managers,etc.Compared with online courses such asMOOCs,students’academi...Predicting students’academic achievements is an essential issue in education,which can benefit many stakeholders,for instance,students,teachers,managers,etc.Compared with online courses such asMOOCs,students’academicrelateddata in the face-to-face physical teaching environment is usually sparsity,and the sample size is relativelysmall.It makes building models to predict students’performance accurately in such an environment even morechallenging.This paper proposes a Two-WayNeuralNetwork(TWNN)model based on the bidirectional recurrentneural network and graph neural network to predict students’next semester’s course performance using only theirprevious course achievements.Extensive experiments on a real dataset show that our model performs better thanthe baselines in many indicators.展开更多
Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the pre...Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the preferred method for modeling accident severity.Deep learning’s strength lies in handling intricate relation-ships within extensive datasets,making it popular for accident severity level(ASL)prediction and classification.Despite prior success,there is a need for an efficient system recognizing ASL in diverse road conditions.To address this,we present an innovative Accident Severity Level Prediction Deep Learning(ASLP-DL)framework,incorporating DNN,D-CNN,and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic Gradient Descent.The framework optimizes hidden layers and integrates data augmentation,Gaussian noise,and dropout regularization for improved generalization.Sensitivity and factor contribution analyses identify influential predictors.Evaluated on three diverse crash record databases—NCDB 2018–2019,UK 2015–2020,and US 2016–2021—the D-RNN model excels with an ACC score of 89.0281%,a Roc Area of 0.751,an F-estimate of 0.941,and a Kappa score of 0.0629 over the NCDB dataset.The proposed framework consistently outperforms traditional methods,existing machine learning,and deep learning techniques.展开更多
BACKGROUND Liver metastases(LM)is the primary factor contributing to unfavorable outcomes in patients diagnosed with gastric cancer(GC).The objective of this study is to analyze significant prognostic risk factors for...BACKGROUND Liver metastases(LM)is the primary factor contributing to unfavorable outcomes in patients diagnosed with gastric cancer(GC).The objective of this study is to analyze significant prognostic risk factors for patients with GCLM and develop a reliable nomogram model that can accurately predict individualized prognosis,thereby enhancing the ability to evaluate patient outcomes.AIM To analyze prognostic risk factors for GCLM and develop a reliable nomogram model to accurately predict individualized prognosis,thereby enhancing patient outcome assessment.METHODS Retrospective analysis was conducted on clinical data pertaining to GCLM(type III),admitted to the Department of General Surgery across multiple centers of the Chinese PLA General Hospital from January 2010 to January 2018.The dataset was divided into a development cohort and validation cohort in a ratio of 2:1.In the development cohort,we utilized univariate and multivariate Cox regression analyses to identify independent risk factors associated with overall survival in GCLM patients.Subsequently,we established a prediction model based on these findings and evaluated its performance using receiver operator characteristic curve analysis,calibration curves,and clinical decision curves.A nomogram was created to visually represent the prediction model,which was then externally validated using the validation cohort.RESULTS A total of 372 patients were included in this study,comprising 248 individuals in the development cohort and 124 individuals in the validation cohort.Based on Cox analysis results,our final prediction model incorporated five independent risk factors including albumin levels,primary tumor size,presence of extrahepatic metastases,surgical treatment status,and chemotherapy administration.The 1-,3-,and 5-years Area Under the Curve values in the development cohort are 0.753,0.859,and 0.909,respectively;whereas in the validation cohort,they are observed to be 0.772,0.848,and 0.923.Furthermore,the calibration curves demonstrated excellent consistency between observed values and actual values.Finally,the decision curve analysis curve indicated substantial net clinical benefit.CONCLUSION Our study identified significant prognostic risk factors for GCLM and developed a reliable nomogram model,demonstrating promising predictive accuracy and potential clinical benefit in evaluating patient outcomes.展开更多
