Objective:To investigate the interrelationship between cardiovascular health awareness,risk perception,behavioural intention,and INTERHEART risk stratification in a middle-aged adult population in Malaysia.Methods:A c...Objective:To investigate the interrelationship between cardiovascular health awareness,risk perception,behavioural intention,and INTERHEART risk stratification in a middle-aged adult population in Malaysia.Methods:A cross-sectional survey with convenience sampling was conducted during November 2022 and January 2023.Participants completed validated questionnaires assessing cardiovascular health awareness,risk perception of cardiovascular diseases,behavioural intention towards adopting healthy habits,and INTERHEART risk stratification score(IHRS)based on established risk factors.A total of 602 respondents were included in the final analysis.Data were analysed with independent t-test/one-way ANOVA or Mann-Whitney/Kruskal-Wallis to test the differences,Pearson correlation or linear regression test to analyze the association of independent and dependent variables.Results:There was a significant positive correlation between medical knowledge related to cardiovascular disease(CVD)and knowledge related to CVD risk prevention,risk perception,behavioural intention and IHRS(P<0.05,Pearson correlation).Notably,individuals with higher IHRS tended to have lower knowledge related to CVD and CVD risk prevention,risk perception,and behavioural intention.Males,laborers,active/former smokers,individuals with lower household income and educational levels,those involved in occupations not related to the healthcare sector,and those who did not receive the CVD health brochure or are unaware of health self-assessment tools are likely to have lower levels of knowledge,risk perception,and poorer behavioural intention regarding cardiovascular health(P<0.05,one-way ANOVA).While educational level,smoking status,awareness about CVD poster,self-assessment tools were repeatedly significantly associated with knowledge related to CVD and CVD risk prevention,risk perception,behavioral intention and/or IHRS(P<0.05,linear regression).Conclusions:These findings underscore the importance of promoting cardiovascular health awareness and risk perception among middle-aged adults to foster positive BI and reduce CVD risk.Tailored interventions targeting specific risk factors identified by INTERHEART may enhance risk stratification accuracy and facilitate targeted preventive strategies.展开更多
Objectives:Anastomotic leakage(AL)stands out as a prevalent and severe complication following gastric cancer surgery.It frequently precipitates additional serious complications,significantly influencing the overall su...Objectives:Anastomotic leakage(AL)stands out as a prevalent and severe complication following gastric cancer surgery.It frequently precipitates additional serious complications,significantly influencing the overall survival time of patients.This study aims to enhance the risk-assessment strategy for AL following gastrectomy for gastric cancer.Methods:This study included a derivation cohort and validation cohort.The derivation cohort included patients who underwent radical gastrectomy at Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,from January 1,2015 to December 31,2020.An evidence-based predictor questionnaire was crafted through extensive literature review and panel discussions.Based on the questionnaire,inpatient data were collected to form a model-derivation cohort.This cohort underwent both univariate and multivariate analyses to identify factors associated with AL events,and a logistic regression model with stepwise regression was developed.A 5-fold cross-validation ensured model reliability.The validation cohort included patients from August 1,2021 to December 31,2021 at the same hospital.Using the same imputation method,we organized the validation-queue data.We then employed the risk-prediction model constructed in the earlier phase of the study to predict the risk of AL in the subjects included in the validation queue.We compared the predictions with the actual occurrence,and evaluated the external validation performance of the model using model-evaluation indicators such as the area under the receiver operating characteristic curve(AUROC),Brier score,and calibration curve.Results:The derivation cohort included 1377 patients,and the validation cohort included 131 patients.The independent predictors of AL after radical gastrectomy included age65 y,preoperative albumin<35 g/L,resection extent,operative time240 min,and intraoperative blood loss90 mL.The predictive model exhibited a solid AUROC of 0.750(95%CI:0.694e0.806;p<0.001)with a Brier score of 0.049.The 5-fold cross-validation confirmed these findings with a calibrated C-index of 0.749 and an average Brier score of 0.052.External validation showed an AUROC of 0.723(95%CI:0.564e0.882;p?0.006)and a Brier score of 0.055,confirming reliability in different clinical settings.Conclusions:We successfully developed a risk-prediction model for AL following radical gastrectomy.This tool will aid healthcare professionals in anticipating AL,potentially reducing unnecessary interventions.展开更多
BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages ...BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis.Therefore,attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment.AIM To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model.METHODS A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022.The patient cohort was divided into a training group(n=84)and a validation group(n=36)at a ratio of 7:3.The patients’anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale(SAS)and the Selfrating Depression Scale(SDS),respectively.Logistic regression was used to analyze the risk factors affecting negative mood,and a risk prediction model was constructed.RESULTS In the training group,35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50,respectively.Based on the scores,we further subclassified patients into two groups:a bad mood group(n=35)and an emotional stability group(n=49).Multivariate logistic regression analysis showed that marital status,castration scheme,and postoperative Visual Analogue Scale(VAS)score were independent risk factors affecting a patient's bad mood(P<0.05).In the training and validation groups,patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients(P<0.0001).The area under the curve(AUC)of the risk prediction model for predicting bad mood in the training group was 0.743,the specificity was 70.96%,and the sensitivity was 66.03%,while in the validation group,the AUC,specificity,and sensitivity were 0.755,66.67%,and 76.19%,respectively.The Hosmer-Lemeshow test showed aχ^(2) of 4.2856,a P value of 0.830,and a C-index of 0.773(0.692-0.854).The calibration curve revealed that the predicted curve was basically consistent with the actual curve,and the calibration curve showed that the prediction model had good discrimination and accuracy.Decision curve analysis showed that the model had a high net profit.CONCLUSION In PC patients,marital status,castration scheme,and postoperative pain(VAS)score are important factors affecting postoperative anxiety and depression.The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.展开更多
