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
Shen et al’s retrospective study aims to compare the utility of two separate scoring systems for predicting mortality attributable to gastrointestinal(GI)injury in critically ill patients[the GI Dysfunction Score(GID...Shen et al’s retrospective study aims to compare the utility of two separate scoring systems for predicting mortality attributable to gastrointestinal(GI)injury in critically ill patients[the GI Dysfunction Score(GIDS)and the Acute Gastroin-testinal Injury(AGI)grade].The authors note that this study is the first proposal that suggests an equivalence between the ability of both scores to predict mor-tality at 28 days from intensive care unit(ICU)admission.Shen et al retrospec-tively analysed an ICU cohort of patients utilising two physicians administering both the AGI grade and GIDS score,using electronic healthcare records and ICU flowsheets.Where these physicians disagreed about the scores,the final decision as to the scores was made by an associate chief physician,or chief physician.We note that the primary reason for the development of GIDS was to create a clear score for GI dysfunction,with minimal subjectivity or inter-operator variability.The subjectivity inherent to the older AGI grading system is what ultimately led to the development of GIDS in 2021.By ensuring consensus between physicians administering the AGI,Shen et al have controlled for one of this grading systems biggest issues.We have concerns,however,that this does not represent the real-world challenges associated with applying the AGI compared to the newer GIDS,and wonder if this arbitration process had not been instituted,would the two scoring systems remain equivalent in terms of predicted mortality?展开更多
Background: Primary non-function(PNF) and early allograft failure(EAF) after liver transplantation(LT) seriously affect patient outcomes. In clinical practice, effective prognostic tools for early identifying recipien...Background: Primary non-function(PNF) and early allograft failure(EAF) after liver transplantation(LT) seriously affect patient outcomes. In clinical practice, effective prognostic tools for early identifying recipients at high risk of PNF and EAF were urgently needed. Recently, the Model for Early Allograft Function(MEAF), PNF score by King's College(King-PNF) and Balance-and-Risk-Lactate(BAR-Lac) score were developed to assess the risks of PNF and EAF. This study aimed to externally validate and compare the prognostic performance of these three scores for predicting PNF and EAF. Methods: A retrospective study included 720 patients with primary LT between January 2015 and December 2020. MEAF, King-PNF and BAR-Lac scores were compared using receiver operating characteristic(ROC) and the net reclassification improvement(NRI) and integrated discrimination improvement(IDI) analyses. Results: Of all 720 patients, 28(3.9%) developed PNF and 67(9.3%) developed EAF in 3 months. The overall early allograft dysfunction(EAD) rate was 39.0%. The 3-month patient mortality was 8.6% while 1-year graft-failure-free survival was 89.2%. The median MEAF, King-PNF and BAR-Lac scores were 5.0(3.5–6.3),-2.1(-2.6 to-1.2), and 5.0(2.0–11.0), respectively. For predicting PNF, MEAF and King-PNF scores had excellent area under curves(AUCs) of 0.872 and 0.891, superior to BAR-Lac(AUC = 0.830). The NRI and IDI analyses confirmed that King-PNF score had the best performance in predicting PNF while MEAF served as a better predictor of EAD. The EAF risk curve and 1-year graft-failure-free survival curve showed that King-PNF was superior to MEAF and BAR-Lac scores for stratifying the risk of EAF. Conclusions: MEAF, King-PNF and BAR-Lac were validated as practical and effective risk assessment tools of PNF. King-PNF score outperformed MEAF and BAR-Lac in predicting PNF and EAF within 6 months. BAR-Lac score had a huge advantage in the prediction for PNF without post-transplant variables. Proper use of these scores will help early identify PNF, standardize grading of EAF and reasonably select clinical endpoints in relative studies.展开更多
BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)placement is a procedure that can effectively treat complications of portal hypertension,such as variceal bleeding and refractory ascites.However,there hav...BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)placement is a procedure that can effectively treat complications of portal hypertension,such as variceal bleeding and refractory ascites.However,there have been no specific studies on predicting long-term survival after TIPS placement.AIM To establish a model to predict long-term survival in patients with hepatitis cirrhosis after TIPS.METHODS A retrospective analysis was conducted on a cohort of 224 patients who un-derwent TIPS implantation.Through univariate and multivariate Cox regression analyses,various factors were examined for their ability to predict survival at 6 years after TIPS.Consequently,a composite score was formulated,encompassing the indication,shunt reasonability,portal venous pressure gradient(PPG)after TIPS,percentage decrease in portal venous pressure(PVP),indocyanine green retention rate at 15 min(ICGR15)and total bilirubin(Tbil)level.Furthermore,the performance of the newly developed Cox(NDC)model was evaluated in an in-ternal validation cohort and compared with that of a series of existing models.RESULTS The indication(variceal bleeding or ascites),shunt reasonability(reasonable or unreasonable),ICGR15,post-operative PPG,percentage of PVP decrease and Tbil were found to be independent factors affecting long-term survival after TIPS placement.The NDC model incorporated these parameters and successfully identified patients at high risk,exhibiting a notably elevated mortality rate following the TIPS procedure,as observed in both the training and validation cohorts.Additionally,in terms of predicting the long-term survival rate,the performance of the NDC model was significantly better than that of the other four models[Child-Pugh,model for end-stage liver disease(MELD),MELD-sodium and the Freiburg index of post-TIPS survival].CONCLUSION The NDC model can accurately predict long-term survival after the TIPS procedure in patients with hepatitis cirrhosis,help identify high-risk patients and guide follow-up management after TIPS implantation.展开更多
Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectiv...Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.展开更多
Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiv...Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiveness of sinter quality prediction,an intelligent flare monitoring system for sintering machine tails that combines hybrid neural networks integrating convolutional neural network with long short-term memory(CNN-LSTM)networks was proposed.The system utilized a high-temperature thermal imager for image acquisition at the sintering machine tail and employed a zone-triggered method to accurately capture dynamic feature images under challenging conditions of high-temperature,high dust,and occlusion.The feature images were then segmented through a triple-iteration multi-thresholding approach based on the maximum between-class variance method to minimize detail loss during the segmentation process.Leveraging the advantages of CNN and LSTM networks in capturing temporal and spatial information,a comprehensive model for sinter quality prediction was constructed,with inputs including the proportion of combustion layer,porosity rate,temperature distribution,and image features obtained from the convolutional neural network,and outputs comprising quality indicators such as underburning index,uniformity index,and FeO content of the sinter.The accuracy is notably increased,achieving a 95.8%hit rate within an error margin of±1.0.After the system is applied,the average qualified rate of FeO content increases from 87.24%to 89.99%,representing an improvement of 2.75%.The average monthly solid fuel consumption is reduced from 49.75 to 46.44 kg/t,leading to a 6.65%reduction and underscoring significant energy saving and cost reduction effects.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
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
Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of c...Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of contemporary healthcare systems and a growing patient populace call for advanced forecasting models.Factors like technological advancements,novel treatment protocols,and the increasing prevalence of chronic illnesses have diminished the efficacy of traditional estimation approaches.Novel forecasting methodologies,including time-series analysis,machine learning,and simulation-based techniques,have been developed to tackle these challenges.Time-series analysis recognizes patterns from past data,whereas machine learning uses extensive datasets to uncover concealed trends.Simulation models are employed to assess diverse scenarios,assisting in proactive adjustments to staffing.These techniques offer distinct advantages,such as the identification of seasonal patterns,the management of large datasets,and the ability to test various assumptions.By integrating these sophisticated models into workforce planning,organizations can optimize staffing,reduce financial waste,and elevate the standard of patient care.As the healthcare field progresses,the utilization of these predictive models will be pivotal for fostering adaptable and resilient workforce management.展开更多
With the rapid development of economy,air pollution caused by industrial expansion has caused serious harm to human health and social development.Therefore,establishing an effective air pollution concentration predict...With the rapid development of economy,air pollution caused by industrial expansion has caused serious harm to human health and social development.Therefore,establishing an effective air pollution concentration prediction system is of great scientific and practical significance for accurate and reliable predictions.This paper proposes a combination of pointinterval prediction system for pollutant concentration prediction by leveraging neural network,meta-heuristic optimization algorithm,and fuzzy theory.Fuzzy information granulation technology is used in data preprocessing to transform numerical sequences into fuzzy particles for comprehensive feature extraction.The golden Jackal optimization algorithm is employed in the optimization stage to fine-tune model hyperparameters.In the prediction stage,an ensemble learning method combines training results frommultiplemodels to obtain final point predictions while also utilizing quantile regression and kernel density estimation methods for interval predictions on the test set.Experimental results demonstrate that the combined model achieves a high goodness of fit coefficient of determination(R^(2))at 99.3% and a maximum difference between prediction accuracy mean absolute percentage error(MAPE)and benchmark model at 12.6%.This suggests that the integrated learning system proposed in this paper can provide more accurate deterministic predictions as well as reliable uncertainty analysis compared to traditionalmodels,offering practical reference for air quality early warning.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
Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-value...Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-values of O^(1)D,NO_(2),HONO,H_(2)O_(2),HCHO,and NO_(3),which are the crucial values for the prediction of the atmospheric oxidation capacity(AOC)and secondary pollutant concentrations such as ozone(O_(3)),secondary organic aerosols(SOA).The J-ML can self-select the optimal“Model+Hyperparameters”without human interference.The evaluated results showed that the J-ML had a good performance to reproduce the J-values wheremost of the correlation(R)coefficients exceed 0.93 and the accuracy(P)values are in the range of 0.68-0.83,comparing with the J-values from observations and from the tropospheric ultraviolet and visible(TUV)radiation model in Beijing,Chengdu,Guangzhou and Shanghai,China.The hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days,respectively.Compared with O_(3)concentrations by using J-values from the TUV model,an emission-driven observation-based model(e-OBM)by using the J-values from the J-ML showed a 4%-12%increase in R and 4%-30%decrease in ME,indicating that the J-ML could be used as an excellent supplement to traditional numerical models.The feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values,and the other dominant factors for all J-values were 2-m mean temperature,O_(3),total cloud cover,boundary layer height,relative humidity and surface pressure.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
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
Objective This systematic review aimed to identify the predictors of recovery from dysphagia after stroke in the last ten years,thereby providing an evidence-based basis for nurses to identify high-risk patients and d...Objective This systematic review aimed to identify the predictors of recovery from dysphagia after stroke in the last ten years,thereby providing an evidence-based basis for nurses to identify high-risk patients and develop individualized rehabilitation plans to improve patient prognosis.Methods Databases including the China National Knowledge Infrastructure(CNKI),China Biology Medicine disc(CBMdisc),China Science and Technology Journal(VIP),WanFang,PubMed,Embase,CINAHL,Web of Science,the Cochrane Library,and Scopus were retrieved to search for literature on the predictors of recovery from dysphagia after stroke.The retrieval period was from January 2013 to December 2023.The quality of studies was assessed using the Newcastle-Ottawa Scale(NOS)and the Prediction model Risk of Bias Assessment Tool(PROBAST).Meta-analysis was performed using Revman5.3 and Stata15.1 software.The review protocol has been registered with PROSPERO(CRD42024605570).Results A total of 1,216 results were obtained,including 599 in English and 617 in Chinese.A total of 34 studies were included,involving 156,309 patients with post-stroke dysphagia,and the rate of dysphagia recovery increased from 13.53%at 1 week to 95%at 6 months after stroke.Meta-analysis results showed that older age[OR=1.06,95%CI(1.04,1.08),P<0.001],lower BMI[OR=1.28,95%CI(1.17,1.40),P<0.001],bilateral stroke[OR=3.10,95%CI(2.04,4.72),P<0.001],higher National Institutes of Health Stroke Scale(NIHSS)score[OR=1.19,95%CI(1.01,1.39),P=0.030],tracheal intubation[OR=5.08,95%CI(1.57,16.39),P=0.007]and aspiration[OR=4.70,95%CI(3.06,7.20),P<0.001]were unfavorable factors for the recovery of swallowing function in patients with post-stroke dysphagia.Conclusions The lack of standardized criteria for rehabilitation assessment of post-stroke dysphagia has resulted in reported recovery rates of swallowing function exhibiting wide variability.Nurses should take targeted preventive measures for patients aged≥70 years,low BMI,bilateral stroke,high NIHSS score,tracheal intubation,and aspiration to promote the recovery of swallowing function in patients with post-stroke dysphagia.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
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Shen et al’s retrospective study aims to compare the utility of two separate scoring systems for predicting mortality attributable to gastrointestinal(GI)injury in critically ill patients[the GI Dysfunction Score(GIDS)and the Acute Gastroin-testinal Injury(AGI)grade].The authors note that this study is the first proposal that suggests an equivalence between the ability of both scores to predict mor-tality at 28 days from intensive care unit(ICU)admission.Shen et al retrospec-tively analysed an ICU cohort of patients utilising two physicians administering both the AGI grade and GIDS score,using electronic healthcare records and ICU flowsheets.Where these physicians disagreed about the scores,the final decision as to the scores was made by an associate chief physician,or chief physician.We note that the primary reason for the development of GIDS was to create a clear score for GI dysfunction,with minimal subjectivity or inter-operator variability.The subjectivity inherent to the older AGI grading system is what ultimately led to the development of GIDS in 2021.By ensuring consensus between physicians administering the AGI,Shen et al have controlled for one of this grading systems biggest issues.We have concerns,however,that this does not represent the real-world challenges associated with applying the AGI compared to the newer GIDS,and wonder if this arbitration process had not been instituted,would the two scoring systems remain equivalent in terms of predicted mortality?