BACKGROUND Sterol O-acyltransferase 1(SOAT1)is an important target in the diagnosis and treatment of liver cancer.However,the prognostic value of SOAT1 in patients with hepatocellular carcinoma(HCC)is still not clear....BACKGROUND Sterol O-acyltransferase 1(SOAT1)is an important target in the diagnosis and treatment of liver cancer.However,the prognostic value of SOAT1 in patients with hepatocellular carcinoma(HCC)is still not clear.AIM To investigate the correlation of SOAT1 expression with HCC,using RNA-seq and gene expression data of The Cancer Genome Atlas(TCGA)-liver hepatocellular carcinoma(LIHC)and pan-cancer.METHODS The correlation between SOAT1 expression and HCC was analyzed.Cox hazard regression models were conducted to investigate the prognostic value of SOAT1 in HCC.Overall survival and disease-specific survival were explored based on TCGA-LIHC data.Biological processes and functional pathways mediated by SOAT1 were characterized by gene ontology(GO)analysis and the Kyoto Encyclopedia of Genes and Genomes(KEGG)analysis of differentially expressed genes.In addition,the protein-protein interaction network and co-expression analyses of SOAT1 in HCC were performed to better understand the regulatory mechanisms of SOAT1 in this malignancy.RESULTS SOAT1 and SOAT2 were highly expressed in unpaired samples,while only SOAT1 was highly expressed in paired samples.The area under the receiver operating characteristic curve of SOAT1 expression in tumor samples from LIHC patients compared with para-carcinoma tissues was 0.748,while the area under the curve of SOAT1 expression in tumor samples from LIHC patients compared with GTEx was 0.676.Patients with higher SOAT1 expression had lower survival rates.Results from GO/KEGG and gene set enrichment analyses suggested that the PI3K/AKT signaling pathway,the IL-18 signaling pathway,the calcium signaling pathway,secreted factors,the Wnt signaling pathway,the Jak/STAT signaling pathway,the MAPK family signaling pathway,and cell–cell communication were involved in such association.SOAT1 expression was positively associated with the abundance of macrophages,Th2 cells,T helper cells,CD56bright natural killer cells,and Th1 cells,and negatively linked to the abundance of Th17 cells,dendritic cells,and cytotoxic cells.CONCLUSION Our findings demonstrate that SOAT1 may serve as a novel target for HCC treatment,which is helpful for the development of new strategies for immunotherapy and metabolic therapy.展开更多
BACKGROUND The controlling nutritional status(CONUT)score effectively reflects a patient’s nutritional status,which is closely related to cancer prognosis.This study invest-igated the relationship between the CONUT s...BACKGROUND The controlling nutritional status(CONUT)score effectively reflects a patient’s nutritional status,which is closely related to cancer prognosis.This study invest-igated the relationship between the CONUT score and prognosis after radical surgery for colorectal cancer,and compared the predictive ability of the CONUT score with other indexes.AIM To analyze the predictive performance of the CONUT score for the survival rate of colorectal cancer patients who underwent potentially curative resection.METHODS This retrospective analysis included 217 patients with newly diagnosed colorectal.The CONUT score was calculated based on the serum albumin level,total lymphocyte count,and total cholesterol level.The cutoff value of the CONUT score for predicting prognosis was 4 according to the Youden Index by the receiver operating characteristic curve.The associations between the CONUT score and the prognosis were performed using Kaplan-Meier curves and Cox regression analysis.RESULTS Using the cutoff value of the CONUT score,patients were stratified into CONUT low(n=189)and CONUT high groups(n=28).The CONUT high group had worse overall survival(OS)(P=0.013)and relapse-free survival(RFS)(P=0.015).The predictive performance of CONUT was superior to the modified Glasgow prognostic score,the prognostic nutritional index,and the neutrophil-to-lymphocyte ratio.Meanwhile,the predictive performances of CONUT+tumor node metastasis(TNM)stage for 3-year OS[area under the receiver operating characteristics curve(AUC)=0.803]and 3-year RFS(AUC=0.752)were no less than skeletal muscle mass index(SMI)+TNM stage.The CONUT score was negatively correlated with SMI(P<0.01).CONCLUSION As a nutritional indicator,the CONUT score could predict long-term outcomes after radical surgery for colorectal cancer,and its predictive ability was superior to other indexes.The correlation between the CONUT score and skeletal muscle may be one of the factors that play a predictive role.展开更多
Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of ...Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.展开更多
BACKGROUND Gastric cancer is the third most common cause of cancer related death worldwide.Surgery with or without chemotherapy is the most common approach with curative intent;however,the prognosis is poor as mortali...BACKGROUND Gastric cancer is the third most common cause of cancer related death worldwide.Surgery with or without chemotherapy is the most common approach with curative intent;however,the prognosis is poor as mortality rates remain high.Several indexes have been proposed in the past few years in order to estimate the survival of patients undergoing gastrectomy.The preoperative nutritional status of gastric cancer patients has recently gained attention as a factor that could affect the postoperative course and various indexes have been developed.The aim of this systematic review was to assess the role of the prognostic nutritional index(PNI)in predicting the survival of patients with gastric or gastroesophageal adenocarcinoma who underwent gastrectomy with curative intent.AIM To investigate the role of PNI in predicting the survival of patients with gastric or gastroesophageal junction adenocarcinoma.METHODS A thorough literature search of PubMed and the Cochrane library was performed for studies comparing the