Developmental and reproductive toxicity(DART)endpoint entails a toxicological assessment of all developmental stages and reproductive cycles of an organism.In silico tools to predict DART will provide a method to asse...Developmental and reproductive toxicity(DART)endpoint entails a toxicological assessment of all developmental stages and reproductive cycles of an organism.In silico tools to predict DART will provide a method to assess this complex toxicity endpoint and will be valuable for screening emerging pollutants as well as for m anaging new chemicals in China.Currently,there are few published DART prediction models in China,but many related research and development projects are in progress.In 2013,WU et al.published an expert rule-based DART decision tree(DT).This DT relies on known chemical structures linked to DART to forecast DART potential of a given chemical.Within this procedure,an accurate DART data interpretation is the foundation of building and expanding the DT.This paper excerpted case studies demonstrating DART data curation and interpretation of four chemicals(including 8-hydroxyquinoline,3,5,6-trichloro-2-pyridinol,thiacloprid,and imidacloprid)to expand the existing DART DT.Chemicals were first selected from the database of Solid Waste and Chemicals Management Center,Ministry of Ecology and Environment(MEESCC)in China.The structures of these 4 chemicals were analyzed and preliminarily grouped by chemists based on core structural features,functional groups,receptor binding property,metabolism,and possible mode of actions.Then,the DART conclusion was derived by collecting chemical information,searching,integrating,and interpreting DART data by the toxicologists.Finally,these chemicals were classified into either an existing category or a new category via integrating their chemical features,DART conclusions,and biological properties.The results showed that 8-hydroxyquinoline impacted estrous cyclicity,s exual organ weights,and embryonal development,and 3,5,6-trichloro-2-pyridinol caused central nervous system(CNS)malformations,which were added to an existing subcategory 8e(aromatic compounds with multi-halogen and nitro groups)of the DT.Thiacloprid caused dystocia and fetal skeletal malformation,and imidacloprid disrupted the endocrine system and male fertility.They both contain 2-chloro-5-methylpyridine substituted imidazolidine c yclic ring,which were expected to create a new category of neonicotinoids.The current work delineates a t ransparent process of curating toxicological data for the purpose of DART data interpretation.In the presence of sufficient related structures and DART data,the DT can be expanded by iteratively adding chemicals within the a pplicable domain of each category or subcategory.This DT can potentially serve as a tool for screening emerging pollutants and assessing new chemicals in China.展开更多
Traumatic Brain Injury is a major cause of death and long-term disability.The early identification of patients at high risk of mortality is important for both management and prognosis.Although many modified scoring sy...Traumatic Brain Injury is a major cause of death and long-term disability.The early identification of patients at high risk of mortality is important for both management and prognosis.Although many modified scoring systems have been developed for improving the prediction accuracy in patients with trauma,few studies have focused on prediction accuracy and application in patients with traumatic brain injury.The shock index(SI)which was first introduced in the 1960s has shown to strongly correlate degree of circulatory shock with increasing SI.In this editorial we comment on a publication by Carteri et al wherein they perform a retrospective analysis studying the predictive potential of SI and its variants in populations with severe traumatic brain injury.展开更多
BACKGROUND The increase in severe traumatic brain injury(sTBI)incidence is a worldwide phenomenon,resulting in a heavy disease burden in the public health systems,specifically in emerging countries.The shock index(SI)...BACKGROUND The increase in severe traumatic brain injury(sTBI)incidence is a worldwide phenomenon,resulting in a heavy disease burden in the public health systems,specifically in emerging countries.The shock index(SI)is a physiological parameter that indicates cardiovascular status and has been used as a tool to assess the presence and severity of shock,which is increased in sTBI.Considering the high mortality of sTBI,scrutinizing the predictive potential of SI and its variants is vital.AIM To describe the predictive potential of SI and its variants in sTBI.METHODS This study included 71 patients(61 men and 10 women)divided into two groups:Survival(S;n=49)and Non-survival(NS;n=22).The responses of blood pressure and heart rate(HR)were collected at admission and 48 h after admission.The SI,reverse SI(rSI),rSI multiplied