The Nigerian oil sands represent the largest oil sand deposit in Africa, yet there is little published information on the distribution and potential health and ecological risks of trace elements in the oil resource. I...The Nigerian oil sands represent the largest oil sand deposit in Africa, yet there is little published information on the distribution and potential health and ecological risks of trace elements in the oil resource. In the present study, we investigated the distribution pattern of 18trace elements(including biophile and chalcophile elements) as well as the estimated risks associated with exposure to these elements. The results of the study indicated that Fe was the most abundant element, with a mean concentration of 22,131 mg/kg while Br had the lowest mean concentration of 48 mg/kg. The high occurrence of Fe and Ti suggested a possible occurrence of ilmenite(Fe TiO_(3)) in the oil sands. Source apportionment using positive matrix factorization showed that the possible sources of detected elements in the oil sands were geogenic, metal production, and crustal. The contamination factor, geo-accumulation index, modified degree of contamination, pollution load index, and Nemerow pollution index indicated that the oil sands are heavily polluted by the elements. Health risk assessment showed that children were relatively more susceptible to the potentially toxic elements in the oil sands principally via ingestion exposure route(HQ > 1E-04). Cancer risks from inhalation are unlikely due to CR < 1E-06 but ingestion and dermal contact pose severe risks(CR > 1E-04). The high concentrations of the elements pose serious threats due to the potential for atmospheric transport, bioaccessibility, and bioavailability.展开更多
Historical forest fire risk databases are vital for evaluating the effectiveness of past forest management approaches,enhancing forest fire warnings and emergency response capabilities,and accurately budgeting potenti...Historical forest fire risk databases are vital for evaluating the effectiveness of past forest management approaches,enhancing forest fire warnings and emergency response capabilities,and accurately budgeting potential carbon emissions resulting from fires.However,due to the unavailability of spatial information technology,such databases are extremely difficult to build reliably and completely in the non-satellite era.This study presented an improved forest fire risk reconstruction framework that integrates a deep learning-based time series prediction model and spatial interpolation to address the challenge in Sichuan Province,southwestern China.First,the forest fire danger index(FFDI)was improved by supplementing slope and aspect information.We compared the performances of three time series models,namely,the autoregressive integrated moving average(ARIMA),Prophet and long short-term memory(LSTM)in predicting the modified forest fire danger index(MFFDI).The bestperforming model was used to retrace the MFFDI of individual stations from 1941 to 1970.Following this,the Anusplin spatial interpolation method was used to map the distributions of the MFFDI at five-year intervals,which were then subjected to weighted overlay with the distance-to-river layer to generate forest fire risk maps for reconstructing the forest fire danger database.The results revealed LSTM as the most accurate in fitting and predicting the historical MFFDI,with a fitting determination coefficient(R^2)of 0.709,mean square error(MSE)of0.047,and validation R^2 and MSE of 0.508 and 0.11,respectively.Independent validation of the predicted forest fire risk maps indicated that 5 out of 7 historical forest fire events were located in forest fire-prone areas,which is higher than the results determined from the original FFDI(2 out of 7).This proves the effectiveness of the improved MFFDI and indicates a high level of reliability of the historical forest fire risk reconstruction method proposed in this study.展开更多
Objective:To investigate the prevalence and risk factors associated with long COVID symptoms among children and adolescents who have recovered from COVID-19.Methods:This study applied a cross-sectional approach within...Objective:To investigate the prevalence and risk factors associated with long COVID symptoms among children and adolescents who have recovered from COVID-19.Methods:This study applied a cross-sectional approach within community settings in a southern province of Vietnam.A structured questionnaire featuring socio-demographic information and common long COVID symptoms was employed.Phi correlation coefficients assessed associations among pairs of long COVID symptoms.Additionally,multivariable logistic regression models were performed to investigate the risk factors of long COVID in recovered COVID-19 children and adolescents.Results:Among 422 participants,39.3%reported long COVID symptoms,with a prevalence of 45.2%(SD=0.5)in children and 22.2%(SD=0.4)in adolescents.Common symptoms reported were cough 34.6%(SD=0.5),fatigue 20.6%(SD=0.4),shortness of breath 10.9%(SD=0.3),and lack of appetite 6.6%(SD=0.3).Concerning risk factors of long COVID,a higher risk was observed among demographic groups,including girls(OR 1.25,95%CI 1.15-1.37;P<0.001,reference:boys),children compared to adolescents(OR 1.24,95%CI 1.12-1.37;P<0.001),overweight individuals(OR 1.14,95%CI 1.02-1.27;P=0.018,reference:healthy weight),and participants without any COVID-19 vaccination(OR 1.36,95%CI 1.20-1.54;P<0.001),or have received only one single dose(OR 1.35,95%CI 1.10-1.64;P=0.004)compared to those who have received two doses.Besides,patients with a COVID-19 treatment duration exceeding two weeks also had a higher risk of long COVID(OR 1.32,95%CI 1.09-1.60;P=0.003)than those who recovered less than seven days.Conclusions:The insights from this study provide crucial guidance for predicting the factors associated with the occurrence of long COVID in pediatric patients,contributing to strategic interventions aimed at mitigating the long COVID risks among children and adolescents in Vietnam.展开更多
