Feasibility of oil production predicting results influences the annual planning and long-term field development plan of oil field, so the selection of predicting models plays a core role. In this paper, three differen...Feasibility of oil production predicting results influences the annual planning and long-term field development plan of oil field, so the selection of predicting models plays a core role. In this paper, three different predicting models are introduced, they are neural network model, support vector machine model and GM (1, 1) model. By using these three different models to predict the oil production in XINJIANG oilfield in China, advantages and disadvantages of these models have been discussed. The predicting results show: the fitting accuracy by the neural network model or by the support vector machine model is higher than GM (1, 1) model, the prediction error is smaller than 10%, so neural network model and support vector machine model can be used to short-term forecast of oil production;predicting accuracy by GM (1, 1) model is not good, but the curve trend with GM (1, 1) model is consistent with the downward trend in oil production, so GM (1, 1) predicting model can be used to long-term prediction of oil production.展开更多
Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutua...Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutual feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic approximation, and can describe any non linear dynamic system. After the structure and mathematical description being given, dynamic back propagation (BP) algorithm of training weights of Elman neural network is deduced. At last, the network is used to predict ash content of black amber in jigging production process. The results show that this neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex production process.展开更多
A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorith...A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.展开更多
Objective:To determine the clinical characteristics and prognosis of primary tracheobronchial tumors(PTTs)in children,and to explore the most common tumor identification methods.Methods:The medical records of children...Objective:To determine the clinical characteristics and prognosis of primary tracheobronchial tumors(PTTs)in children,and to explore the most common tumor identification methods.Methods:The medical records of children with PTTs who were hospitalized at the Children's Hospital of Chongqing Medical University from January 1995 to January 2020 were reviewed retrospectively.The clinical features,imaging,treatments,and outcomes of these patients were statistically analyzed.Machine learning techniques such as Gaussian na?ve Bayes,support vector machine(SVM)and decision tree models were used to identify mucoepidermoid carcinoma(ME).Results:A total of 16 children were hospitalized with PTTs during the study period.This included 5(31.3%)children with ME,3(18.8%)children with inflammatory myofibroblastic tumors(IMT),2 children(12.5%)with sarcomas,2(12.5%)children with papillomatosis and 1 child(6.3%)each with carcinoid carcinoma,adenoid cystic carcinoma(ACC),hemangioma,and schwannoma,respectively.ME was the most common tumor type and amongst the 3 ME recognition methods,the SVM model showed the best performance.The main clinical symptoms of PPTs were cough(81.3%),breathlessness(50%),wheezing(43.8%),progressive dyspnea(37.5%),hemoptysis(37.5%),and fever(25%).Of the 16 patients,7 were treated with surgery,8 underwent bronchoscopic tumor resection,and 1 child died.Of the 11 other children,3 experienced recurrence,and the last 8 remained disease-free.No deaths were observed during the follow-up period.Conclusion:PTT are very rare in children and the highest percentage of cases is due to ME.The SVM model was highly accurate in identifying ME.Chest CT and bronchoscopy can effectively diagnose PTTs.Surgery and bronchoscopic intervention can both achieve good clinical results and the prognosis of the 11 children that were followed up was good.展开更多
Fluvial sediment transport data is a very important data for effective water resource management.However,acquiring this data is expensive and tedious hence sediment yield modeling has become an alternative approach in...Fluvial sediment transport data is a very important data for effective water resource management.However,acquiring this data is expensive and tedious hence sediment yield modeling has become an alternative approach in estimating river sediment yields.In Ghana,several sediment yield predicting models have been developed to estimate the sediment yields of ungauged rivers including the Pra River Basin.In this paper,10 months sediment yield data of the Pra River Basin was used to evaluate the existing sediment yield predicting models of Ghana.A regression analysis between predicted sediment yield data derived from the models and the observed suspended sediment yields of the Pra Basin was done to determine the extent of estimation of observed sediment yields.The prediction of suspended sediment yield was done for 4 out of 5 existing sediment yield predicting models in Ghana.There were variations in sediment yield between observed and predicted suspended sediments.All predicted sediment yields were lower than observed data except for equation 3 where the results were mixed.All models were found to be good estimators of fluvial sediments with the best model being equation 4.Sediment yield tends to increase with drainage basin area.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
The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key target.Previous studies have shown th...The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key target.Previous studies have shown that deep learning methods possess a certain level of superiority in predicting ENSO indices.The present study develops a deep learning model for predicting the spatial pattern of sea surface temperature anomalies(SSTAs)in the equatorial Pacific by training a convolutional neural network(CNN)model with historical simulations from CMIP6 models.Compared with dynamical models,the CNN model has higher skill in predicting the SSTAs in the equatorial western-central Pacific,but not in the eastern Pacific.The CNN model can successfully capture the small-scale precursors in the initial SSTAs for the development of central Pacific ENSO to distinguish the spatial mode up to a lead time of seven months.A fusion model combining the predictions of the CNN model and the dynamical models achieves higher skill than each of them for both central and eastern Pacific ENSO.展开更多
When the mining goaf is close to the cliff,rock slope subsidence induced by underground mining is significantly affected by its boundary conditions.In this study,an analytical method is proposed by considering the key...When the mining goaf is close to the cliff,rock slope subsidence induced by underground mining is significantly affected by its boundary conditions.In this study,an analytical method is proposed by considering the key strata as a semi-infinite Euler-Bernoulli beam rested on a Winkler foundation with a local subsidence area.The analytical solutions of deflection are derived by analyzing the boundary and continuity conditions of the cliff.Then,the analytical solutions are verified by the results from experimental tests,FEM and InSAR,respectively.After that,the influence of changing parameters on deflections is studied with sensitivity analysis.The results show that the distance between goaf and cliff significantly affects the deflection of semi-infinite beam.The response of semi-infinite beam is obviously determined by the length of goaf and the bending stiffness of beam.The comparisons between semi-infinite beam and infinite beam illustrate the ascendancy of the improved model in such problems.展开更多