by the Glasgow Coma Score(rSIG),and Age multiplied SI(AgeSI)were calculated.Group comparisons included Shapiro-Wilk tests,and independent samples t-tests.For predictive analysis,logistic regression,receiver operator curves(ROC)curves,and area under the curve(AUC)measurements were performed.RESULTS No significant differences between groups were identified for SI,rSI,or rSIG.The AgeSI was significantly higher in NS patients at 48 h following admission(S:26.32±14.2,and NS:37.27±17.8;P=0.016).Both the logistic regression and the AUC following ROC curve analysis showed that only AgeSI at 48 h was capable of predicting sTBI outcomes.CONCLUSION Although an altered balance between HR and blood pressure can provide insights into the adequacy of oxygen delivery to tissues and the overall cardiac function,only the AgeSI was a viable outcome-predictive tool in sTBI,warranting future research in different cohorts.展开更多
Train braking performance is important for the safety and reliability of railway systems. The availability of a tool that allows evaluating such performance on the basis of the main train features can be useful for tr...Train braking performance is important for the safety and reliability of railway systems. The availability of a tool that allows evaluating such performance on the basis of the main train features can be useful for train system designers to choose proper dimensions for and optimize train＇s subsystems. This paper presents a modular tool for the prediction of train braking performance, with a par- ticular attention to the accurate prediction of stopping distances. The tool takes into account different loading and operating conditions, in order to verify the safety require- ments prescribed by European technical specifications for interoperability of high-speed trains and the corresponding EN regulations. The numerical results given by the tool were verified and validated by comparison with experimental data, considering as benchmark case an Ansaldo EMU V250 train--a European high-speed train--currently developed for Belgium and Netherlands high-speed lines, on which technical information and experimental data directly recorded during the preliminary tests were available. An accurate identification of the influence of the braking pad friction factor on braking performances allowed obtaining reliable results.展开更多
As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symboli...As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.展开更多
The nth-order expansion of the parabolized stability equation （EPSEn） is obtained from the Taylor expansion of the linear parabolized stability equation （LPSE） in the streamwise direction. The EPSE together with t...The nth-order expansion of the parabolized stability equation （EPSEn） is obtained from the Taylor expansion of the linear parabolized stability equation （LPSE） in the streamwise direction. The EPSE together with the homogeneous boundary conditions forms a local eigenvalue problem, in which the streamwise variations of the mean flow and the disturbance shape function are considered. The first-order EPSE （EPSE1） and the second-order EPSE （EPSE2） are used to study the crossflow instability in the swept NLF（2）-0415 wing boundary layer. The non-parallelism degree of the boundary layer is strong. Compared with the growth rates predicted by the linear stability theory （LST）, the results given by the EPSE1 and EPSE2 agree well with those given by the LPSE. In particular, the results given by the EPSE2 are almost the same as those given by the LPSE. The prediction of the EPSE1 is more accurate than the prediction of the LST, and is more efficient than the predictions of the EPSE2 and LPSE. Therefore, the EPSE1 is an efficient ey prediction tool for the crossflow instability in swept-wing boundary-layer flows.展开更多
BACKGROUND Insulin is the preferred clinical treatment for hospitalized patients with type 2 diabetes mellitus(T2DM)to control blood glucose effectively.Hypoglycemia is one of the most common adverse events.Accurate p...BACKGROUND Insulin is the preferred clinical treatment for hospitalized patients with type 2 diabetes mellitus(T2DM)to control blood glucose effectively.Hypoglycemia is one of the most common adverse events.Accurate prediction of the risk of hypoglycemia is critical in reducing hypoglycemic events and related adverse events in hospitalized diabetic patients treated with insulin.AIM To develop and validate a hypoglycemia risk prediction tool for hospitalized patients with T2DM treated with insulin.METHODS This retrospective study included 802 hospitalized patients with T2DM in the Department of Endocrinology,the Third Affiliated Hospital of Sun Yat-sen University,between January 2021 and December 2021.The hypoglycemia risk prediction model was developed using logistic regression and nomogram models.The model was validated and calibrated using receiver operating