overall survival(OS)of patients with gastric or gastroesophageal cancer after surgical resection depending on the preoperative PNI value.The PRISMA algorithm was used in the screening process and finally 16 studies were included in this systematic review.The review protocol was registered in the International Prospective Register of Systematic Reviews(PRO) RESULTS Sixteen studies involving 14551 patients with gastric or esophagogastric junction adenocarcinoma undergoing open or laparoscopic or robotic gastrectomy with or without adjuvant chemotherapy were included in this systematic review.The patients were divided into high-and low-PNI groups according to cut-off values that were set according to previous reports or by using receiver operating characteristic curve analysis in each individual study.The 5-year OS of patients in the low-PNI groups ranged between 39%and 70.6%,while in the high-PNI groups,it ranged between 54.9%and 95.8%.In most of the included studies,patients with high preoperative PNI showed statistically significant better OS than the low PNI groups.In multivariate analyses,low PNI was repeatedly recognised as an independent prognostic factor for poor survival.CONCLUSION According to the present study,low preoperative PNI seems to be an indicator of poor OS of patients undergoing gastrectomy for gastric or gastroesophageal cancer.展开更多
文摘Objective To explore novel long non-coding RNA(lncRNA)molecular markers related to bladder cancer prognosis and to construct a prognostic prediction model for bladder cancer patients.Methods LncRNA expression data of patients with bladder cancer were downloaded from TCGA database.Univariate Cox regression and likelihood-based survival analysis were used to discover prognosis related lncRNAs.Functional studies of prognosis related lncRNAs were conducted by co-expression analysis and pathway enrichment analysis.Multivariate Cox regression analysis was used to establish risk score model,and Receiver Operating Characteristic analysis was used to determine the optimal cut-off point of the model.The risk score model was validated through Kaplan Meier estimation method and log-rank test.Results Seven prognosis related lncRNAs(OCIAD1-AS1,RP11-111 J6.2,AC079354.3,RP11-553 A21.3,RP11-598 F7.3,CYP4 F35 P and RP11-113 K21.4)which can predict survival of bladder cancer patient were discovered.Co-expression analysis and pathway analysis of these novel lncRNA signature and their target genes further revealed that these lncRNAs play important roles in the occurrence and development of bladder cancer.Additionally,a seven-lncRNA signature based risk score model for prognostic prediction of bladder cancer patients was established and validated.Notably,we identified the potential significance of two tumor-related antisense lncRNAs(OCIAD1-AS1 and RP11-553 A21.3)in the prognosis of bladder cancer.Conclusion Our results suggest that these lncRNA markers may serve as potential prognosis predictors for bladder cancer and deserve further functional verification studies.
基金The work in Section III was supported by the National Science Foundation of China(NSFC)(Nos.52025056,52005387)the work in Section IV was supported by the National Science Foundation of China(NSFC)(Nos.62233017,62073336).
文摘As the fundamental and key technique to ensure the safe and reliable operation of vital systems,prognostics with an emphasis on the remaining useful life(RUL)prediction has attracted great attention in the last decades.In this paper,we briefly discuss the general idea and advances of various prognostics and RUL prediction methods for machinery,mainly including data-driven methods,physics-based methods,hybrid methods,etc.Based on the observations fromthe state of the art,we provide comprehensive discussions on the possible opportunities and challenges of prognostics and RUL prediction of machinery so as to steer the future development.
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.
文摘Background: Studies of gastrointestinal (GIT) cancers have shown that circZFR could be involved in the development and progression of various GIT cancers. However, small sample sizes limit the clinical significance of these studies. Here, a meta-analysis was conducted to ascertain the actual involvement of circZFR in the development and prognosis of GIT cancers. Methods: PubMed, Embase, Web of Science, and the Cochrane Library were searched up to December 31, 2023. Hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs) were pooled to evaluate the association between circZFR expression and overall survival (OS). Publication bias was measured using the funnel plot and Egger’s test. Results: 10 studies having 659 participants were enrolled for meta-analysis. High circZFR expression was associated with poor OS (HR = 1.4, 95% CI: 1.20, 1.70). High circZFR expression also predicted larger tumor size (OR = 4.38, 95% CI 2.65, 7.25), advanced clinical stage (OR = 5.33, 95% CI 3.10, 9.16), and tendency for distant metastasis (OR = 2.89, 95% CI: 1.62, 5.11), but was not related to age, gender, and histological grade. Conclusions: In summary, high circZFR expression was associated with poor OS, larger tumor size, advanced stage cancer and tendency for distant metastasis. These findings suggested that circZFR could be a prognostic marker for GIT cancers.
文摘In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.