BACKGROUND Acute myocardial infarction(AMI)is a severe cardiovascular disease caused by the blockage of coronary arteries that leads to ischemic necrosis of the myocardium.Timely medical contact is critical for succes...BACKGROUND Acute myocardial infarction(AMI)is a severe cardiovascular disease caused by the blockage of coronary arteries that leads to ischemic necrosis of the myocardium.Timely medical contact is critical for successful AMI treatment,and delays increase the risk of death for patients.Pre-hospital delay time(PDT)is a significant challenge for reducing treatment times,as identifying high-risk patients with AMI remains difficult.This study aims to construct a risk prediction model to identify high-risk patients and develop targeted strategies for effective and prompt care,ultimately reducing PDT and improving treatment outcomes.AIM To construct a nomogram model for forecasting pre-hospital delay(PHD)likelihood in patients with AMI and to assess the precision of the nomogram model in predicting PHD risk.METHODS A retrospective cohort design was employed to investigate predictive factors for PHD in patients with AMI diagnosed between January 2022 and September 2022.The study included 252 patients,with 180 randomly assigned to the development group and the remaining 72 to the validation group in a 7:3 ratio.Independent risk factors influencing PHD were identified in the development group,leading to the establishment of a nomogram model for predicting PHD in patients with AMI.The model's predictive performance was evaluated using the receiver operating characteristic curve in both the development and validation groups.RESULTS Independent risk factors for PHD in patients with AMI included living alone,hyperlipidemia,age,diabetes mellitus,and digestive system diseases(P<0.05).A characteristic curve analysis indicated area under the receiver operating characteristic curve values of 0.787(95%confidence interval:0.716–0.858)and 0.770(95%confidence interval:0.660-0.879)in the development and validation groups,respectively,demonstrating the model's good discriminatory ability.The Hosmer–Lemeshow goodness-of-fit test revealed no statistically significant disparity between the anticipated and observed incidence of PHD in both development and validation cohorts(P>0.05),indicating satisfactory model calibration.CONCLUSION The nomogram model,developed with independent risk factors,accurately forecasts PHD likelihood in AMI individuals,enabling efficient identification of PHD risk in these patients.展开更多
Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid ...Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural damage.Therefore,the development of reliable prediction models for rock bursts is paramount to mitigating these hazards.This study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the LightGBMmodel.Two population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM model.Finally,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock bursts.The results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM model.The developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,respectively.The sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.展开更多
To date,few models are available in the literature to consider the creep behavior of geosynthetics when predicting the lateral deformation(d)of geosynthetics-reinforced soil(GRS)retaining walls.In this study,a general...To date,few models are available in the literature to consider the creep behavior of geosynthetics when predicting the lateral deformation(d)of geosynthetics-reinforced soil(GRS)retaining walls.In this study,a general hyperbolic creep model was first introduced to describe the long-term deformation of geosynthetics,which is a function of elapsed time and two empirical parameters a and b.The conventional creep tests with three different tensile loads(Pr)were conducted on two uniaxial geogrids to determine their creep behavior,as well as the a-Pr and b-Pr relationships.The test results show that increasing Pr accelerates the development of creep deformation for both geogrids.Meanwhile,a and b respectively show exponential and negatively linear relationships with Pr,which were confirmed by abundant experimental data available in other studies.Based on the above creep model and relationships,an accurate and reliable analytical model was then proposed for predicting the time-dependent d of GRS walls with modular block facing,which was further validated using a relevant numerical investigation from the previous literature.Performance evaluation and comparison of the proposed model with six available prediction models were performed.Then a parametric study was carried out to evaluate the effects of wall height,vertical spacing of geogrids,unit weight and internal friction angle of backfills,and factor of safety against pullout on d at the end of construction and 5 years afterwards.The findings show that the creep effect not only promotes d but also raises the elevation of the maximum d along the wall height.Finally,the limitations and application prospects of the proposed model were discussed and analyzed.展开更多
After the excavation of the roadway,the original stress balance is destroyed,resulting in the redistribution of stress and the formation of an excavation damaged zone(EDZ)around the roadway.The thickness of EDZ is the...After the excavation of the roadway,the original stress balance is destroyed,resulting in the redistribution of stress and the formation of an excavation damaged zone(EDZ)around the roadway.The thickness of EDZ is the key basis for roadway stability discrimination and support structure design,and it is of great engineering significance to accurately predict the thickness of EDZ.Considering the advantages of machine learning(ML)in dealing with high-dimensional,nonlinear problems,a hybrid prediction model based on the random forest(RF)algorithm is developed in this paper.The model used the dragonfly algorithm(DA)to optimize two hyperparameters in RF,namely mtry and ntree,and used mean absolute error(MAE),rootmean square error(RMSE),determination coefficient(R^(2)),and variance accounted for(VAF)to evaluatemodel prediction performance.A database containing 217 sets of data was collected,with embedding depth(ED),drift span(DS),surrounding rock mass strength(RMS),joint index(JI)as input variables,and the excavation damaged zone thickness(EDZT)as output variable.In addition,four classic models,back propagation neural network(BPNN),extreme learning machine(ELM),radial basis function network(RBF),and RF were compared with the DA-RF model.The results showed that the DARF mold had the best prediction performance(training set:MAE=0.1036,RMSE=0.1514,R^(2)=0.9577,VAF=94.2645;test set:MAE=0.1115,RMSE=0.1417,R^(2)=0.9423,VAF=94.0836).The results of the sensitivity analysis showed that the relative importance of each input variable was DS,ED,RMS,and JI from low to high.展开更多
BACKGROUND Surgical resection is the primary treatment for hepatocellular carcinoma(HCC).However,studies indicate that nearly 70%of patients experience HCC recurrence within five years following hepatectomy.The earlie...BACKGROUND Surgical resection is the primary treatment for hepatocellular carcinoma(HCC).However,studies indicate that nearly 70%of patients experience HCC recurrence within five years following hepatectomy.The earlier the recurrence,the worse the prognosis.Current studies on postoperative recurrence primarily rely on postoperative pathology and patient clinical data,which are lagging.Hence,developing a new pre-operative prediction model for postoperative recurrence is crucial for guiding individualized treatment of HCC patients and enhancing their prognosis.AIM To identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC.METHODS The demographic and clinical data of 371 HCC patients were collected for this retrospective study.These data were randomly divided into training and test sets at a ratio of 8:2.The training set was analyzed,and key feature variables with predictive value for early HCC recurrence were selected to construct six different machine learning prediction models.Each model was evaluated,and the bestperforming model was selected for interpreting the importance of each variable.Finally,an online calculator based on the model was generated for daily clinical practice.RESULTS Following machine learning analysis,eight key feature variables(age,intratumoral arteries,alpha-fetoprotein,preoperative blood glucose,number of tumors,glucose-to-lymphocyte ratio,liver cirrhosis,and pre-operative platelets)were selected to construct six different prediction models.The XGBoost model outperformed other models,with the area under the receiver operating characteristic curve in the training,validation,and test datasets being 0.993(95%confidence interval:0.982-1.000),0.734(0.601-0.867),and 0.706(0.585-0.827),respectively.Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value.CONCLUSION The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence.This model may guide surgical strategies and postoperative individualized medicine.展开更多