characteristic curves and the Hosmer-Lemeshow goodness of fit test.RESULTS The incidence of hypoglycemia among the enrolled patients was 44.9%.The hypoglycemic risk prediction model included six predictors:Body mass index,duration of diabetes,history of hypoglycemia within 1 year,glomerular filtration rate,blood triglyceride levels,and duration of treatment.The hypoglycemia risk prediction model displayed high discrimination ability(area under the curve=0.67)and good calibration power(goodness of fit,χ^(2)=12.25,P=0.14).CONCLUSION The hypoglycemia risk prediction model for hospitalized patients with T2DM on insulin therapy displayed high reliability and discrimination ability.The model is a promising tool for clinicians to screen hospitalized patients with T2DM and an elevated risk of hypoglycemia and guide personalized interventions to prevent and treat hypoglycemia.展开更多
Liquid smoke is a natural product made up of smoke concentrate which is used to impart a smoky flavour without resorting to the traditional smoking technique:it is practical to use,cheap,easy to dose,able to control t...Liquid smoke is a natural product made up of smoke concentrate which is used to impart a smoky flavour without resorting to the traditional smoking technique:it is practical to use,cheap,easy to dose,able to control the presence of undesirable substances and,above all,with reduced environmental impact,unlike traditional smoking systems.On the other hand,it has a low preservation effect.Thus,the objective of this study was to design the production of an innovative fish product using liquid smoke in combination with natural compounds through three subsequent phases:1)economic concept evaluation to assess the acceptability of the proposed product through a qualitative and quantitative investigation;2)optimisation of the process(individuation of smoking liquid composition and process parameters)using modelling predictive tools,i.e.tertiary and secondary models;3)product realization and its evaluation in terms of microbiological profile,chemical-physical param-eters,and consumers’acceptability.Results show that sea bream and sea bass fillets could be sprayed using a solution composed by lemon extract(0.75%),acetic acid(0.5%),NaCl(2%),and liquid smoke(0.002%),packed under vacuum and stored at 4℃ for at least two weeks,during which spoilage bacteria maintained low cell loads.After 14 days,in fact,smoked fillets showed total viable count and psychrotrophs of about 5 log CFU/g,Pseudomonadaceae about 6 log CFU/g,while for Enterobacteriaceae cell loads of 2-3 log CFU/g were recorded.Other microbial groups were absent and pathogens were never detected.In addition,results from the consumer survey highlight that over 60%of the interviewed sample appeared inclined to accept innovation,with 50%of respondents also willing to pay a premium price of 20%,thus sug-gesting that the proposed eco-smoking technique could be adopted and help in ensuring a more sustainable food production.展开更多
Background:Diabetes and hypertension are two of the commonest diseases in the world.As they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed wel...Background:Diabetes and hypertension are two of the commonest diseases in the world.As they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed well in advance.Objective:This research aims to determine the effectiveness of artificial neural networks(ANNs)in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these diseases.Sample:This work used two online datasets which consist of data collected from 768 individuals.We applied neural network algorithms to predict if the individuals have those two diseases based on some factors.Diabetes prediction is based on five factors:age,weight,fat-ratio,glucose,and insulin,while blood pressure prediction is based on six factors:age,weight,fat-ratio,blood pressure,alcohol,and smoking.Method:A model based on the Multi-Layer Perceptron Neural Network(MLP)was implemented.The inputs of the network were the factors for each disease,while the output was the prediction of the disease’s occurrence.The model performance was compared with other classifiers such as Support Vector Machine(SVM)and K-Nearest Neighbors(KNN).We used performance metrics measures to assess the accuracy and performance of MLP.Also,a tool was implemented to help diagnose the diseases and to understand the results.Result:The model predicted the two diseases with correct classification rate(CCR)of 77.6%for diabetes and 68.7%for hypertension.The results indicate that MLP correctly predicts the probability of being diseased or not,and the performance can be significantly increased compared with both SVM and KNN.This shows MLPs effectiveness in early disease prediction.展开更多