With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk manageme...With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management,but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry.Therefore,an improved risk assessment algorithm(PS-AE-LSTM)based on long short-term memory network(LSTM)with autoencoder(AE)is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels.Firstly,based on the normal distribution characteristics of flight data,a probability severity(PS)model is established to enhance the quality of risk assessment labels.Secondly,autoencoder is introduced to reconstruct the flight parameter data to improve the data quality.Finally,utilizing the time-series nature of flight data,a long and short-termmemory network is used to classify the risk level and improve the accuracy of risk assessment.Thus,a risk assessment experimentwas conducted to analyze a fleet landing phase dataset using the PS-AE-LSTMalgorithm to assess the risk level associated with aircraft hard landing events.The results show that the proposed algorithm achieves an accuracy of 86.45%compared with seven baseline models and has excellent risk assessment capability.展开更多
Fluoride and nitrate enriched groundwater are potential threats to the safety of the groundwater supply that may cause significant effects on human health and public safety,especially in aggregated population areas an...Fluoride and nitrate enriched groundwater are potential threats to the safety of the groundwater supply that may cause significant effects on human health and public safety,especially in aggregated population areas and economic hubs.This study focuses on the high F^(−)and NO_(3)^(−)concentration groundwater in Tongzhou District,Beijing,North China.A total of 36 groundwater samples were collected to analyze the hydrochemical characteristics,elucidate genetic mechanisms and evaluate the potential human health risks.The results of the analysis indicate:Firstly,most of the groundwater samples are characterized by Mg-HCO_(3) and Na-HCO_(3) with the pH ranging from 7.19 to 8.28 and TDS with a large variation across the range 471-2337 mg/L.The NO_(3)^(−)concentration in 38.89%groundwater samples and the F^(−)concentration in 66.67%groundwater samples exceed the permissible limited value.Secondly,F^(−)in groundwater originates predominantly from water-rock interactions and the fluorite dissolution,which is also regulated by cation exchange,competitive adsorption of HCO_(3)−and an alkaline environment.Thirdly,the effect of sewage disposal and agricultural activities have a significant effect on high NO3-concentration,while the high F^(−)concentration is less influenced by anthropogenic activity.The alkaline environment favors nitrification,thus being conducive to the production of NO_(3)^(−).Finally,the health risk assessment is evaluated for different population groups.The results indicate that high NO_(3)^(−)and F^(−)concentration in groundwater would have the largest threat to children’s health.The findings of this study could contribute to the provision of a scientific basis for groundwater supply policy formulation relating to public health in Tongzhou District.展开更多
BACKGROUND:Postpartum posttraumatic stress disorder(PTSD)can occur in women who give birth after emergency admission.The identification of risk factors for this condition is crucial for developing effective preventive...BACKGROUND:Postpartum posttraumatic stress disorder(PTSD)can occur in women who give birth after emergency admission.The identification of risk factors for this condition is crucial for developing effective preventive measures.This retrospective study aimed to explore the incidence and risk factors for postpartum PTSD in women who give birth after emergency admission.METHODS:Medical records of women who gave birth after emergency admission were collected between March 2021 and April 2023.The patients’general conditions and perinatal clinical indicators were recorded.The puerperae were divided into PTSD group and control group based on symptom occurrence at six weeks postpartum.Multivariate logistic regression analysis was performed to identify risk factors.RESULTS:A total of 276 puerperae were included,with a PTSD incidence of 20.3% at six weeks postpartum.Multivariate logistic regression analysis identified emergency cesarean section(odds ratio[OR]=2.102;95%confidence interval[CI]:1.114-3.966,P=0.022),admission to the emergency department after midnight(12:00 AM)(OR=2.245;95%CI:1.170-4.305,P<0.001),and cervical dilation(OR=3.203;95%CI:1.670–6.141,P=0.039)as independent risk factors for postpartum PTSD.Analgesia pump use(OR=0.500;95%CI:0.259–0.966,P=0.015)was found to be a protective factor against postpartum PTSD.CONCLUSION:Emergency cesarean section,admission to the emergency department after midnight,and cervical dilation were identified as independent risk factors for postpartum PTSD,while analgesic pump use was a protective factor.These findings provide insights for developing more effective preventive measures for women who give birth after emergency admission.展开更多
Objective This study explored the potentially modifiable factors for depression and major depressive disorder(MDD)from the MR-Base database and further evaluated the associations between drug targets with MDD.Methods ...Objective This study explored the potentially modifiable factors for depression and major depressive disorder(MDD)from the MR-Base database and further evaluated the associations between drug targets with MDD.Methods We analyzed two-sample of Mendelian randomization(2SMR)using genetic variant depression(n=113,154)and MDD(n=208,811)from Genome-Wide Association Studies(GWAS).Separate calculations were performed with modifiable risk factors from MR-Base for 1,001 genomes.The MR analysis was performed by screening drug targets with MDD in the DrugBank database to explore the therapeutic targets for MDD.Inverse variance weighted(IVW),fixed-effect inverse variance weighted(FE-IVW),MR-Egger,weighted median,and weighted mode were used for complementary calculation.Results The potential causal relationship between modifiable risk factors and depression contained 459 results for depression and 424 for MDD.Also,the associations between drug targets and MDD showed that SLC6A4,GRIN2A,GRIN2C,SCN10A,and IL1B expression are associated with an increased risk of depression.In contrast,ADRB1,CHRNA3,HTR3A,GSTP1,and GABRG2 genes are candidate protective factors against depression.Conclusion This study identified the risk factors causally associated with depression and MDD,and estimated 10 drug targets with significant impact on MDD,providing essential information for formulating strategies to prevent and treat depression.展开更多