BACKGROUND Acute bleeding due to esophageal varices(EVs)is a life-threatening complication in patients with cirrhosis.The diagnosis of EVs is mainly through upper gastrointestinal endoscopy,but the discomfort,contrain...BACKGROUND Acute bleeding due to esophageal varices(EVs)is a life-threatening complication in patients with cirrhosis.The diagnosis of EVs is mainly through upper gastrointestinal endoscopy,but the discomfort,contraindications and complications of gastrointestinal endoscopic screening reduce patient compliance.According to the bleeding risk of EVs,the Baveno VI consensus divides varices into high bleeding risk EVs(HEVs)and low bleeding risk EVs(LEVs).We sought to identify a non-invasive prediction model based on spleen stiffness measurement(SSM)and liver stiffness measurement(LSM)as an alternative to EVs screening.AIM To develop a safe,simple and non-invasive model to predict HEVs in patients with viral cirrhosis and identify patients who can be exempted from upper gastrointestinal endoscopy.METHODS Data from 200 patients with viral cirrhosis were included in this study,with 140 patients as the modelling group and 60 patients as the external validation group,and the EVs types of patients were determined by upper gastrointestinal endoscopy and the Baveno Ⅵ consensus.Those patients were divided into the HEVs group(66 patients)and the LEVs group(74 patients).The effect of each parameter on HEVs was analyzed by univariate and multivariate analyses,and a noninvasive prediction model was established.Finally,the discrimination ability,calibration ability and clinical efficacy of the new model were verified in the modelling group and the external validation group.RESULTS Univariate and multivariate analyses showed that SSM and LSM were associated with the occurrence of HEVs in patients with viral cirrhosis.On this basis,logistic regression analysis was used to construct a prediction model:Ln[P/(1-P)]=-8.184-0.228×SSM+0.642×LSM.The area under the curve of the new model was 0.965.When the cut-off value was 0.27,the sensitivity,specificity,positive predictive value and negative predictive value of the model for predicting HEVs were 100.00%,82.43%,83.52%,and 100%,respectively.Compared with the four prediction models of liver stiffness-spleen diameter to platelet ratio score,variceal risk index,aspartate aminotransferase to alanine aminotransferase ratio,and Baveno VI,the established model can better predict HEVs in patients with viral cirrhosis.CONCLUSION Based on the SSM and LSM measured by transient elastography,we established a non-invasive prediction model for HEVs.The new model is reliable in predicting HEVs and can be used as an alternative to routine upper gastrointestinal endoscopy screening,which is helpful for clinical decision making.展开更多
Settlement prediction of geosynthetic-reinforced soil(GRS)abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil engineers.Hence,in this paper,a novel hybrid ar...Settlement prediction of geosynthetic-reinforced soil(GRS)abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil engineers.Hence,in this paper,a novel hybrid artificial intelligence(AI)-based model was developed by the combination of artificial neural network(ANN)and Harris hawks’optimisation(HHO),that is,ANN-HHO,to predict the settlement of the GRS abutments.Five other robust intelligent models such as support vector regression(SVR),Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimisation regression(SMOR),and least-median square regression(LMSR)were constructed and compared to the ANN-HHO model.The predictive strength,relalibility and robustness of the model were evaluated based on rigorous statistical testing,ranking criteria,multi-criteria approach,uncertainity analysis and sensitivity analysis(SA).Moreover,the predictive veracity of the model was also substantiated against several large-scale independent experimental studies on GRS abutments reported in the scientific literature.The acquired findings demonstrated that the ANN-HHO model predicted the settlement of GRS abutments with reasonable accuracy and yielded superior performance in comparison to counterpart models.Therefore,it becomes one of predictive tools employed by geotechnical/civil engineers in preliminary decision-making when investigating the in-service performance of GRS abutments.Finally,the model has been converted into a simple mathematical formulation for easy hand calculations,and it is proved cost-effective and less time-consuming in comparison to experimental tests and numerical simulations.展开更多
predicting high-risk esophageal varices based on liver and spleen stiffness".Acute bleeding caused by esophageal varices is a life-threatening complication in patients with liver cirrhosis.Due to the discomfort,c...predicting high-risk esophageal varices based on liver and spleen stiffness".Acute bleeding caused by esophageal varices is a life-threatening complication in patients with liver cirrhosis.Due to the discomfort,contraindications,and associated complications of upper gastrointestinal endoscopy screening,it is crucial to identify an imaging-based non-invasive model for predicting high-risk esophageal varices in patients with cirrhosis.展开更多
BACKGROUND Colorectal cancer(CRC)is a significant global health issue,and lymph node metastasis(LNM)is a crucial prognostic factor.Accurate prediction of LNM is essential for developing individualized treatment strate...BACKGROUND Colorectal cancer(CRC)is a significant global health issue,and lymph node metastasis(LNM)is a crucial prognostic factor.Accurate prediction of LNM is essential for developing individualized treatment strategies for patients with CRC.However,the prediction of LNM is challenging and depends on various factors such as tumor histology,clinicopathological features,and molecular characteristics.The most reliable method to detect LNM is the histopathological examination of surgically resected specimens;however,this method is invasive,time-consuming,and subject to sampling errors and interobserver variability.AIM To analyze influencing factors and develop and validate a risk prediction model for LNM in CRC based on a large patient queue.METHODS This study retrospectively analyzed 300 patients who underwent CRC surgery at two Peking University Shenzhen hospitals between January and December 2021.A deep learning approach was used to extract features potentially associated with LNM from primary tumor histological images while a logistic regression model was employed to predict LNM in CRC using machine-learning-derived features and clinicopathological variables as predictors.RESULTS The prediction model constructed for LNM in CRC was based on a logistic regression framework that incorporated machine learning-extracted features and clinicopathological variables.The model achieved high accuracy(0.86),sensitivity(0.81),specificity(0.87),positive predictive value(0.66),negative predictive value(0.94),area under the curve for the receiver operating characteristic(0.91),and a low Brier score(0.10).The model showed good agreement between the observed and predicted probabilities of LNM across a range of risk thresholds,indicating good calibration and clinical utility.CONCLUSION The present study successfully developed and validated a potent and effective risk-prediction model for LNM in patients with CRC.This model utilizes machine-learning-derived features extracted from primary tumor histology and clinicopathological variables,demonstrating superior performance and clinical applicability compared to existing models.The study provides new insights into the potential of deep learning to extract valuable information from tumor histology,in turn,improving the prediction of LNM in CRC and facilitate risk stratification and decision-making in clinical practice.展开更多