Background The role of adjuvant transarterial chemoembolisation(TACE)to reduce postoperative recurrence varies widely among patients undergoing hepatectomy with curative intent for hepatocellular carcinoma(HCC).Person...Background The role of adjuvant transarterial chemoembolisation(TACE)to reduce postoperative recurrence varies widely among patients undergoing hepatectomy with curative intent for hepatocellular carcinoma(HCC).Personalised predictive tool to select which patients may benefit from adjuvant TACE is lacking.This study aimed to develop and validate an online calculator for estimating the reduced risk of early recurrence from adjuvant TACE for patients with HCC.Methods From a multi-institutional database,2590 eligible patients undergoing curative-intent hepatectomy for HCC were enrolled,and randomly assigned to the training and validation cohorts.Independent predictors of early recurrence within 1 year of surgery were identified in the training cohort,and subsequently used to construct a model and corresponding prediction calculator.The predictive performance of the model was validated using concordance indexes(C-indexes)and calibration curves,and compared with conventional HCC staging systems.The reduced risk of early recurrence when receiving adjuvant TACE was used to estimate the expected benefit from adjuvant TACE.Results The prediction model was developed by integrating eight factors that were independently associated with risk of early recurrence:alpha-fetoprotein level,maximum tumour size,tumour number,macrovascular and microvascular invasion,satellite nodules,resection margin and adjuvant TACE.The model demonstrated good calibration and discrimination in the training and validation cohorts(C-indexes:0.799 and 0.778,respectively),and performed better among the whole cohort than four conventional HCC staging systems(C-indexes:0.797 vs 0.562–0.673,all p<0.001).An online calculator was built to estimate the reduced risk of early recurrence from adjuvant TACE for patients with resected HCC.Conclusions The proposed calculator can be adopted to assist decision-making for clinicians and patients to determine which patients with resected HCC can significantly benefit from adjuvant TACE.WHAT IS ALREADY KNOWN ON THIS TOPIC⇒Previous studies have indicated that adjuvant transarterial chemoembolisation(TACE)may im-prove long-term survival in certain subgroups of patients with hepatocellular carcinoma(HCC)after hepatectomy.⇒However,these studies did not provide personalised risk assessment or net benefit estimation for indi-vidual patients,highlighting the need for a more refined prediction model.WHAT THIS STUDY ADDS⇒This study developed a risk prediction model in-corporating eight independent factors associat-ed with early recurrence after hepatectomy for HCC,demonstrating good predictive accuracy and discrimination.⇒The model outperformed four commonly used con-ventional HCC staging systems and facilitated the development of an online calculator to estimate in-dividual patient’s reduced risk of early recurrence using adjuvant TACE.HOW THIS STUDY MIGHT AFFECT RESEARCH,PRACTICE OR POLICY⇒The study’s findings may assist clinicians in decid-ing whether to use adjuvant TACE after hepatectomy for HCC,potentially improving patient outcomes.⇒Further research should validate the model with larger cohorts or those from other centres to assess its broader applicability.展开更多
This study assessed the sex-based relationship and prediction pattern between fingerprint patterns,ridge counts,and learning disability(LD).This cross-sectional study recruited 300 students(150 LD and 150 non-LD)aged ...This study assessed the sex-based relationship and prediction pattern between fingerprint patterns,ridge counts,and learning disability(LD).This cross-sectional study recruited 300 students(150 LD and 150 non-LD)aged between 3 and 29 years.The fingerprint patterns(arch,whorl,ulnar loop,and radial loop)and the ridge count:total finger ridge count(TFRC),absolute ridge count(ARC),ulnar ridge count(URC),and radial ridge count(RRC)were accessed.Students with LD showed a significantly higher whorl and a significantly lower ulnar loop than students without LD.There is a significant association of whorl pattern in the first right finger of subjects with LD compared to non-LD counterparts.TFRC,ARC,and URC were significantly higher in females with LD than non-LD females(P=0.01,0.03,and 0.001).Males with LD showed significantly lower TFRC,RRC,and URC counts than the non-LD males(P=0.02,0.01,and 0.001).TFRC can predict LD in males(odds ratio[OR]=1.010,P=0.032)and females(OR=0.993,P=0.012).Fingerprint pattern and ridge counts are sexually dimorphic in subjects with or without LD.TFRC and whorl fingerprint patterns may be vital predictive and screening tools for LD in males and females.展开更多