文摘A recently published modeling approach for the penetration into adobe and previous approaches implicitly criticized are reviewed and discussed.This article contains a note on the paper titled“Ballistic model for the prediction of penetration depth and residual velocity in adobe:A new interpretation of the ballistic resistance of earthen masonry”(DOI:https://doi.org/10.1016/j.dt.2018.07.017).Reply to the Note from Li Piani et al is linked to this article.
基金The study protocol was approved by the Clinical Research Ethics Committee of Honghui Hospital,Xi’an Jiaotong University(No.202401004).
文摘BACKGROUND Duodenal neuroendocrine tumours(DNETs)are rare neoplasms.However,the incidence of DNETs has been increasing in recent years,especially as an incidental finding during endoscopic studies.Regrettably,there is no consensus regarding the ideal treatment of DNETs.Even there are few studies on the clinical features and survival analysis of DNETs.AIM To analyze the clinical characteristics and prognostic factors of patients with duodenal neuroendocrine tumours.METHODS The clinical data of DNETs diagnosed in the First Affiliated Hospital of Air Force Military Medical University from June 2011 to July 2022 were collected.Neuroen-docrine tumours located in the ampulla area of the duodenum were divided into the ampullary region group;neuroendocrine tumours in any part of the duo-denum outside the ampullary area were divided into the nonampullary region group.Using a retrospective study,the clinical characteristics of the two groups and risk factors affecting the survival of DNET patients were analysed.RESULTS Twenty-nine DNET patients were screened.The male to female ratio was 1:1.9,and females comprised the majority.The ampullary region group accounted for 24.1%(7/29),while the nonampullary region group accounted for 75.9%(22/29).When diagnosed,the clinical symptoms of the ampullary region group were mainly abdominal pain(85.7%),while those of the nonampullary region groups were mainly abdominal distension(59.1%).There were differences in the composition of staging of tumours between the two groups(Fisher's exact probability method,P=0.001),with nonampullary stage II tumours(68.2%)being the main stage(P<0.05).After the diagnosis of DNETs,the survival rate of the ampullary region group was 14.3%(1/7),which was lower than that of 72.7%(16/22)in the nonampullary region group(Fisher's exact probability method,P=0.011).The survival time of the ampullary region group was shorter than that of the nonampullary region group(P<0.000).The median survival time of the ampullary region group was 10.0 months and that of the nonampullary region group was 451.0 months.Multivariate analysis showed that tumours in the ampulla region and no surgical treatment after diagnosis were independent risk factors for the survival of DNET patients(HR=0.029,95%CI 0.004-0.199,P<0.000;HR=12.609,95%CI:2.889-55.037,P=0.001).Further analysis of nonampullary DNET patients showed that the survival time of patients with a tumour diameter<2 cm was longer than that of patients with a tumour diameter≥2 cm(t=7.243,P=0.048).As of follow-up,6 patients who died of nonampullary DNETs had a tumour diameter that was≥2 cm,and 3 patients in stage IV had liver metastasis.Patients with a tumour diameter<2 cm underwent surgical treatment,and all survived after surgery.CONCLUSION Surgical treatment is a protective factor for prolonging the survival of DNET patients.Compared to DNETs in the ampullary region,patients in the nonampullary region group had a longer survival period.The liver is the organ most susceptible to distant metastasis of nonampullary DNETs.
基金sponsored by Sino Medical,Tianjin,Chinasupported by the Beijing Municipal Science and Technology Project[Z191100006619107 to B.X.]Capital Health Development Research Project[20201–4032 to K.D.].
文摘OBJECTIVES To establish a scoring system combining the ACEF score and the quantitative blood flow ratio(QFR) to improve the long-term risk prediction of patients undergoing percutaneous coronary intervention(PCI).METHODS In this population-based cohort study, a total of 46 features, including patient clinical and coronary lesion characteristics, were assessed for analysis through machine learning models. The ACEF-QFR scoring system was developed using 1263consecutive cases of CAD patients after PCI in PANDA Ⅲ trial database. The newly developed score was then validated on the other remaining 542 patients in the cohort.RESULTS In both the Random Forest Model and the Deep Surv Model, age, renal function(creatinine), cardiac function(LVEF)and post-PCI coronary physiological index(QFR) were identified and confirmed to be significant predictive factors for 2-year adverse cardiac events. The ACEF-QFR score was constructed based on the developmental dataset and computed as age(years)/EF(%) + 1(if creatinine ≥ 2.0 mg/d L) + 1(if post-PCI QFR ≤ 0.92). The performance of the ACEF-QFR scoring system was preliminarily evaluated in the developmental dataset, and then further explored in the validation dataset. The ACEF-QFR score showed superior discrimination(C-statistic = 0.651;95% CI: 0.611-0.691, P < 0.05 versus post-PCI physiological index and other commonly used risk scores) and excellent calibration(Hosmer–Lemeshow χ^(2)= 7.070;P = 0.529) for predicting 2-year patient-oriented composite endpoint(POCE). The good prognostic value of the ACEF-QFR score was further validated by multivariable Cox regression and Kaplan–Meier analysis(adjusted HR = 1.89;95% CI: 1.18–3.04;log-rank P < 0.01) after stratified the patients into high-risk group and low-risk group.CONCLUSIONS An improved scoring system combining clinical and coronary lesion-based functional variables(ACEF-QFR)was developed, and its ability for prognostic prediction in patients with PCI was further validated to be significantly better than the post-PCI physiological index and other commonly used risk scores.