Liposome is one of the most widely used carriers for drug delivery because of the great biocompatibility and biodegradability.Due to the complex formulation components and preparation process,formulation screening mos...Liposome is one of the most widely used carriers for drug delivery because of the great biocompatibility and biodegradability.Due to the complex formulation components and preparation process,formulation screening mostly relies on trial-and-error process with low efficiency.Here liposome formulation prediction models have been built by machine learning(ML)approaches.The important parameters of liposomes,including size,polydispersity index(PDI),zeta potential and encapsulation,are predicted individually by optimal ML algorithm,while the formulation features are also ranked to provide important guidance for formulation design.The analysis of key parameter reveals that drug molecules with logS[-3,-6],molecular complexity[500,1000]and XLogP3(≥2)are priority for preparing liposome with higher encapsulation.In addition,naproxen(NAP)and palmatine HCl(PAL)represented the insoluble and water-soluble molecules are prepared as liposome formulations to validate prediction ability.The consistency between predicted and experimental value verifies the satisfied accuracy of ML models.As the drug properties are critical for liposome particles,the molecular interactions and dynamics of NAP and PAL liposome are further investigated by coarse-grained molecular dynamics simulations.The modeling structure reveals that NAP molecules could distribute into lipid layer,while most PAL molecules aggregate in the inner aqueous phase of liposome.The completely different physical state of NAP and PAL confirms the importance of drug properties for liposome formulations.In summary,the general prediction models are built to predict liposome formulations,and the impacts of key factors are analyzed by combing ML with molecular modeling.The availability and rationality of these intelligent prediction systems have been proved in this study,which could be applied for liposome formulation development in the future.展开更多
BACKGROUND Sarcopenia may be associated with hepatocellular carcinoma(HCC)following hepatectomy.But traditional single clinical variables are still insufficient to predict recurrence.We still lack effective prediction...BACKGROUND Sarcopenia may be associated with hepatocellular carcinoma(HCC)following hepatectomy.But traditional single clinical variables are still insufficient to predict recurrence.We still lack effective prediction models for recent recurrence(time to recurrence<2 years)after hepatectomy for HCC.AIM To establish an interventable prediction model to estimate recurrence-free survival(RFS)after hepatectomy for HCC based on sarcopenia.METHODS We retrospectively analyzed 283 hepatitis B-related HCC patients who underwent curative hepatectomy for the first time,and the skeletal muscle index at the third lumbar spine was measured by preoperative computed tomography.94 of these patients were enrolled for external validation.Cox multivariate analysis was per-formed to identify the risk factors of postoperative recurrence in training cohort.A nomogram model was developed to predict the RFS of HCC patients,and its predictive performance was validated.The predictive efficacy of this model was evaluated using the receiver operating characteristic curve.RESULTS Multivariate analysis showed that sarcopenia[Hazard ratio(HR)=1.767,95%CI:1.166-2.678,P<0.05],alpha-fetoprotein≥40 ng/mL(HR=1.984,95%CI:1.307-3.011,P<0.05),the maximum diameter of tumor>5 cm(HR=2.222,95%CI:1.285-3.842,P<0.05),and hepatitis B virus DNA level≥2000 IU/mL(HR=2.1,95%CI:1.407-3.135,P<0.05)were independent risk factors associated with postoperative recurrence of HCC.Based on the sarcopenia to assess the RFS model of hepatectomy with hepatitis B-related liver cancer disease(SAMD)was established combined with other the above risk factors.The area under the curve of the SAMD model was 0.782(95%CI:0.705-0.858)in the training cohort(sensitivity 81%,specificity 63%)and 0.773(95%CI:0.707-0.838)in the validation cohort.Besides,a SAMD score≥110 was better to distinguish the high-risk group of postoperative recurrence of HCC.CONCLUSION Sarcopenia is associated with recent recurrence after hepatectomy for hepatitis B-related HCC.A nutritional status-based prediction model is first established for postoperative recurrence of hepatitis B-related HCC,which is superior to other models and contributes to prognosis prediction.展开更多
With continuous hydrocarbon exploration extending to deeper basins,the deepest industrial oil accumulation was discovered below 8,200 m,revealing a new exploration field.Hence,the extent to which oil exploration can b...With continuous hydrocarbon exploration extending to deeper basins,the deepest industrial oil accumulation was discovered below 8,200 m,revealing a new exploration field.Hence,the extent to which oil exploration can be extended,and the prediction of the depth limit of oil accumulation(DLOA),are issues that have attracted significant attention in petroleum geology.Since it is difficult to characterize the evolution of the physical properties of the marine carbonate reservoir with burial depth,and the deepest drilling still cannot reach the DLOA.Hence,the DLOA cannot be predicted by directly establishing the relationship between the ratio of drilling to the dry layer and the depth.In this study,by establishing the relationships between the porosity and the depth and dry layer ratio of the carbonate reservoir,the relationships between the depth and dry layer ratio were obtained collectively.The depth corresponding to a dry layer ratio of 100%is the DLOA.Based on this,a quantitative prediction model for the DLOA was finally built.The results indicate that the porosity of the carbonate reservoir,Lower Ordovician in Tazhong area of Tarim Basin,tends to decrease with burial depth,and manifests as an overall low porosity reservoir in deep layer.The critical porosity of the DLOA was 1.8%,which is the critical geological condition corresponding to a 100%dry layer ratio encountered in the reservoir.The depth of the DLOA was 9,000 m.This study provides a new method for DLOA prediction that is beneficial for a deeper understanding of oil accumulation,and is of great importance for scientific guidance on deep oil drilling.展开更多
文摘Feasibility of oil production predicting results influences the annual planning and long-term field development plan of oil field, so the selection of predicting models plays a core role. In this paper, three different predicting models are introduced, they are neural network model, support vector machine model and GM (1, 1) model. By using these three different models to predict the oil production in XINJIANG oilfield in China, advantages and disadvantages of these models have been discussed. The predicting results show: the fitting accuracy by the neural network model or by the support vector machine model is higher than GM (1, 1) model, the prediction error is smaller than 10%, so neural network model and support vector machine model can be used to short-term forecast of oil production;predicting accuracy by GM (1, 1) model is not good, but the curve trend with GM (1, 1) model is consistent with the downward trend in oil production, so GM (1, 1) predicting model can be used to long-term prediction of oil production.