文摘Developmental and reproductive toxicity(DART)endpoint entails a toxicological assessment of all developmental stages and reproductive cycles of an organism.In silico tools to predict DART will provide a method to assess this complex toxicity endpoint and will be valuable for screening emerging pollutants as well as for m anaging new chemicals in China.Currently,there are few published DART prediction models in China,but many related research and development projects are in progress.In 2013,WU et al.published an expert rule-based DART decision tree(DT).This DT relies on known chemical structures linked to DART to forecast DART potential of a given chemical.Within this procedure,an accurate DART data interpretation is the foundation of building and expanding the DT.This paper excerpted case studies demonstrating DART data curation and interpretation of four chemicals(including 8-hydroxyquinoline,3,5,6-trichloro-2-pyridinol,thiacloprid,and imidacloprid)to expand the existing DART DT.Chemicals were first selected from the database of Solid Waste and Chemicals Management Center,Ministry of Ecology and Environment(MEESCC)in China.The structures of these 4 chemicals were analyzed and preliminarily grouped by chemists based on core structural features,functional groups,receptor binding property,metabolism,and possible mode of actions.Then,the DART conclusion was derived by collecting chemical information,searching,integrating,and interpreting DART data by the toxicologists.Finally,these chemicals were classified into either an existing category or a new category via integrating their chemical features,DART conclusions,and biological properties.The results showed that 8-hydroxyquinoline impacted estrous cyclicity,s exual organ weights,and embryonal development,and 3,5,6-trichloro-2-pyridinol caused central nervous system(CNS)malformations,which were added to an existing subcategory 8e(aromatic compounds with multi-halogen and nitro groups)of the DT.Thiacloprid caused dystocia and fetal skeletal malformation,and imidacloprid disrupted the endocrine system and male fertility.They both contain 2-chloro-5-methylpyridine substituted imidazolidine c yclic ring,which were expected to create a new category of neonicotinoids.The current work delineates a t ransparent process of curating toxicological data for the purpose of DART data interpretation.In the presence of sufficient related structures and DART data,the DT can be expanded by iteratively adding chemicals within the a pplicable domain of each category or subcategory.This DT can potentially serve as a tool for screening emerging pollutants and assessing new chemicals in China.
文摘Traumatic Brain Injury is a major cause of death and long-term disability.The early identification of patients at high risk of mortality is important for both management and prognosis.Although many modified scoring systems have been developed for improving the prediction accuracy in patients with trauma,few studies have focused on prediction accuracy and application in patients with traumatic brain injury.The shock index(SI)which was first introduced in the 1960s has shown to strongly correlate degree of circulatory shock with increasing SI.In this editorial we comment on a publication by Carteri et al wherein they perform a retrospective analysis studying the predictive potential of SI and its variants in populations with severe traumatic brain injury.
文摘BACKGROUND The increase in severe traumatic brain injury(sTBI)incidence is a worldwide phenomenon,resulting in a heavy disease burden in the public health systems,specifically in emerging countries.The shock index(SI)is a physiological parameter that indicates cardiovascular status and has been used as a tool to assess the presence and severity of shock,which is increased in sTBI.Considering the high mortality of sTBI,scrutinizing the predictive potential of SI and its variants is vital.AIM To describe the predictive potential of SI and its variants in sTBI.METHODS This study included 71 patients(61 men and 10 women)divided into two groups:Survival(S;n=49)and Non-survival(NS;n=22).The responses of blood pressure and heart rate(HR)were collected at admission and 48 h after admission.The SI,reverse SI(rSI),rSI multiplied by the Glasgow Coma Score(rSIG),and Age multiplied SI(AgeSI)were calculated.Group comparisons included Shapiro-Wilk tests,and independent samples t-tests.For predictive analysis,logistic regression,receiver operator curves(ROC)curves,and area under the curve(AUC)measurements were performed.RESULTS No significant differences between groups were identified for SI,rSI,or rSIG.The AgeSI was significantly higher in NS patients at 48 h following admission(S:26.32±14.2,and NS:37.27±17.8;P=0.016).Both the logistic regression and the AUC following ROC curve analysis showed that only AgeSI at 48 h was capable of predicting sTBI outcomes.CONCLUSION Although an altered balance between HR and blood pressure can provide insights into the adequacy of oxygen delivery to tissues and the overall cardiac function,only the AgeSI was a viable outcome-predictive tool in sTBI,warranting future research in different cohorts.