In the Tano River Basin,groundwater serves as a crucial resource;however,its quantity and quality with regard to trace elements and microbiological loadings remain poorly understood due to the lack of groundwater logs...In the Tano River Basin,groundwater serves as a crucial resource;however,its quantity and quality with regard to trace elements and microbiological loadings remain poorly understood due to the lack of groundwater logs and limited water research.This study presents a comprehensive analysis of the Tano River Basin,focusing on three key objectives.First,it investigated the aquifer hydraulic parameters and the results showed significant spatial variations in borehole depths,yields,transmissivity,hydraulic conductivity,and specific capacity.Deeper boreholes were concentrated in the northeastern and southeastern zones,while geological formations,particu-larly the Apollonian Formation,exhibit a strong influence on borehole yields.The study identified areas with high transmissivity and hydraulic conductivity in the southern and eastern regions,suggesting good groundwater avail-ability and suitability for sustainable water supply.Sec-ondly,the research investigated the groundwater quality and observed that the majority of borehole samples fall within WHO(Guidelines for Drinking-water Quality,Environmental Health Criteria,Geneva,2011,2017.http://www.who.int)limit.However,some samples have pH levels below the standards,although the groundwater generally qualifies as freshwater.The study further explores hydrochemical facies and health risk assessment,highlighting the dominance of Ca–HCO3 water type.Trace element analysis reveals minimal health risks from most elements,with chromium(Cr)as the primary contributor to chronic health risk.Overall,this study has provided a key insights into the Tano River Basin’s hydrogeology and associated health risks.The outcome of this research has contributed to the broader understanding of hydrogeologi-cal dynamics and the importance of managing groundwater resources sustainably in complex geological environments.展开更多
A resilience-incorporated risk assessment framework is proposed and demonstrated in this study to manifest the advantageous seismic resilience of precast concrete frame(PCF)structures with“dry”connections in terms o...A resilience-incorporated risk assessment framework is proposed and demonstrated in this study to manifest the advantageous seismic resilience of precast concrete frame(PCF)structures with“dry”connections in terms of their low damage and rapid recovery.The framework integrates various uncertainties in the seismic hazard,fragility,capacity,demand,loss functions,and post-earthquake recovery.In this study,the PCF structures are distinguished from ordinary reinforced concrete frame(RCF)structures by characterizing multiple limit states for the PCF based on its unique damage mechanisms.Accordingly,probabilistic story-wise pushover analyses are performed to yield story-wise capacities for the predefined limit states.In the seismic resilience analysis,a step-wise recovery model is proposed to idealize the functionality recovery process,with separate considerations of the repair and non-repair events.The recovery model leverages the economic loss and downtime to delineate the stochastic post-earthquake recovery curves for the resilience loss estimation.As such,contingencies in the probabilistic post-earthquake repairs are incorporated and the empirical judgments on the recovery parameters are largely circumvented.The proposed framework is demonstrated through a comparative study between two“dry”connected PCFs and one RCF designed as alternative structural systems for a prototype building.The results from the risk quantification indicate that the PCFs show reduced loss hazards and lower expected losses relative to the RCF.Particularly,the PCF equipped with energy dissipation devices at the“dry”connections largely reduces the expected economic loss,downtime,and resilience loss by 29%,56%,and 60%,respectively,compared to the RCF.展开更多
Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In thi...Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In this paper,a machine learning-based decision-making mechanism for risk assessment of CVD is designed.In this mechanism,the logistics regression analysismethod and factor analysismodel are used to select age,obesity degree,blood pressure,blood fat,blood sugar,smoking status,drinking status,and exercise status as the main pathogenic factors of CVD,and an index systemof risk assessment for CVD is established.Then,a two-stage model combining K-means cluster analysis and random forest(RF)is proposed to evaluate and predict the risk of CVD,and the predicted results are compared with the methods of Bayesian discrimination,K-means cluster analysis and RF.The results show that thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results.展开更多
Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to ob...Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.展开更多
Consider a nonstandard continuous-time bidimensional risk model with constant force of interest,in which the two classes of claims with subexponential distributions satisfy a general dependence structure and each pair...Consider a nonstandard continuous-time bidimensional risk model with constant force of interest,in which the two classes of claims with subexponential distributions satisfy a general dependence structure and each pair of the claim-inter-arrival times is arbitrarily dependent.Under some mild conditions,we achieve a locally uniform approximation of the finite-time ruin probability for all time horizon within a finite interval.If we further assume that each pair of the claim-inter-arrival times is negative quadrant dependent and the two classes of claims are consistently-varying-tailed,it shows that the above obtained approximation is also globally uniform for all time horizon within an infinite interval.展开更多
Objective Hypertriglyceridemic waist(HW),hypertriglyceridemic waist-to-height ratio(HWHtR),and waist-to-hip ratio(WHR)have been shown to be indicators of cardiometabolic risk factors.However,it is not clear which indi...Objective Hypertriglyceridemic waist(HW),hypertriglyceridemic waist-to-height ratio(HWHtR),and waist-to-hip ratio(WHR)have been shown to be indicators of cardiometabolic risk factors.However,it is not clear which indicator is more suitable for children and adolescents.We aimed to investigate the relationship between HW,HWHtR,WHR,and cardiovascular risk factors clustering to determine the best screening tools for cardiometabolic risk in children and adolescents.Methods This was a national cross-sectional study.Anthropometric and biochemical variables were assessed in approximately 70,000 participants aged 6–18 years from seven provinces in China.Demographics,physical activity,dietary intake,and family history of chronic diseases were obtained through questionnaires.ANOVA,χ2 and logistic regression analysis was conducted.Results A significant sex difference was observed for HWHtR and WHR,but not for HW phenotype.The risk of cardiometabolic health risk factor clustering with HW phenotype or the HWHtR phenotype was significantly higher than that with the non-HW or non-HWHtR phenotypes among children and adolescents(HW:OR=12.22,95%CI:9.54-15.67;HWHtR:OR=9.70,95%CI:6.93-13.58).Compared with the HW and HWHtR phenotypes,the association between risk of cardiometabolic health risk factors(CHRF)clustering and high WHR was much weaker and not significant(WHR:OR=1.14,95%CI:0.97-1.34).Conclusion Compared with HWHtR and WHR,the HW phenotype is a more convenient indicator with higher applicability to screen children and adolescents for cardiovascular risk factors.展开更多