文摘Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutual feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic approximation, and can describe any non linear dynamic system. After the structure and mathematical description being given, dynamic back propagation (BP) algorithm of training weights of Elman neural network is deduced. At last, the network is used to predict ash content of black amber in jigging production process. The results show that this neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex production process.
文摘A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.
基金supported by the Chongqing Science and Health Joint Medical Research Project(No.8187011078).
文摘Objective:To determine the clinical characteristics and prognosis of primary tracheobronchial tumors(PTTs)in children,and to explore the most common tumor identification methods.Methods:The medical records of children with PTTs who were hospitalized at the Children's Hospital of Chongqing Medical University from January 1995 to January 2020 were reviewed retrospectively.The clinical features,imaging,treatments,and outcomes of these patients were statistically analyzed.Machine learning techniques such as Gaussian na?ve Bayes,support vector machine(SVM)and decision tree models were used to identify mucoepidermoid carcinoma(ME).Results:A total of 16 children were hospitalized with PTTs during the study period.This included 5(31.3%)children with ME,3(18.8%)children with inflammatory myofibroblastic tumors(IMT),2 children(12.5%)with sarcomas,2(12.5%)children with papillomatosis and 1 child(6.3%)each with carcinoid carcinoma,adenoid cystic carcinoma(ACC),hemangioma,and schwannoma,respectively.ME was the most common tumor type and amongst the 3 ME recognition methods,the SVM model showed the best performance.The main clinical symptoms of PPTs were cough(81.3%),breathlessness(50%),wheezing(43.8%),progressive dyspnea(37.5%),hemoptysis(37.5%),and fever(25%).Of the 16 patients,7 were treated with surgery,8 underwent bronchoscopic tumor resection,and 1 child died.Of the 11 other children,3 experienced recurrence,and the last 8 remained disease-free.No deaths were observed during the follow-up period.Conclusion:PTT are very rare in children and the highest percentage of cases is due to ME.The SVM model was highly accurate in identifying ME.Chest CT and bronchoscopy can effectively diagnose PTTs.Surgery and bronchoscopic intervention can both achieve good clinical results and the prognosis of the 11 children that were followed up was good.
文摘Fluvial sediment transport data is a very important data for effective water resource management.However,acquiring this data is expensive and tedious hence sediment yield modeling has become an alternative approach in estimating river sediment yields.In Ghana,several sediment yield predicting models have been developed to estimate the sediment yields of ungauged rivers including the Pra River Basin.In this paper,10 months sediment yield data of the Pra River Basin was used to evaluate the existing sediment yield predicting models of Ghana.A regression analysis between predicted sediment yield data derived from the models and the observed suspended sediment yields of the Pra Basin was done to determine the extent of estimation of observed sediment yields.The prediction of suspended sediment yield was done for 4 out of 5 existing sediment yield predicting models in Ghana.There were variations in sediment yield between observed and predicted suspended sediments.All predicted sediment yields were lower than observed data except for equation 3 where the results were mixed.All models were found to be good estimators of fluvial sediments with the best model being equation 4.Sediment yield tends to increase with drainage basin area.
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 by the National Key R&D Program of China(Grant No.2019YFA0606703)the National Natural Science Foundation of China(Grant No.41975116)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant No.Y202025)。
文摘The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key target.Previous studies have shown that deep learning methods possess a certain level of superiority in predicting ENSO indices.The present study develops a deep learning model for predicting the spatial pattern of sea surface temperature anomalies(SSTAs)in the equatorial Pacific by training a convolutional neural network(CNN)model with historical simulations from CMIP6 models.Compared with dynamical models,the CNN model has higher skill in predicting the SSTAs in the equatorial western-central Pacific,but not in the eastern Pacific.The CNN model can successfully capture the small-scale precursors in the initial SSTAs for the development of central Pacific ENSO to distinguish the spatial mode up to a lead time of seven months.A fusion model combining the predictions of the CNN model and the dynamical models achieves higher skill than each of them for both central and eastern Pacific ENSO.
基金supported by the National Natural Science Foundation of China(No.52074042)National Key R&D Program of China(No.2018YFC1504802).
文摘When the mining goaf is close to the cliff,rock slope subsidence induced by underground mining is significantly affected by its boundary conditions.In this study,an analytical method is proposed by considering the key strata as a semi-infinite Euler-Bernoulli beam rested on a Winkler foundation with a local subsidence area.The analytical solutions of deflection are derived by analyzing the boundary and continuity conditions of the cliff.Then,the analytical solutions are verified by the results from experimental tests,FEM and InSAR,respectively.After that,the influence of changing parameters on deflections is studied with sensitivity analysis.The results show that the distance between goaf and cliff significantly affects the deflection of semi-infinite beam.The response of semi-infinite beam is obviously determined by the length of goaf and the bending stiffness of beam.The comparisons between semi-infinite beam and infinite beam illustrate the ascendancy of the improved model in such problems.