文摘The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.
基金supported in part by the National Key Research and Development Program of China under 2020AAA0106000the National Natural Science Foundation of China under U20B2060 and U21B2036supported by a grant from the Guoqiang Institute, Tsinghua University under 2021GQG1005
文摘Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The rising federated learning provides us with a promising solution to this problem,which enables mobile devices to collaboratively learn a shared prediction model while keeping all the training data on the device,decoupling the ability to do machine learning from the need to store the data in the cloud.However,existing federated learningbased methods either do not provide privacy guarantees or have vulnerability in terms of privacy leakage.In this paper,we combine the techniques of data perturbation and model perturbation mechanisms and propose a privacy-preserving mobility prediction algorithm,where we add noise to the transmitted model and the raw data collaboratively to protect user privacy and keep the mobility prediction performance.Extensive experimental results show that our proposed method significantly outperforms the existing stateof-the-art mobility prediction method in terms of defensive performance against practical attacks while having comparable mobility prediction performance,demonstrating its effectiveness.
基金Supported by State Administration of Traditional Chinese Medicine Base Construction Stomach Cancer Special Fund,No.Y2020CX57Jiangsu Provincial Graduate Research and Practical Innovation Program Project,No.SJCX23-0799.
文摘BACKGROUND Duodenal cancer is one of the most common subtypes of small intestinal cancer,and distant metastasis(DM)in this type of cancer still leads to poor prognosis.Although nomograms have recently been used in tumor areas,no studies have focused on the diagnostic and prognostic evaluation of DM in patients with primary duodenal cancer.AIM To develop and evaluate nomograms for predicting the risk of DM and person-alized prognosis in patients with duodenal cancer.METHODS Data on duodenal cancer patients diagnosed between 2010 and 2019 were extracted from the Surveillance,Epidemiology,and End Results database.Univariate and multivariate logistic regression analyses were used to identify independent risk factors for DM in patients with duodenal cancer,and univariate and multivariate Cox proportional hazards regression analyses were used to determine independent prognostic factors in duodenal cancer patients with DM.Two novel nomograms were established,and the results were evaluated by receiver operating characteristic(ROC)curves,calibration curves,and decision curve analysis(DCA).RESULTS A total of 2603 patients with duodenal cancer were included,of whom 457 cases(17.56%)had DM at the time of diagnosis.Logistic analysis revealed independent risk factors for DM in duodenal cancer patients,including gender,grade,tumor size,T stage,and N stage(P<0.05).Univariate and multivariate COX analyses further identified independent prognostic factors for duodenal cancer patients with DM,including age,histological type,T stage,tumor grade,tumor size,bone metastasis,chemotherapy,and surgery(P<0.05).The accuracy of the nomograms was validated in the training set,validation set,and expanded testing set using ROC curves,calibration curves,and DCA curves.The results of Kaplan-Meier survival curves(P<0.001)indicated that both nomograms accurately predicted the occurrence and prognosis of DM in patients with duodenal cancer.CONCLUSION The two nomograms are expected as effective tools for predicting DM risk in duodenal cancer patients and offering personalized prognosis predictions for those with DM,potentially enhancing clinical decision-making.
基金Our study has been approved by Medical Research Ethics Approval Committee(2023010122HN11C).