It is common to observe the epidemic risk perception(ERP)and a decline in subjective well-being(SWB)in the context of public health events,such as Corona Virus Disease 2019(COVID-19).However,there have been few studie...It is common to observe the epidemic risk perception(ERP)and a decline in subjective well-being(SWB)in the context of public health events,such as Corona Virus Disease 2019(COVID-19).However,there have been few studies exploring the impact of individuals’ERP within living space on their SWB,especially from a geographical and daily activity perspective after the resumption of work and other activities following a wave of the pandemic.In this paper,we conducted a study with 789 participants in urban China,measuring their ERP within living space and examining its influence on their SWB using path analysis.The results indicated that individuals’ERP within their living space had a significant negative effect on their SWB.The density of certain types of facilities within their living space,such as bus stops,subway stations,restaurants,fast food shops,convenience shops,hospitals,and public toilets,had a significantly negative impact on their SWB,mediated by their ERP within living space.Additionally,participation in out-of-home work and other activities not only increased individuals’ERP within living space,but also strengthened its negative effect on their SWB.展开更多
BACKGROUND Deep learning provides an efficient automatic image recognition method for small bowel(SB)capsule endoscopy(CE)that can assist physicians in diagnosis.However,the existing deep learning models present some ...BACKGROUND Deep learning provides an efficient automatic image recognition method for small bowel(SB)capsule endoscopy(CE)that can assist physicians in diagnosis.However,the existing deep learning models present some unresolved challenges.AIM To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks,and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups.METHODS The proposed model represents a two-stage method that combined image classification with object detection.First,we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images,normal SB mucosa images,and invalid images.Then,the improved YOLO-V5 detection model was utilized to detect the type of lesion and its risk of bleeding,and the location of the lesion was marked.We constructed training and testing sets and compared model-assisted reading with physician reading.RESULTS The accuracy of the model constructed in this study reached 98.96%,which was higher than the accuracy of other systems using only a single module.The sensitivity,specificity,and accuracy of the model-assisted reading detection of all images were 99.17%,99.92%,and 99.86%,which were significantly higher than those of the endoscopists’diagnoses.The image processing time of the model was 48 ms/image,and the image processing time of the physicians was 0.40±0.24 s/image(P<0.001).CONCLUSION The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images,which enhances the diagnostic efficiency of physicians and improves the ability of physicians to identify high-risk bleeding groups.展开更多
The social transformation brought aboutby digital technology is deeply impacting various industries.Digital education products, with core technologiessuch as 5G, AI, IoT (Internet of Things),etc., are continuously pen...The social transformation brought aboutby digital technology is deeply impacting various industries.Digital education products, with core technologiessuch as 5G, AI, IoT (Internet of Things),etc., are continuously penetrating areas such as teaching,management, and evaluation. Apps, miniprograms,and emerging large-scale models are providingexcellent knowledge performance and flexiblecross-media output. However, they also exposerisks such as content discrimination and algorithmcommercialization. This paper conducts anevidence-based analysis of digital education productrisks from four dimensions: “digital resourcesinformationdissemination-algorithm design-cognitiveassessment”. It breaks through corresponding identificationtechnologies and, relying on the diverse characteristicsof governance systems, explores governancestrategies for digital education products from the threedomains of “regulators-developers-users”.展开更多
BACKGROUND The lack of specific symptoms of gastric cancer(GC)causes great challenges in its early diagnosis.Thus it is essential to identify the risk factors for early diagnosis and treatment of GC and to improve the...BACKGROUND The lack of specific symptoms of gastric cancer(GC)causes great challenges in its early diagnosis.Thus it is essential to identify the risk factors for early diagnosis and treatment of GC and to improve the survival rates.AIM To assist physicians in identifying changes in the output of publications and research hotspots related to risk factors for GC,constructing a list of key risk factors,and providing a reference for early identification of patients at high risk for GC.METHODS Research articles on risk factors for GC were searched in the Web of Science core collection,and relevant information was extracted after screening.The literature was analyzed using Microsoft Excel 2019,CiteSpace V,and VOSviewer 1.6.18.RESULTS A total of 2514 papers from 72 countries and 2507 research institutions were retrieved.China(n=1061),National Cancer Center(n=138),and Shoichiro Tsugane(n=36)were the most productive country,institution,or author,respectively.The research hotspots in the study of risk factors for GC are summarized in four areas,namely:Helicobacter pylori(H.pylori)infection,single nucleotide polymorphism,bio-diagnostic markers,and GC risk prediction models.CONCLUSION In this study,we found that H.pylori infection is the most significant risk factor for GC;single-nucleotide polymorphism(SNP)is the most dominant genetic factor for GC;bio-diagnostic markers are the most promising diagnostic modality for GC.GC risk prediction models are the latest current research hotspot.We conclude that the most important risk factors for the development of GC are H.pylori infection,SNP,smoking,diet,and alcohol.展开更多
基金This work was supported by the Ministry of Higher Education(MOHE),Malaysia,through Fundamental Research Grant Scheme(FRGS/1/2022/SKK10/UTAR/02/1)Universiti Tunku Abdul Rahman,Malaysia,through UTAR-Research Grant(IPSR/RMC/UTARRF/2021-C2/L08)MBBS Community Health Project(2022/2023),UTAR.