文摘BACKGROUND Acute myocardial infarction(AMI)is a severe cardiovascular disease caused by the blockage of coronary arteries that leads to ischemic necrosis of the myocardium.Timely medical contact is critical for successful AMI treatment,and delays increase the risk of death for patients.Pre-hospital delay time(PDT)is a significant challenge for reducing treatment times,as identifying high-risk patients with AMI remains difficult.This study aims to construct a risk prediction model to identify high-risk patients and develop targeted strategies for effective and prompt care,ultimately reducing PDT and improving treatment outcomes.AIM To construct a nomogram model for forecasting pre-hospital delay(PHD)likelihood in patients with AMI and to assess the precision of the nomogram model in predicting PHD risk.METHODS A retrospective cohort design was employed to investigate predictive factors for PHD in patients with AMI diagnosed between January 2022 and September 2022.The study included 252 patients,with 180 randomly assigned to the development group and the remaining 72 to the validation group in a 7:3 ratio.Independent risk factors influencing PHD were identified in the development group,leading to the establishment of a nomogram model for predicting PHD in patients with AMI.The model's predictive performance was evaluated using the receiver operating characteristic curve in both the development and validation groups.RESULTS Independent risk factors for PHD in patients with AMI included living alone,hyperlipidemia,age,diabetes mellitus,and digestive system diseases(P<0.05).A characteristic curve analysis indicated area under the receiver operating characteristic curve values of 0.787(95%confidence interval:0.716–0.858)and 0.770(95%confidence interval:0.660-0.879)in the development and validation groups,respectively,demonstrating the model's good discriminatory ability.The Hosmer–Lemeshow goodness-of-fit test revealed no statistically significant disparity between the anticipated and observed incidence of PHD in both development and validation cohorts(P>0.05),indicating satisfactory model calibration.CONCLUSION The nomogram model,developed with independent risk factors,accurately forecasts PHD likelihood in AMI individuals,enabling efficient identification of PHD risk in these patients.
文摘Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural damage.Therefore,the development of reliable prediction models for rock bursts is paramount to mitigating these hazards.This study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the LightGBMmodel.Two population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM model.Finally,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock bursts.The results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM model.The developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,respectively.The sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.
基金This research work was financially supported by the National Natural Science Foundation of China(Grant Nos.52078182 and 41877255)the Tianjin Municipal Natural Science Foundation(Grant No.20JCYBJC00630).Their financial support is gratefully acknowledged.
文摘To date,few models are available in the literature to consider the creep behavior of geosynthetics when predicting the lateral deformation(d)of geosynthetics-reinforced soil(GRS)retaining walls.In this study,a general hyperbolic creep model was first introduced to describe the long-term deformation of geosynthetics,which is a function of elapsed time and two empirical parameters a and b.The conventional creep tests with three different tensile loads(Pr)were conducted on two uniaxial geogrids to determine their creep behavior,as well as the a-Pr and b-Pr relationships.The test results show that increasing Pr accelerates the development of creep deformation for both geogrids.Meanwhile,a and b respectively show exponential and negatively linear relationships with Pr,which were confirmed by abundant experimental data available in other studies.Based on the above creep model and relationships,an accurate and reliable analytical model was then proposed for predicting the time-dependent d of GRS walls with modular block facing,which was further validated using a relevant numerical investigation from the previous literature.Performance evaluation and comparison of the proposed model with six available prediction models were performed.Then a parametric study was carried out to evaluate the effects of wall height,vertical spacing of geogrids,unit weight and internal friction angle of backfills,and factor of safety against pullout on d at the end of construction and 5 years afterwards.The findings show that the creep effect not only promotes d but also raises the elevation of the maximum d along the wall height.Finally,the limitations and application prospects of the proposed model were discussed and analyzed.
基金funded by the National Science Foundation of China(42177164)the Distinguished Youth Science Foundation of Hunan Province of China(2022JJ10073)the Innovation-Driven Project of Central South University(2020CX040).
文摘After the excavation of the roadway,the original stress balance is destroyed,resulting in the redistribution of stress and the formation of an excavation damaged zone(EDZ)around the roadway.The thickness of EDZ is the key basis for roadway stability discrimination and support structure design,and it is of great engineering significance to accurately predict the thickness of EDZ.Considering the advantages of machine learning(ML)in dealing with high-dimensional,nonlinear problems,a hybrid prediction model based on the random forest(RF)algorithm is developed in this paper.The model used the dragonfly algorithm(DA)to optimize two hyperparameters in RF,namely mtry and ntree,and used mean absolute error(MAE),rootmean square error(RMSE),determination coefficient(R^(2)),and variance accounted for(VAF)to evaluatemodel prediction performance.A database containing 217 sets of data was collected,with embedding depth(ED),drift span(DS),surrounding rock mass strength(RMS),joint index(JI)as input variables,and the excavation damaged zone thickness(EDZT)as output variable.In addition,four classic models,back propagation neural network(BPNN),extreme learning machine(ELM),radial basis function network(RBF),and RF were compared with the DA-RF model.The results showed that the DARF mold had the best prediction performance(training set:MAE=0.1036,RMSE=0.1514,R^(2)=0.9577,VAF=94.2645;test set:MAE=0.1115,RMSE=0.1417,R^(2)=0.9423,VAF=94.0836).The results of the sensitivity analysis showed that the relative importance of each input variable was DS,ED,RMS,and JI from low to high.
基金Supported by Ningxia Key Research and Development Program,No.2018BEG03001.
文摘BACKGROUND Surgical resection is the primary treatment for hepatocellular carcinoma(HCC).However,studies indicate that nearly 70%of patients experience HCC recurrence within five years following hepatectomy.The earlier the recurrence,the worse the prognosis.Current studies on postoperative recurrence primarily rely on postoperative pathology and patient clinical data,which are lagging.Hence,developing a new pre-operative prediction model for postoperative recurrence is crucial for guiding individualized treatment of HCC patients and enhancing their prognosis.AIM To identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC.METHODS The demographic and clinical data of 371 HCC patients were collected for this retrospective study.These data were randomly divided into training and test sets at a ratio of 8:2.The training set was analyzed,and key feature variables with predictive value for early HCC recurrence were selected to construct six different machine learning prediction models.Each model was evaluated,and the bestperforming model was selected for interpreting the importance of each variable.Finally,an online calculator based on the model was generated for daily clinical practice.RESULTS Following machine learning analysis,eight key feature variables(age,intratumoral arteries,alpha-fetoprotein,preoperative blood glucose,number of tumors,glucose-to-lymphocyte ratio,liver cirrhosis,and pre-operative platelets)were selected to construct six different prediction models.The XGBoost model outperformed other models,with the area under the receiver operating characteristic curve in the training,validation,and test datasets being 0.993(95%confidence interval:0.982-1.000),0.734(0.601-0.867),and 0.706(0.585-0.827),respectively.Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value.CONCLUSION The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence.This model may guide surgical strategies and postoperative individualized medicine.