基金supported by grants from the National Nat-ural Science Foundation of China (81570587 and 81700557)the Guangdong Provincial Key Laboratory Construction Projection on Organ Donation and Transplant Immunology (2013A061401007 and 2017B030314018)+3 种基金Guangdong Provincial Natural Science Funds for Major Basic Science Culture Project (2015A030308010)Science and Technology Program of Guangzhou (201704020150)the Natural Science Foundations of Guangdong province (2016A030310141 and 2020A1515010091)Young Teachers Training Project of Sun Yat-sen University (K0401068) and the Guangdong Science and Technology Innovation Strategy (pdjh2022b0010 and pdjh2023a0002)。
文摘Background: Primary non-function(PNF) and early allograft failure(EAF) after liver transplantation(LT) seriously affect patient outcomes. In clinical practice, effective prognostic tools for early identifying recipients at high risk of PNF and EAF were urgently needed. Recently, the Model for Early Allograft Function(MEAF), PNF score by King's College(King-PNF) and Balance-and-Risk-Lactate(BAR-Lac) score were developed to assess the risks of PNF and EAF. This study aimed to externally validate and compare the prognostic performance of these three scores for predicting PNF and EAF. Methods: A retrospective study included 720 patients with primary LT between January 2015 and December 2020. MEAF, King-PNF and BAR-Lac scores were compared using receiver operating characteristic(ROC) and the net reclassification improvement(NRI) and integrated discrimination improvement(IDI) analyses. Results: Of all 720 patients, 28(3.9%) developed PNF and 67(9.3%) developed EAF in 3 months. The overall early allograft dysfunction(EAD) rate was 39.0%. The 3-month patient mortality was 8.6% while 1-year graft-failure-free survival was 89.2%. The median MEAF, King-PNF and BAR-Lac scores were 5.0(3.5–6.3),-2.1(-2.6 to-1.2), and 5.0(2.0–11.0), respectively. For predicting PNF, MEAF and King-PNF scores had excellent area under curves(AUCs) of 0.872 and 0.891, superior to BAR-Lac(AUC = 0.830). The NRI and IDI analyses confirmed that King-PNF score had the best performance in predicting PNF while MEAF served as a better predictor of EAD. The EAF risk curve and 1-year graft-failure-free survival curve showed that King-PNF was superior to MEAF and BAR-Lac scores for stratifying the risk of EAF. Conclusions: MEAF, King-PNF and BAR-Lac were validated as practical and effective risk assessment tools of PNF. King-PNF score outperformed MEAF and BAR-Lac in predicting PNF and EAF within 6 months. BAR-Lac score had a huge advantage in the prediction for PNF without post-transplant variables. Proper use of these scores will help early identify PNF, standardize grading of EAF and reasonably select clinical endpoints in relative studies.
基金Supported by the Talent Training Plan during the"14th Five-Year Plan"period of Beijing Shijitan Hospital Affiliated to Capital Medical University,No.2023LJRCLFQ.