文摘Train braking performance is important for the safety and reliability of railway systems. The availability of a tool that allows evaluating such performance on the basis of the main train features can be useful for train system designers to choose proper dimensions for and optimize train＇s subsystems. This paper presents a modular tool for the prediction of train braking performance, with a par- ticular attention to the accurate prediction of stopping distances. The tool takes into account different loading and operating conditions, in order to verify the safety require- ments prescribed by European technical specifications for interoperability of high-speed trains and the corresponding EN regulations. The numerical results given by the tool were verified and validated by comparison with experimental data, considering as benchmark case an Ansaldo EMU V250 train--a European high-speed train--currently developed for Belgium and Netherlands high-speed lines, on which technical information and experimental data directly recorded during the preliminary tests were available. An accurate identification of the influence of the braking pad friction factor on braking performances allowed obtaining reliable results.
基金Supported in part by Natural Science Foundation of China(Grant Nos.51835009,51705398)Shaanxi Province 2020 Natural Science Basic Research Plan(Grant No.2020JQ-042)Aeronautical Science Foundation(Grant No.2019ZB070001).
文摘As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.
基金supported by the National Natural Science Foundation of China(No.11332007)
文摘The nth-order expansion of the parabolized stability equation （EPSEn） is obtained from the Taylor expansion of the linear parabolized stability equation （LPSE） in the streamwise direction. The EPSE together with the homogeneous boundary conditions forms a local eigenvalue problem, in which the streamwise variations of the mean flow and the disturbance shape function are considered. The first-order EPSE （EPSE1） and the second-order EPSE （EPSE2） are used to study the crossflow instability in the swept NLF（2）-0415 wing boundary layer. The non-parallelism degree of the boundary layer is strong. Compared with the growth rates predicted by the linear stability theory （LST）, the results given by the EPSE1 and EPSE2 agree well with those given by the LPSE. In particular, the results given by the EPSE2 are almost the same as those given by the LPSE. The prediction of the EPSE1 is more accurate than the prediction of the LST, and is more efficient than the predictions of the EPSE2 and LPSE. Therefore, the EPSE1 is an efficient ey prediction tool for the crossflow instability in swept-wing boundary-layer flows.
基金Supported by Medical Scientific Research Foundation of Guangdong Province of China,No.A2023183 and No.A2024530Nursing Innovation Development Research Project,No.YJYZ202304+2 种基金National Natural Science Foundation of China,No.72204277Guangdong Basic and Applied Basic Research Foundation,No.2025A15150127063rd Affiliated Hospital of Sun Yat-sen University,Clinical Research Program,No.YHJH202404.
文摘BACKGROUND Insulin is the preferred clinical treatment for hospitalized patients with type 2 diabetes mellitus(T2DM)to control blood glucose effectively.Hypoglycemia is one of the most common adverse events.Accurate prediction of the risk of hypoglycemia is critical in reducing hypoglycemic events and related adverse events in hospitalized diabetic patients treated with insulin.AIM To develop and validate a hypoglycemia risk prediction tool for hospitalized patients with T2DM treated with insulin.METHODS This retrospective study included 802 hospitalized patients with T2DM in the Department of Endocrinology,the Third Affiliated Hospital of Sun Yat-sen University,between January 2021 and December 2021.The hypoglycemia risk prediction model was developed using logistic regression and nomogram models.The model was validated and calibrated using receiver operating characteristic curves and the Hosmer-Lemeshow goodness of fit test.RESULTS The incidence of hypoglycemia among the enrolled patients was 44.9%.The hypoglycemic risk prediction model included six predictors:Body mass index,duration of diabetes,history of hypoglycemia within 1 year,glomerular filtration rate,blood triglyceride levels,and duration of treatment.The hypoglycemia risk prediction model displayed high discrimination ability(area under the curve=0.67)and good calibration power(goodness of fit,χ^(2)=12.25,P=0.14).CONCLUSION The hypoglycemia risk prediction model for hospitalized patients with T2DM on insulin therapy displayed high reliability and discrimination ability.The model is a promising tool for clinicians to screen hospitalized patients with T2DM and an elevated risk of hypoglycemia and guide personalized interventions to prevent and treat hypoglycemia.