基金Supported by the Shaanxi Provincial Key Research and Development Plan,No.2020SF-159.
文摘BACKGROUND Acute bleeding due to esophageal varices(EVs)is a life-threatening complication in patients with cirrhosis.The diagnosis of EVs is mainly through upper gastrointestinal endoscopy,but the discomfort,contraindications and complications of gastrointestinal endoscopic screening reduce patient compliance.According to the bleeding risk of EVs,the Baveno VI consensus divides varices into high bleeding risk EVs(HEVs)and low bleeding risk EVs(LEVs).We sought to identify a non-invasive prediction model based on spleen stiffness measurement(SSM)and liver stiffness measurement(LSM)as an alternative to EVs screening.AIM To develop a safe,simple and non-invasive model to predict HEVs in patients with viral cirrhosis and identify patients who can be exempted from upper gastrointestinal endoscopy.METHODS Data from 200 patients with viral cirrhosis were included in this study,with 140 patients as the modelling group and 60 patients as the external validation group,and the EVs types of patients were determined by upper gastrointestinal endoscopy and the Baveno Ⅵ consensus.Those patients were divided into the HEVs group(66 patients)and the LEVs group(74 patients).The effect of each parameter on HEVs was analyzed by univariate and multivariate analyses,and a noninvasive prediction model was established.Finally,the discrimination ability,calibration ability and clinical efficacy of the new model were verified in the modelling group and the external validation group.RESULTS Univariate and multivariate analyses showed that SSM and LSM were associated with the occurrence of HEVs in patients with viral cirrhosis.On this basis,logistic regression analysis was used to construct a prediction model:Ln[P/(1-P)]=-8.184-0.228×SSM+0.642×LSM.The area under the curve of the new model was 0.965.When the cut-off value was 0.27,the sensitivity,specificity,positive predictive value and negative predictive value of the model for predicting HEVs were 100.00%,82.43%,83.52%,and 100%,respectively.Compared with the four prediction models of liver stiffness-spleen diameter to platelet ratio score,variceal risk index,aspartate aminotransferase to alanine aminotransferase ratio,and Baveno VI,the established model can better predict HEVs in patients with viral cirrhosis.CONCLUSION Based on the SSM and LSM measured by transient elastography,we established a non-invasive prediction model for HEVs.The new model is reliable in predicting HEVs and can be used as an alternative to routine upper gastrointestinal endoscopy screening,which is helpful for clinical decision making.
文摘Settlement prediction of geosynthetic-reinforced soil(GRS)abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil engineers.Hence,in this paper,a novel hybrid artificial intelligence(AI)-based model was developed by the combination of artificial neural network(ANN)and Harris hawks’optimisation(HHO),that is,ANN-HHO,to predict the settlement of the GRS abutments.Five other robust intelligent models such as support vector regression(SVR),Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimisation regression(SMOR),and least-median square regression(LMSR)were constructed and compared to the ANN-HHO model.The predictive strength,relalibility and robustness of the model were evaluated based on rigorous statistical testing,ranking criteria,multi-criteria approach,uncertainity analysis and sensitivity analysis(SA).Moreover,the predictive veracity of the model was also substantiated against several large-scale independent experimental studies on GRS abutments reported in the scientific literature.The acquired findings demonstrated that the ANN-HHO model predicted the settlement of GRS abutments with reasonable accuracy and yielded superior performance in comparison to counterpart models.Therefore,it becomes one of predictive tools employed by geotechnical/civil engineers in preliminary decision-making when investigating the in-service performance of GRS abutments.Finally,the model has been converted into a simple mathematical formulation for easy hand calculations,and it is proved cost-effective and less time-consuming in comparison to experimental tests and numerical simulations.
基金the Shaanxi Provincial Key Research and Development Plan,No.2020SF-159.
文摘predicting high-risk esophageal varices based on liver and spleen stiffness".Acute bleeding caused by esophageal varices is a life-threatening complication in patients with liver cirrhosis.Due to the discomfort,contraindications,and associated complications of upper gastrointestinal endoscopy screening,it is crucial to identify an imaging-based non-invasive model for predicting high-risk esophageal varices in patients with cirrhosis.
文摘BACKGROUND Colorectal cancer(CRC)is a significant global health issue,and lymph node metastasis(LNM)is a crucial prognostic factor.Accurate prediction of LNM is essential for developing individualized treatment strategies for patients with CRC.However,the prediction of LNM is challenging and depends on various factors such as tumor histology,clinicopathological features,and molecular characteristics.The most reliable method to detect LNM is the histopathological examination of surgically resected specimens;however,this method is invasive,time-consuming,and subject to sampling errors and interobserver variability.AIM To analyze influencing factors and develop and validate a risk prediction model for LNM in CRC based on a large patient queue.METHODS This study retrospectively analyzed 300 patients who underwent CRC surgery at two Peking University Shenzhen hospitals between January and December 2021.A deep learning approach was used to extract features potentially associated with LNM from primary tumor histological images while a logistic regression model was employed to predict LNM in CRC using machine-learning-derived features and clinicopathological variables as predictors.RESULTS The prediction model constructed for LNM in CRC was based on a logistic regression framework that incorporated machine learning-extracted features and clinicopathological variables.The model achieved high accuracy(0.86),sensitivity(0.81),specificity(0.87),positive predictive value(0.66),negative predictive value(0.94),area under the curve for the receiver operating characteristic(0.91),and a low Brier score(0.10).The model showed good agreement between the observed and predicted probabilities of LNM across a range of risk thresholds,indicating good calibration and clinical utility.CONCLUSION The present study successfully developed and validated a potent and effective risk-prediction model for LNM in patients with CRC.This model utilizes machine-learning-derived features extracted from primary tumor histology and clinicopathological variables,demonstrating superior performance and clinical applicability compared to existing models.The study provides new insights into the potential of deep learning to extract valuable information from tumor histology,in turn,improving the prediction of LNM in CRC and facilitate risk stratification and decision-making in clinical practice.