文摘BACKGROUND Immunotherapy for advanced gastric cancer has attracted widespread attention in recent years.However,the adverse reactions of immunotherapy and its relationship with patient prognosis still need further study.In order to determine the association between adverse reaction factors and prognosis,the aim of this study was to conduct a systematic prognostic analysis.By comprehensively evaluating the clinical data of patients with advanced gastric cancer treated by immunotherapy,a nomogram model will be established to predict the survival status of patients more accurately.AIM To explore the characteristics and predictors of immune-related adverse reactions(irAEs)in advanced gastric cancer patients receiving immunotherapy with programmed death protein-1(PD-1)inhibitors and to analyze the correlation between irAEs and patient prognosis.METHODS A total of 140 patients with advanced gastric cancer who were treated with PD-1 inhibitors in our hospital from June 2021 to October 2023 were selected.Patients were divided into the irAEs group and the non-irAEs group according to whether or not irAEs occurred.Clinical features,manifestations,and prognosis of irAEs in the two groups were collected and analyzed.A multivariate logistic regression model was used to analyze the related factors affecting the occurrence of irAEs,and the prediction model of irAEs was established.The receiver operating characteristic(ROC)curve was used to evaluate the ability of different indicators to predict irAEs.A Kaplan-Meier survival curve was used to analyze the correlation between irAEs and prognosis.The Cox proportional risk model was used to analyze the related factors affecting the prognosis of patients.RESULTS A total of 132 patients were followed up,of whom 63(47.7%)developed irAEs.We looked at the two groups’clinical features and found that the two groups were statistically different in age≥65 years,Ki-67 index,white blood cell count,neutrophil count,and regulatory T cell(Treg)count(all P<0.05).Multivariate logistic regression analysis showed that Treg count was a protective factor affecting irAEs occurrence(P=0.030).The ROC curve indicated that Treg+Ki-67+age(≥65 years)combined could predict irAEs well(area under the curve=0.753,95%confidence interval:0.623-0.848,P=0.001).Results of the Kaplan-Meier survival curve showed that progressionfree survival(PFS)was longer in the irAEs group than in the non-irAEs group(P=0.001).Cox proportional hazard regression analysis suggested that the occurrence of irAEs was an independent factor for PFS(P=0.006).CONCLUSION The number of Treg cells is a separate factor that affects irAEs in advanced gastric cancer patients receiving PD-1 inhibitor immunotherapy.irAEs can affect the patients’PFS and result in longer PFS.Treg+Ki-67+age(≥65 years old)combined can better predict the occurrence of adverse reactions.
文摘The scientific community recognizes the seriousness of rockbursts and the need for effective mitigation measures.The literature reports various successful applications of machine learning(ML)models for rockburst assessment;however,a significant question remains unanswered:How reliable are these models,and at what confidence level are classifications made?Typically,ML models output single rockburst grade even in the face of intricate and out-of-distribution samples,without any associated confidence value.Given the susceptibility of ML models to errors,it becomes imperative to quantify their uncertainty to prevent consequential failures.To address this issue,we propose a conformal prediction(CP)framework built on traditional ML models(extreme gradient boosting and random forest)to generate valid classifications of rockburst while producing a measure of confidence for its output.The proposed framework guarantees marginal coverage and,in most cases,conditional coverage on the test dataset.The CP was evaluated on a rockburst case in the Sanshandao Gold Mine in China,where it achieved high coverage and efficiency at applicable confidence levels.Significantly,the CP identified several“confident”classifications from the traditional ML model as unreliable,necessitating expert verification for informed decision-making.The proposed framework improves the reliability and accuracy of rockburst assessments,with the potential to bolster user confidence.
基金the National Natural Science Foundation of China under Grant Nos.U2268204,62172061 and 61662017National Key R&D Program of China under Grant Nos.2020YFB1711800 and 2020YFB1707900+1 种基金the Science and Technology Project of Sichuan Province under Grant Nos.2022YFG0155,2022YFG0157,2021GFW019,2021YFG0152,2021YFG0025,2020YFG0322the Guangxi Natural Science Foundation Project under Grant No.2021GXNSFAA220074.
文摘Predicting students’academic achievements is an essential issue in education,which can benefit many stakeholders,for instance,students,teachers,managers,etc.Compared with online courses such asMOOCs,students’academicrelateddata in the face-to-face physical teaching environment is usually sparsity,and the sample size is relativelysmall.It makes building models to predict students’performance accurately in such an environment even morechallenging.This paper proposes a Two-WayNeuralNetwork(TWNN)model based on the bidirectional recurrentneural network and graph neural network to predict students’next semester’s course performance using only theirprevious course achievements.Extensive experiments on a real dataset show that our model performs better thanthe baselines in many indicators.
文摘Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the preferred method for modeling accident severity.Deep learning’s strength lies in handling intricate relation-ships within extensive datasets,making it popular for accident severity level(ASL)prediction and classification.Despite prior success,there is a need for an efficient system recognizing ASL in diverse road conditions.To address this,we present an innovative Accident Severity Level Prediction Deep Learning(ASLP-DL)framework,incorporating DNN,D-CNN,and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic Gradient Descent.The framework optimizes hidden layers and integrates data augmentation,Gaussian noise,and dropout regularization for improved generalization.Sensitivity and factor contribution analyses identify influential predictors.Evaluated on three diverse crash record databases—NCDB 2018–2019,UK 2015–2020,and US 2016–2021—the D-RNN model excels with an ACC score of 89.0281%,a Roc Area of 0.751,an F-estimate of 0.941,and a Kappa score of 0.0629 over the NCDB dataset.The proposed framework consistently outperforms traditional methods,existing machine learning,and deep learning techniques.