文摘Objective:To investigate the interrelationship between cardiovascular health awareness,risk perception,behavioural intention,and INTERHEART risk stratification in a middle-aged adult population in Malaysia.Methods:A cross-sectional survey with convenience sampling was conducted during November 2022 and January 2023.Participants completed validated questionnaires assessing cardiovascular health awareness,risk perception of cardiovascular diseases,behavioural intention towards adopting healthy habits,and INTERHEART risk stratification score(IHRS)based on established risk factors.A total of 602 respondents were included in the final analysis.Data were analysed with independent t-test/one-way ANOVA or Mann-Whitney/Kruskal-Wallis to test the differences,Pearson correlation or linear regression test to analyze the association of independent and dependent variables.Results:There was a significant positive correlation between medical knowledge related to cardiovascular disease(CVD)and knowledge related to CVD risk prevention,risk perception,behavioural intention and IHRS(P<0.05,Pearson correlation).Notably,individuals with higher IHRS tended to have lower knowledge related to CVD and CVD risk prevention,risk perception,and behavioural intention.Males,laborers,active/former smokers,individuals with lower household income and educational levels,those involved in occupations not related to the healthcare sector,and those who did not receive the CVD health brochure or are unaware of health self-assessment tools are likely to have lower levels of knowledge,risk perception,and poorer behavioural intention regarding cardiovascular health(P<0.05,one-way ANOVA).While educational level,smoking status,awareness about CVD poster,self-assessment tools were repeatedly significantly associated with knowledge related to CVD and CVD risk prevention,risk perception,behavioral intention and/or IHRS(P<0.05,linear regression).Conclusions:These findings underscore the importance of promoting cardiovascular health awareness and risk perception among middle-aged adults to foster positive BI and reduce CVD risk.Tailored interventions targeting specific risk factors identified by INTERHEART may enhance risk stratification accuracy and facilitate targeted preventive strategies.
基金This workwas supported by the Medical and Health Science and Technology Project of Zhejiang Province(No.2021KY180).
文摘Objectives:Anastomotic leakage(AL)stands out as a prevalent and severe complication following gastric cancer surgery.It frequently precipitates additional serious complications,significantly influencing the overall survival time of patients.This study aims to enhance the risk-assessment strategy for AL following gastrectomy for gastric cancer.Methods:This study included a derivation cohort and validation cohort.The derivation cohort included patients who underwent radical gastrectomy at Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,from January 1,2015 to December 31,2020.An evidence-based predictor questionnaire was crafted through extensive literature review and panel discussions.Based on the questionnaire,inpatient data were collected to form a model-derivation cohort.This cohort underwent both univariate and multivariate analyses to identify factors associated with AL events,and a logistic regression model with stepwise regression was developed.A 5-fold cross-validation ensured model reliability.The validation cohort included patients from August 1,2021 to December 31,2021 at the same hospital.Using the same imputation method,we organized the validation-queue data.We then employed the risk-prediction model constructed in the earlier phase of the study to predict the risk of AL in the subjects included in the validation queue.We compared the predictions with the actual occurrence,and evaluated the external validation performance of the model using model-evaluation indicators such as the area under the receiver operating characteristic curve(AUROC),Brier score,and calibration curve.Results:The derivation cohort included 1377 patients,and the validation cohort included 131 patients.The independent predictors of AL after radical gastrectomy included age65 y,preoperative albumin<35 g/L,resection extent,operative time240 min,and intraoperative blood loss90 mL.The predictive model exhibited a solid AUROC of 0.750(95%CI:0.694e0.806;p<0.001)with a Brier score of 0.049.The 5-fold cross-validation confirmed these findings with a calibrated C-index of 0.749 and an average Brier score of 0.052.External validation showed an AUROC of 0.723(95%CI:0.564e0.882;p?0.006)and a Brier score of 0.055,confirming reliability in different clinical settings.Conclusions:We successfully developed a risk-prediction model for AL following radical gastrectomy.This tool will aid healthcare professionals in anticipating AL,potentially reducing unnecessary interventions.
文摘BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis.Therefore,attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment.AIM To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model.METHODS A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022.The patient cohort was divided into a training group(n=84)and a validation group(n=36)at a ratio of 7:3.The patients’anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale(SAS)and the Selfrating Depression Scale(SDS),respectively.Logistic regression was used to analyze the risk factors affecting negative mood,and a risk prediction model was constructed.RESULTS In the training group,35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50,respectively.Based on the scores,we further subclassified patients into two groups:a bad mood group(n=35)and an emotional stability group(n=49).Multivariate logistic regression analysis showed that marital status,castration scheme,and postoperative Visual Analogue Scale(VAS)score were independent risk factors affecting a patient's bad mood(P<0.05).In the training and validation groups,patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients(P<0.0001).The area under the curve(AUC)of the risk prediction model for predicting bad mood in the training group was 0.743,the specificity was 70.96%,and the sensitivity was 66.03%,while in the validation group,the AUC,specificity,and sensitivity were 0.755,66.67%,and 76.19%,respectively.The Hosmer-Lemeshow test showed aχ^(2) of 4.2856,a P value of 0.830,and a C-index of 0.773(0.692-0.854).The calibration curve revealed that the predicted curve was basically consistent with the actual curve,and the calibration curve showed that the prediction model had good discrimination and accuracy.Decision curve analysis showed that the model had a high net profit.CONCLUSION In PC patients,marital status,castration scheme,and postoperative pain(VAS)score are important factors affecting postoperative anxiety and depression.The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.