基金supported by the Multi-Year Research Grants from the University of Macao(MYRG2019-00032-ICMS and MYRG2020-00113-ICMS)the Macao FDCT research grant(0108/2021/A)Molecular modeling was performed at the High-Performance Computing Cluster(HPCC),which is supported by the Information and Communication Technology Office(ICTO)of the University of Macao.
文摘Liposome is one of the most widely used carriers for drug delivery because of the great biocompatibility and biodegradability.Due to the complex formulation components and preparation process,formulation screening mostly relies on trial-and-error process with low efficiency.Here liposome formulation prediction models have been built by machine learning(ML)approaches.The important parameters of liposomes,including size,polydispersity index(PDI),zeta potential and encapsulation,are predicted individually by optimal ML algorithm,while the formulation features are also ranked to provide important guidance for formulation design.The analysis of key parameter reveals that drug molecules with logS[-3,-6],molecular complexity[500,1000]and XLogP3(≥2)are priority for preparing liposome with higher encapsulation.In addition,naproxen(NAP)and palmatine HCl(PAL)represented the insoluble and water-soluble molecules are prepared as liposome formulations to validate prediction ability.The consistency between predicted and experimental value verifies the satisfied accuracy of ML models.As the drug properties are critical for liposome particles,the molecular interactions and dynamics of NAP and PAL liposome are further investigated by coarse-grained molecular dynamics simulations.The modeling structure reveals that NAP molecules could distribute into lipid layer,while most PAL molecules aggregate in the inner aqueous phase of liposome.The completely different physical state of NAP and PAL confirms the importance of drug properties for liposome formulations.In summary,the general prediction models are built to predict liposome formulations,and the impacts of key factors are analyzed by combing ML with molecular modeling.The availability and rationality of these intelligent prediction systems have been proved in this study,which could be applied for liposome formulation development in the future.
基金Supported by Guizhou Provincial Science and Technology Projects,No.[2021]013 and No.[2021]053Doctor Foundation of Guizhou Provincial People's Hospital,No.GZSYBS[2021]07.
文摘BACKGROUND Sarcopenia may be associated with hepatocellular carcinoma(HCC)following hepatectomy.But traditional single clinical variables are still insufficient to predict recurrence.We still lack effective prediction models for recent recurrence(time to recurrence<2 years)after hepatectomy for HCC.AIM To establish an interventable prediction model to estimate recurrence-free survival(RFS)after hepatectomy for HCC based on sarcopenia.METHODS We retrospectively analyzed 283 hepatitis B-related HCC patients who underwent curative hepatectomy for the first time,and the skeletal muscle index at the third lumbar spine was measured by preoperative computed tomography.94 of these patients were enrolled for external validation.Cox multivariate analysis was per-formed to identify the risk factors of postoperative recurrence in training cohort.A nomogram model was developed to predict the RFS of HCC patients,and its predictive performance was validated.The predictive efficacy of this model was evaluated using the receiver operating characteristic curve.RESULTS Multivariate analysis showed that sarcopenia[Hazard ratio(HR)=1.767,95%CI:1.166-2.678,P<0.05],alpha-fetoprotein≥40 ng/mL(HR=1.984,95%CI:1.307-3.011,P<0.05),the maximum diameter of tumor>5 cm(HR=2.222,95%CI:1.285-3.842,P<0.05),and hepatitis B virus DNA level≥2000 IU/mL(HR=2.1,95%CI:1.407-3.135,P<0.05)were independent risk factors associated with postoperative recurrence of HCC.Based on the sarcopenia to assess the RFS model of hepatectomy with hepatitis B-related liver cancer disease(SAMD)was established combined with other the above risk factors.The area under the curve of the SAMD model was 0.782(95%CI:0.705-0.858)in the training cohort(sensitivity 81%,specificity 63%)and 0.773(95%CI:0.707-0.838)in the validation cohort.Besides,a SAMD score≥110 was better to distinguish the high-risk group of postoperative recurrence of HCC.CONCLUSION Sarcopenia is associated with recent recurrence after hepatectomy for hepatitis B-related HCC.A nutritional status-based prediction model is first established for postoperative recurrence of hepatitis B-related HCC,which is superior to other models and contributes to prognosis prediction.
基金This work was supported by the Beijing Nova Program[Z211100002121136]Open Fund Project of State Key Laboratory of Lithospheric Evolution[SKL-K202103]+1 种基金Joint Funds of National Natural Science Foundation of China[U19B6003-02]the National Natural Science Foundation of China[42302149].We would like to thank Prof.Zhu Rixiang from the Institute of Geology and Geophysics,Chinese Academy of Sciences.
文摘With continuous hydrocarbon exploration extending to deeper basins,the deepest industrial oil accumulation was discovered below 8,200 m,revealing a new exploration field.Hence,the extent to which oil exploration can be extended,and the prediction of the depth limit of oil accumulation(DLOA),are issues that have attracted significant attention in petroleum geology.Since it is difficult to characterize the evolution of the physical properties of the marine carbonate reservoir with burial depth,and the deepest drilling still cannot reach the DLOA.Hence,the DLOA cannot be predicted by directly establishing the relationship between the ratio of drilling to the dry layer and the depth.In this study,by establishing the relationships between the porosity and the depth and dry layer ratio of the carbonate reservoir,the relationships between the depth and dry layer ratio were obtained collectively.The depth corresponding to a dry layer ratio of 100%is the DLOA.Based on this,a quantitative prediction model for the DLOA was finally built.The results indicate that the porosity of the carbonate reservoir,Lower Ordovician in Tazhong area of Tarim Basin,tends to decrease with burial depth,and manifests as an overall low porosity reservoir in deep layer.The critical porosity of the DLOA was 1.8%,which is the critical geological condition corresponding to a 100%dry layer ratio encountered in the reservoir.The depth of the DLOA was 9,000 m.This study provides a new method for DLOA prediction that is beneficial for a deeper understanding of oil accumulation,and is of great importance for scientific guidance on deep oil drilling.