文摘BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)placement is a procedure that can effectively treat complications of portal hypertension,such as variceal bleeding and refractory ascites.However,there have been no specific studies on predicting long-term survival after TIPS placement.AIM To establish a model to predict long-term survival in patients with hepatitis cirrhosis after TIPS.METHODS A retrospective analysis was conducted on a cohort of 224 patients who un-derwent TIPS implantation.Through univariate and multivariate Cox regression analyses,various factors were examined for their ability to predict survival at 6 years after TIPS.Consequently,a composite score was formulated,encompassing the indication,shunt reasonability,portal venous pressure gradient(PPG)after TIPS,percentage decrease in portal venous pressure(PVP),indocyanine green retention rate at 15 min(ICGR15)and total bilirubin(Tbil)level.Furthermore,the performance of the newly developed Cox(NDC)model was evaluated in an in-ternal validation cohort and compared with that of a series of existing models.RESULTS The indication(variceal bleeding or ascites),shunt reasonability(reasonable or unreasonable),ICGR15,post-operative PPG,percentage of PVP decrease and Tbil were found to be independent factors affecting long-term survival after TIPS placement.The NDC model incorporated these parameters and successfully identified patients at high risk,exhibiting a notably elevated mortality rate following the TIPS procedure,as observed in both the training and validation cohorts.Additionally,in terms of predicting the long-term survival rate,the performance of the NDC model was significantly better than that of the other four models[Child-Pugh,model for end-stage liver disease(MELD),MELD-sodium and the Freiburg index of post-TIPS survival].CONCLUSION The NDC model can accurately predict long-term survival after the TIPS procedure in patients with hepatitis cirrhosis,help identify high-risk patients and guide follow-up management after TIPS implantation.
基金supported in part by the National Natural Science Foundation of China(62173255,62188101)Shenzhen Key Laboratory of Control Theory and Intelligent Systems(ZDSYS20220330161800001)
文摘Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.
基金founded by the Open Project Program of Anhui Province Key Laboratory of Metallurgical Engineering and Resources Recycling(Anhui University of Technology)(No.SKF21-06)Research Fund for Young Teachers of Anhui University of Technology in 2020(No.QZ202001).
文摘Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiveness of sinter quality prediction,an intelligent flare monitoring system for sintering machine tails that combines hybrid neural networks integrating convolutional neural network with long short-term memory(CNN-LSTM)networks was proposed.The system utilized a high-temperature thermal imager for image acquisition at the sintering machine tail and employed a zone-triggered method to accurately capture dynamic feature images under challenging conditions of high-temperature,high dust,and occlusion.The feature images were then segmented through a triple-iteration multi-thresholding approach based on the maximum between-class variance method to minimize detail loss during the segmentation process.Leveraging the advantages of CNN and LSTM networks in capturing temporal and spatial information,a comprehensive model for sinter quality prediction was constructed,with inputs including the proportion of combustion layer,porosity rate,temperature distribution,and image features obtained from the convolutional neural network,and outputs comprising quality indicators such as underburning index,uniformity index,and FeO content of the sinter.The accuracy is notably increased,achieving a 95.8%hit rate within an error margin of±1.0.After the system is applied,the average qualified rate of FeO content increases from 87.24%to 89.99%,representing an improvement of 2.75%.The average monthly solid fuel consumption is reduced from 49.75 to 46.44 kg/t,leading to a 6.65%reduction and underscoring significant energy saving and cost reduction effects.
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Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of contemporary healthcare systems and a growing patient populace call for advanced forecasting models.Factors like technological advancements,novel treatment protocols,and the increasing prevalence of chronic illnesses have diminished the efficacy of traditional estimation approaches.Novel forecasting methodologies,including time-series analysis,machine learning,and simulation-based techniques,have been developed to tackle these challenges.Time-series analysis recognizes patterns from past data,whereas machine learning uses extensive datasets to uncover concealed trends.Simulation models are employed to assess diverse scenarios,assisting in proactive adjustments to staffing.These techniques offer distinct advantages,such as the identification of seasonal patterns,the management of large datasets,and the ability to test various assumptions.By integrating these sophisticated models into workforce planning,organizations can optimize staffing,reduce financial waste,and elevate the standard of patient care.As the healthcare field progresses,the utilization of these predictive models will be pivotal for fostering adaptable and resilient workforce management.
基金supported by General Scientific Research Funding of the Science and Technology Development Fund(FDCT)in Macao(No.0150/2022/A)the Faculty Research Grants of Macao University of Science and Technology(No.FRG-22-074-FIE).