文摘The Nigerian oil sands represent the largest oil sand deposit in Africa, yet there is little published information on the distribution and potential health and ecological risks of trace elements in the oil resource. In the present study, we investigated the distribution pattern of 18trace elements(including biophile and chalcophile elements) as well as the estimated risks associated with exposure to these elements. The results of the study indicated that Fe was the most abundant element, with a mean concentration of 22,131 mg/kg while Br had the lowest mean concentration of 48 mg/kg. The high occurrence of Fe and Ti suggested a possible occurrence of ilmenite(Fe TiO_(3)) in the oil sands. Source apportionment using positive matrix factorization showed that the possible sources of detected elements in the oil sands were geogenic, metal production, and crustal. The contamination factor, geo-accumulation index, modified degree of contamination, pollution load index, and Nemerow pollution index indicated that the oil sands are heavily polluted by the elements. Health risk assessment showed that children were relatively more susceptible to the potentially toxic elements in the oil sands principally via ingestion exposure route(HQ > 1E-04). Cancer risks from inhalation are unlikely due to CR < 1E-06 but ingestion and dermal contact pose severe risks(CR > 1E-04). The high concentrations of the elements pose serious threats due to the potential for atmospheric transport, bioaccessibility, and bioavailability.
基金the following grants:The National Key R&D Program of China(2019YFA0606600)the Natural Science Foundation of China(31971577)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)。
文摘Historical forest fire risk databases are vital for evaluating the effectiveness of past forest management approaches,enhancing forest fire warnings and emergency response capabilities,and accurately budgeting potential carbon emissions resulting from fires.However,due to the unavailability of spatial information technology,such databases are extremely difficult to build reliably and completely in the non-satellite era.This study presented an improved forest fire risk reconstruction framework that integrates a deep learning-based time series prediction model and spatial interpolation to address the challenge in Sichuan Province,southwestern China.First,the forest fire danger index(FFDI)was improved by supplementing slope and aspect information.We compared the performances of three time series models,namely,the autoregressive integrated moving average(ARIMA),Prophet and long short-term memory(LSTM)in predicting the modified forest fire danger index(MFFDI).The bestperforming model was used to retrace the MFFDI of individual stations from 1941 to 1970.Following this,the Anusplin spatial interpolation method was used to map the distributions of the MFFDI at five-year intervals,which were then subjected to weighted overlay with the distance-to-river layer to generate forest fire risk maps for reconstructing the forest fire danger database.The results revealed LSTM as the most accurate in fitting and predicting the historical MFFDI,with a fitting determination coefficient(R^2)of 0.709,mean square error(MSE)of0.047,and validation R^2 and MSE of 0.508 and 0.11,respectively.Independent validation of the predicted forest fire risk maps indicated that 5 out of 7 historical forest fire events were located in forest fire-prone areas,which is higher than the results determined from the original FFDI(2 out of 7).This proves the effectiveness of the improved MFFDI and indicates a high level of reliability of the historical forest fire risk reconstruction method proposed in this study.
文摘Objective:To investigate the prevalence and risk factors associated with long COVID symptoms among children and adolescents who have recovered from COVID-19.Methods:This study applied a cross-sectional approach within community settings in a southern province of Vietnam.A structured questionnaire featuring socio-demographic information and common long COVID symptoms was employed.Phi correlation coefficients assessed associations among pairs of long COVID symptoms.Additionally,multivariable logistic regression models were performed to investigate the risk factors of long COVID in recovered COVID-19 children and adolescents.Results:Among 422 participants,39.3%reported long COVID symptoms,with a prevalence of 45.2%(SD=0.5)in children and 22.2%(SD=0.4)in adolescents.Common symptoms reported were cough 34.6%(SD=0.5),fatigue 20.6%(SD=0.4),shortness of breath 10.9%(SD=0.3),and lack of appetite 6.6%(SD=0.3).Concerning risk factors of long COVID,a higher risk was observed among demographic groups,including girls(OR 1.25,95%CI 1.15-1.37;P<0.001,reference:boys),children compared to adolescents(OR 1.24,95%CI 1.12-1.37;P<0.001),overweight individuals(OR 1.14,95%CI 1.02-1.27;P=0.018,reference:healthy weight),and participants without any COVID-19 vaccination(OR 1.36,95%CI 1.20-1.54;P<0.001),or have received only one single dose(OR 1.35,95%CI 1.10-1.64;P=0.004)compared to those who have received two doses.Besides,patients with a COVID-19 treatment duration exceeding two weeks also had a higher risk of long COVID(OR 1.32,95%CI 1.09-1.60;P=0.003)than those who recovered less than seven days.Conclusions:The insights from this study provide crucial guidance for predicting the factors associated with the occurrence of long COVID in pediatric patients,contributing to strategic interventions aimed at mitigating the long COVID risks among children and adolescents in Vietnam.
基金the National Natural Science Foundation of China(U2033213)the Fundamental Research Funds for the Central Universities(FZ2021ZZ01,FZ2022ZX50).
文摘With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management,but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry.Therefore,an improved risk assessment algorithm(PS-AE-LSTM)based on long short-term memory network(LSTM)with autoencoder(AE)is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels.Firstly,based on the normal distribution characteristics of flight data,a probability severity(PS)model is established to enhance the quality of risk assessment labels.Secondly,autoencoder is introduced to reconstruct the flight parameter data to improve the data quality.Finally,utilizing the time-series nature of flight data,a long and short-termmemory network is used to classify the risk level and improve the accuracy of risk assessment.Thus,a risk assessment experimentwas conducted to analyze a fleet landing phase dataset using the PS-AE-LSTMalgorithm to assess the risk level associated with aircraft hard landing events.The results show that the proposed algorithm achieves an accuracy of 86.45%compared with seven baseline models and has excellent risk assessment capability.