基金funded by the project PO FEAMP 2014/2020-Measure 1.26“Valorizzazione di specie ittiche affumicate mediante tecniche tradizionali e innovative”,Apulia region.(CUP N.B71B17000990009Project leader:UNCI-Agroalimentare).
文摘Liquid smoke is a natural product made up of smoke concentrate which is used to impart a smoky flavour without resorting to the traditional smoking technique:it is practical to use,cheap,easy to dose,able to control the presence of undesirable substances and,above all,with reduced environmental impact,unlike traditional smoking systems.On the other hand,it has a low preservation effect.Thus,the objective of this study was to design the production of an innovative fish product using liquid smoke in combination with natural compounds through three subsequent phases:1)economic concept evaluation to assess the acceptability of the proposed product through a qualitative and quantitative investigation;2)optimisation of the process(individuation of smoking liquid composition and process parameters)using modelling predictive tools,i.e.tertiary and secondary models;3)product realization and its evaluation in terms of microbiological profile,chemical-physical param-eters,and consumers’acceptability.Results show that sea bream and sea bass fillets could be sprayed using a solution composed by lemon extract(0.75%),acetic acid(0.5%),NaCl(2%),and liquid smoke(0.002%),packed under vacuum and stored at 4℃ for at least two weeks,during which spoilage bacteria maintained low cell loads.After 14 days,in fact,smoked fillets showed total viable count and psychrotrophs of about 5 log CFU/g,Pseudomonadaceae about 6 log CFU/g,while for Enterobacteriaceae cell loads of 2-3 log CFU/g were recorded.Other microbial groups were absent and pathogens were never detected.In addition,results from the consumer survey highlight that over 60%of the interviewed sample appeared inclined to accept innovation,with 50%of respondents also willing to pay a premium price of 20%,thus sug-gesting that the proposed eco-smoking technique could be adopted and help in ensuring a more sustainable food production.
文摘Background:Diabetes and hypertension are two of the commonest diseases in the world.As they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed well in advance.Objective:This research aims to determine the effectiveness of artificial neural networks(ANNs)in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these diseases.Sample:This work used two online datasets which consist of data collected from 768 individuals.We applied neural network algorithms to predict if the individuals have those two diseases based on some factors.Diabetes prediction is based on five factors:age,weight,fat-ratio,glucose,and insulin,while blood pressure prediction is based on six factors:age,weight,fat-ratio,blood pressure,alcohol,and smoking.Method:A model based on the Multi-Layer Perceptron Neural Network(MLP)was implemented.The inputs of the network were the factors for each disease,while the output was the prediction of the disease’s occurrence.The model performance was compared with other classifiers such as Support Vector Machine(SVM)and K-Nearest Neighbors(KNN).We used performance metrics measures to assess the accuracy and performance of MLP.Also,a tool was implemented to help diagnose the diseases and to understand the results.Result:The model predicted the two diseases with correct classification rate(CCR)of 77.6%for diabetes and 68.7%for hypertension.The results indicate that MLP correctly predicts the probability of being diseased or not,and the performance can be significantly increased compared with both SVM and KNN.This shows MLPs effectiveness in early disease prediction.
基金supported by the National Natural Science Foundation of China(no.82273074)Dawn Project Foundation of Shanghai(no.21SG36)+2 种基金Adjunct Talent Fund of Zhejiang Provincial People’s Hospital(no.2021-YT)the Natural Science Foundation of Shanghai(no.22ZR1477900)Shanghai Science and Technology Committee Rising-Star Programme(no.22QA1411600).