文摘With the rapid development of economy,air pollution caused by industrial expansion has caused serious harm to human health and social development.Therefore,establishing an effective air pollution concentration prediction system is of great scientific and practical significance for accurate and reliable predictions.This paper proposes a combination of pointinterval prediction system for pollutant concentration prediction by leveraging neural network,meta-heuristic optimization algorithm,and fuzzy theory.Fuzzy information granulation technology is used in data preprocessing to transform numerical sequences into fuzzy particles for comprehensive feature extraction.The golden Jackal optimization algorithm is employed in the optimization stage to fine-tune model hyperparameters.In the prediction stage,an ensemble learning method combines training results frommultiplemodels to obtain final point predictions while also utilizing quantile regression and kernel density estimation methods for interval predictions on the test set.Experimental results demonstrate that the combined model achieves a high goodness of fit coefficient of determination(R^(2))at 99.3% and a maximum difference between prediction accuracy mean absolute percentage error(MAPE)and benchmark model at 12.6%.This suggests that the integrated learning system proposed in this paper can provide more accurate deterministic predictions as well as reliable uncertainty analysis compared to traditionalmodels,offering practical reference for air quality early warning.
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 National Key Project of the Ministry of Science and Technology of China(No.2022YFC3701200)the National Natural Science Foundation of China(No.42090030).
文摘Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-values of O^(1)D,NO_(2),HONO,H_(2)O_(2),HCHO,and NO_(3),which are the crucial values for the prediction of the atmospheric oxidation capacity(AOC)and secondary pollutant concentrations such as ozone(O_(3)),secondary organic aerosols(SOA).The J-ML can self-select the optimal“Model+Hyperparameters”without human interference.The evaluated results showed that the J-ML had a good performance to reproduce the J-values wheremost of the correlation(R)coefficients exceed 0.93 and the accuracy(P)values are in the range of 0.68-0.83,comparing with the J-values from observations and from the tropospheric ultraviolet and visible(TUV)radiation model in Beijing,Chengdu,Guangzhou and Shanghai,China.The hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days,respectively.Compared with O_(3)concentrations by using J-values from the TUV model,an emission-driven observation-based model(e-OBM)by using the J-values from the J-ML showed a 4%-12%increase in R and 4%-30%decrease in ME,indicating that the J-ML could be used as an excellent supplement to traditional numerical models.The feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values,and the other dominant factors for all J-values were 2-m mean temperature,O_(3),total cloud cover,boundary layer height,relative humidity and surface pressure.
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.
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Objective This systematic review aimed to identify the predictors of recovery from dysphagia after stroke in the last ten years,thereby providing an evidence-based basis for nurses to identify high-risk patients and develop individualized rehabilitation plans to improve patient prognosis.Methods Databases including the China National Knowledge Infrastructure(CNKI),China Biology Medicine disc(CBMdisc),China Science and Technology Journal(VIP),WanFang,PubMed,Embase,CINAHL,Web of Science,the Cochrane Library,and Scopus were retrieved to search for literature on the predictors of recovery from dysphagia after stroke.The retrieval period was from January 2013 to December 2023.The quality of studies was assessed using the Newcastle-Ottawa Scale(NOS)and the Prediction model Risk of Bias Assessment Tool(PROBAST).Meta-analysis was performed using Revman5.3 and Stata15.1 software.The review protocol has been registered with PROSPERO(CRD42024605570).Results A total of 1,216 results were obtained,including 599 in English and 617 in Chinese.A total of 34 studies were included,involving 156,309 patients with post-stroke dysphagia,and the rate of dysphagia recovery increased from 13.53%at 1 week to 95%at 6 months after stroke.Meta-analysis results showed that older age[OR=1.06,95%CI(1.04,1.08),P<0.001],lower BMI[OR=1.28,95%CI(1.17,1.40),P<0.001],bilateral stroke[OR=3.10,95%CI(2.04,4.72),P<0.001],higher National Institutes of Health Stroke Scale(NIHSS)score[OR=1.19,95%CI(1.01,1.39),P=0.030],tracheal intubation[OR=5.08,95%CI(1.57,16.39),P=0.007]and aspiration[OR=4.70,95%CI(3.06,7.20),P<0.001]were unfavorable factors for the recovery of swallowing function in patients with post-stroke dysphagia.Conclusions The lack of standardized criteria for rehabilitation assessment of post-stroke dysphagia has resulted in reported recovery rates of swallowing function exhibiting wide variability.Nurses should take targeted preventive measures for patients aged≥70 years,low BMI,bilateral stroke,high NIHSS score,tracheal intubation,and aspiration to promote the recovery of swallowing function in patients with post-stroke dysphagia.
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.