文摘BACKGROUND Liver metastases(LM)is the primary factor contributing to unfavorable outcomes in patients diagnosed with gastric cancer(GC).The objective of this study is to analyze significant prognostic risk factors for patients with GCLM and develop a reliable nomogram model that can accurately predict individualized prognosis,thereby enhancing the ability to evaluate patient outcomes.AIM To analyze prognostic risk factors for GCLM and develop a reliable nomogram model to accurately predict individualized prognosis,thereby enhancing patient outcome assessment.METHODS Retrospective analysis was conducted on clinical data pertaining to GCLM(type III),admitted to the Department of General Surgery across multiple centers of the Chinese PLA General Hospital from January 2010 to January 2018.The dataset was divided into a development cohort and validation cohort in a ratio of 2:1.In the development cohort,we utilized univariate and multivariate Cox regression analyses to identify independent risk factors associated with overall survival in GCLM patients.Subsequently,we established a prediction model based on these findings and evaluated its performance using receiver operator characteristic curve analysis,calibration curves,and clinical decision curves.A nomogram was created to visually represent the prediction model,which was then externally validated using the validation cohort.RESULTS A total of 372 patients were included in this study,comprising 248 individuals in the development cohort and 124 individuals in the validation cohort.Based on Cox analysis results,our final prediction model incorporated five independent risk factors including albumin levels,primary tumor size,presence of extrahepatic metastases,surgical treatment status,and chemotherapy administration.The 1-,3-,and 5-years Area Under the Curve values in the development cohort are 0.753,0.859,and 0.909,respectively;whereas in the validation cohort,they are observed to be 0.772,0.848,and 0.923.Furthermore,the calibration curves demonstrated excellent consistency between observed values and actual values.Finally,the decision curve analysis curve indicated substantial net clinical benefit.CONCLUSION Our study identified significant prognostic risk factors for GCLM and developed a reliable nomogram model,demonstrating promising predictive accuracy and potential clinical benefit in evaluating patient outcomes.
基金Supported by the Tianjin Municipal Project of Science and Technology,No.21ZXGWSY00040and the Tianjin Health Research Project,No.TJWJ2022QN043.
文摘BACKGROUND Sterol O-acyltransferase 1(SOAT1)is an important target in the diagnosis and treatment of liver cancer.However,the prognostic value of SOAT1 in patients with hepatocellular carcinoma(HCC)is still not clear.AIM To investigate the correlation of SOAT1 expression with HCC,using RNA-seq and gene expression data of The Cancer Genome Atlas(TCGA)-liver hepatocellular carcinoma(LIHC)and pan-cancer.METHODS The correlation between SOAT1 expression and HCC was analyzed.Cox hazard regression models were conducted to investigate the prognostic value of SOAT1 in HCC.Overall survival and disease-specific survival were explored based on TCGA-LIHC data.Biological processes and functional pathways mediated by SOAT1 were characterized by gene ontology(GO)analysis and the Kyoto Encyclopedia of Genes and Genomes(KEGG)analysis of differentially expressed genes.In addition,the protein-protein interaction network and co-expression analyses of SOAT1 in HCC were performed to better understand the regulatory mechanisms of SOAT1 in this malignancy.RESULTS SOAT1 and SOAT2 were highly expressed in unpaired samples,while only SOAT1 was highly expressed in paired samples.The area under the receiver operating characteristic curve of SOAT1 expression in tumor samples from LIHC patients compared with para-carcinoma tissues was 0.748,while the area under the curve of SOAT1 expression in tumor samples from LIHC patients compared with GTEx was 0.676.Patients with higher SOAT1 expression had lower survival rates.Results from GO/KEGG and gene set enrichment analyses suggested that the PI3K/AKT signaling pathway,the IL-18 signaling pathway,the calcium signaling pathway,secreted factors,the Wnt signaling pathway,the Jak/STAT signaling pathway,the MAPK family signaling pathway,and cell–cell communication were involved in such association.SOAT1 expression was positively associated with the abundance of macrophages,Th2 cells,T helper cells,CD56bright natural killer cells,and Th1 cells,and negatively linked to the abundance of Th17 cells,dendritic cells,and cytotoxic cells.CONCLUSION Our findings demonstrate that SOAT1 may serve as a novel target for HCC treatment,which is helpful for the development of new strategies for immunotherapy and metabolic therapy.