文摘Background The role of adjuvant transarterial chemoembolisation(TACE)to reduce postoperative recurrence varies widely among patients undergoing hepatectomy with curative intent for hepatocellular carcinoma(HCC).Personalised predictive tool to select which patients may benefit from adjuvant TACE is lacking.This study aimed to develop and validate an online calculator for estimating the reduced risk of early recurrence from adjuvant TACE for patients with HCC.Methods From a multi-institutional database,2590 eligible patients undergoing curative-intent hepatectomy for HCC were enrolled,and randomly assigned to the training and validation cohorts.Independent predictors of early recurrence within 1 year of surgery were identified in the training cohort,and subsequently used to construct a model and corresponding prediction calculator.The predictive performance of the model was validated using concordance indexes(C-indexes)and calibration curves,and compared with conventional HCC staging systems.The reduced risk of early recurrence when receiving adjuvant TACE was used to estimate the expected benefit from adjuvant TACE.Results The prediction model was developed by integrating eight factors that were independently associated with risk of early recurrence:alpha-fetoprotein level,maximum tumour size,tumour number,macrovascular and microvascular invasion,satellite nodules,resection margin and adjuvant TACE.The model demonstrated good calibration and discrimination in the training and validation cohorts(C-indexes:0.799 and 0.778,respectively),and performed better among the whole cohort than four conventional HCC staging systems(C-indexes:0.797 vs 0.562–0.673,all p<0.001).An online calculator was built to estimate the reduced risk of early recurrence from adjuvant TACE for patients with resected HCC.Conclusions The proposed calculator can be adopted to assist decision-making for clinicians and patients to determine which patients with resected HCC can significantly benefit from adjuvant TACE.WHAT IS ALREADY KNOWN ON THIS TOPIC⇒Previous studies have indicated that adjuvant transarterial chemoembolisation(TACE)may im-prove long-term survival in certain subgroups of patients with hepatocellular carcinoma(HCC)after hepatectomy.⇒However,these studies did not provide personalised risk assessment or net benefit estimation for indi-vidual patients,highlighting the need for a more refined prediction model.WHAT THIS STUDY ADDS⇒This study developed a risk prediction model in-corporating eight independent factors associat-ed with early recurrence after hepatectomy for HCC,demonstrating good predictive accuracy and discrimination.⇒The model outperformed four commonly used con-ventional HCC staging systems and facilitated the development of an online calculator to estimate in-dividual patient’s reduced risk of early recurrence using adjuvant TACE.HOW THIS STUDY MIGHT AFFECT RESEARCH,PRACTICE OR POLICY⇒The study’s findings may assist clinicians in decid-ing whether to use adjuvant TACE after hepatectomy for HCC,potentially improving patient outcomes.⇒Further research should validate the model with larger cohorts or those from other centres to assess its broader applicability.
文摘This study assessed the sex-based relationship and prediction pattern between fingerprint patterns,ridge counts,and learning disability(LD).This cross-sectional study recruited 300 students(150 LD and 150 non-LD)aged between 3 and 29 years.The fingerprint patterns(arch,whorl,ulnar loop,and radial loop)and the ridge count:total finger ridge count(TFRC),absolute ridge count(ARC),ulnar ridge count(URC),and radial ridge count(RRC)were accessed.Students with LD showed a significantly higher whorl and a significantly lower ulnar loop than students without LD.There is a significant association of whorl pattern in the first right finger of subjects with LD compared to non-LD counterparts.TFRC,ARC,and URC were significantly higher in females with LD than non-LD females(P=0.01,0.03,and 0.001).Males with LD showed significantly lower TFRC,RRC,and URC counts than the non-LD males(P=0.02,0.01,and 0.001).TFRC can predict LD in males(odds ratio[OR]=1.010,P=0.032)and females(OR=0.993,P=0.012).Fingerprint pattern and ridge counts are sexually dimorphic in subjects with or without LD.TFRC and whorl fingerprint patterns may be vital predictive and screening tools for LD in males and females.