基金Clinical Trials from the Affiliated Drum Tower Hospital,Medical School of Nanjing University,2022-LCYJ-PY-17CIMF-CSPEN Project,Z-2017-24-2211Project of Chinese Hospital Reform and Development Institute,Nanjing University and Aid project of Nanjing Drum Tower Hospital Health,Education&Research Foundation,NDYG2022090。
文摘BACKGROUND The controlling nutritional status(CONUT)score effectively reflects a patient’s nutritional status,which is closely related to cancer prognosis.This study invest-igated the relationship between the CONUT score and prognosis after radical surgery for colorectal cancer,and compared the predictive ability of the CONUT score with other indexes.AIM To analyze the predictive performance of the CONUT score for the survival rate of colorectal cancer patients who underwent potentially curative resection.METHODS This retrospective analysis included 217 patients with newly diagnosed colorectal.The CONUT score was calculated based on the serum albumin level,total lymphocyte count,and total cholesterol level.The cutoff value of the CONUT score for predicting prognosis was 4 according to the Youden Index by the receiver operating characteristic curve.The associations between the CONUT score and the prognosis were performed using Kaplan-Meier curves and Cox regression analysis.RESULTS Using the cutoff value of the CONUT score,patients were stratified into CONUT low(n=189)and CONUT high groups(n=28).The CONUT high group had worse overall survival(OS)(P=0.013)and relapse-free survival(RFS)(P=0.015).The predictive performance of CONUT was superior to the modified Glasgow prognostic score,the prognostic nutritional index,and the neutrophil-to-lymphocyte ratio.Meanwhile,the predictive performances of CONUT+tumor node metastasis(TNM)stage for 3-year OS[area under the receiver operating characteristics curve(AUC)=0.803]and 3-year RFS(AUC=0.752)were no less than skeletal muscle mass index(SMI)+TNM stage.The CONUT score was negatively correlated with SMI(P<0.01).CONCLUSION As a nutritional indicator,the CONUT score could predict long-term outcomes after radical surgery for colorectal cancer,and its predictive ability was superior to other indexes.The correlation between the CONUT score and skeletal muscle may be one of the factors that play a predictive role.
基金supported by Science and Technology Plan Project of Zhejiang Provincial Department of Transportation“Research and System Development of Highway Asset Digitalization Technology inUse Based onHigh-PrecisionMap”(Project Number:202203)in part by Science and Technology Plan Project of Zhejiang Provincial Department of Transportation:Research and Demonstration Application of Key Technologies for Precise Sensing of Expressway Thrown Objects(No.202204).
文摘Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.
文摘BACKGROUND Gastric cancer is the third most common cause of cancer related death worldwide.Surgery with or without chemotherapy is the most common approach with curative intent;however,the prognosis is poor as mortality rates remain high.Several indexes have been proposed in the past few years in order to estimate the survival of patients undergoing gastrectomy.The preoperative nutritional status of gastric cancer patients has recently gained attention as a factor that could affect the postoperative course and various indexes have been developed.The aim of this systematic review was to assess the role of the prognostic nutritional index(PNI)in predicting the survival of patients with gastric or gastroesophageal adenocarcinoma who underwent gastrectomy with curative intent.AIM To investigate the role of PNI in predicting the survival of patients with gastric or gastroesophageal junction adenocarcinoma.METHODS A thorough literature search of PubMed and the Cochrane library was performed for studies comparing the overall survival(OS)of patients with gastric or gastroesophageal cancer after surgical resection depending on the preoperative PNI value.The PRISMA algorithm was used in the screening process and finally 16 studies were included in this systematic review.The review protocol was registered in the International Prospective Register of Systematic Reviews(PRO) RESULTS Sixteen studies involving 14551 patients with gastric or esophagogastric junction adenocarcinoma undergoing open or laparoscopic or robotic gastrectomy with or without adjuvant chemotherapy were included in this systematic review.The patients were divided into high-and low-PNI groups according to cut-off values that were set according to previous reports or by using receiver operating characteristic curve analysis in each individual study.The 5-year OS of patients in the low-PNI groups ranged between 39%and 70.6%,while in the high-PNI groups,it ranged between 54.9%and 95.8%.In most of the included studies,patients with high preoperative PNI showed statistically significant better OS than the low PNI groups.In multivariate analyses,low PNI was repeatedly recognised as an independent prognostic factor for poor survival.CONCLUSION According to the present study,low preoperative PNI seems to be an indicator of poor OS of patients undergoing gastrectomy for gastric or gastroesophageal cancer.