基金supported by the project of China Geological Survey(Grant No.DD20221677-2)the fundamental research funds of Chinese Academy of Geological Sciences Basal Research Fund(Grant No.JKYQN202307).
文摘Fluoride and nitrate enriched groundwater are potential threats to the safety of the groundwater supply that may cause significant effects on human health and public safety,especially in aggregated population areas and economic hubs.This study focuses on the high F^(−)and NO_(3)^(−)concentration groundwater in Tongzhou District,Beijing,North China.A total of 36 groundwater samples were collected to analyze the hydrochemical characteristics,elucidate genetic mechanisms and evaluate the potential human health risks.The results of the analysis indicate:Firstly,most of the groundwater samples are characterized by Mg-HCO_(3) and Na-HCO_(3) with the pH ranging from 7.19 to 8.28 and TDS with a large variation across the range 471-2337 mg/L.The NO_(3)^(−)concentration in 38.89%groundwater samples and the F^(−)concentration in 66.67%groundwater samples exceed the permissible limited value.Secondly,F^(−)in groundwater originates predominantly from water-rock interactions and the fluorite dissolution,which is also regulated by cation exchange,competitive adsorption of HCO_(3)−and an alkaline environment.Thirdly,the effect of sewage disposal and agricultural activities have a significant effect on high NO3-concentration,while the high F^(−)concentration is less influenced by anthropogenic activity.The alkaline environment favors nitrification,thus being conducive to the production of NO_(3)^(−).Finally,the health risk assessment is evaluated for different population groups.The results indicate that high NO_(3)^(−)and F^(−)concentration in groundwater would have the largest threat to children’s health.The findings of this study could contribute to the provision of a scientific basis for groundwater supply policy formulation relating to public health in Tongzhou District.
基金Science and Technology Development Plan Project of Suzhou(SKJYD2021035)Science and Technology Development Plan Project of Suzhou(SKJYD2022078)The Key Project Research Fund of the Second Affiliated Hospital of Wannan Medical College(YK2023Z04)。
文摘BACKGROUND:Postpartum posttraumatic stress disorder(PTSD)can occur in women who give birth after emergency admission.The identification of risk factors for this condition is crucial for developing effective preventive measures.This retrospective study aimed to explore the incidence and risk factors for postpartum PTSD in women who give birth after emergency admission.METHODS:Medical records of women who gave birth after emergency admission were collected between March 2021 and April 2023.The patients’general conditions and perinatal clinical indicators were recorded.The puerperae were divided into PTSD group and control group based on symptom occurrence at six weeks postpartum.Multivariate logistic regression analysis was performed to identify risk factors.RESULTS:A total of 276 puerperae were included,with a PTSD incidence of 20.3% at six weeks postpartum.Multivariate logistic regression analysis identified emergency cesarean section(odds ratio[OR]=2.102;95%confidence interval[CI]:1.114-3.966,P=0.022),admission to the emergency department after midnight(12:00 AM)(OR=2.245;95%CI:1.170-4.305,P<0.001),and cervical dilation(OR=3.203;95%CI:1.670–6.141,P=0.039)as independent risk factors for postpartum PTSD.Analgesia pump use(OR=0.500;95%CI:0.259–0.966,P=0.015)was found to be a protective factor against postpartum PTSD.CONCLUSION:Emergency cesarean section,admission to the emergency department after midnight,and cervical dilation were identified as independent risk factors for postpartum PTSD,while analgesic pump use was a protective factor.These findings provide insights for developing more effective preventive measures for women who give birth after emergency admission.
基金supported by Natural Science Foundation of Shandong ProvinceChina[ZR2022MH115]the National Natural Science Foundation of China[81301479,82202593]。
文摘Objective This study explored the potentially modifiable factors for depression and major depressive disorder(MDD)from the MR-Base database and further evaluated the associations between drug targets with MDD.Methods We analyzed two-sample of Mendelian randomization(2SMR)using genetic variant depression(n=113,154)and MDD(n=208,811)from Genome-Wide Association Studies(GWAS).Separate calculations were performed with modifiable risk factors from MR-Base for 1,001 genomes.The MR analysis was performed by screening drug targets with MDD in the DrugBank database to explore the therapeutic targets for MDD.Inverse variance weighted(IVW),fixed-effect inverse variance weighted(FE-IVW),MR-Egger,weighted median,and weighted mode were used for complementary calculation.Results The potential causal relationship between modifiable risk factors and depression contained 459 results for depression and 424 for MDD.Also,the associations between drug targets and MDD showed that SLC6A4,GRIN2A,GRIN2C,SCN10A,and IL1B expression are associated with an increased risk of depression.In contrast,ADRB1,CHRNA3,HTR3A,GSTP1,and GABRG2 genes are candidate protective factors against depression.Conclusion This study identified the risk factors causally associated with depression and MDD,and estimated 10 drug targets with significant impact on MDD,providing essential information for formulating strategies to prevent and treat depression.
文摘In the Tano River Basin,groundwater serves as a crucial resource;however,its quantity and quality with regard to trace elements and microbiological loadings remain poorly understood due to the lack of groundwater logs and limited water research.This study presents a comprehensive analysis of the Tano River Basin,focusing on three key objectives.First,it investigated the aquifer hydraulic parameters and the results showed significant spatial variations in borehole depths,yields,transmissivity,hydraulic conductivity,and specific capacity.Deeper boreholes were concentrated in the northeastern and southeastern zones,while geological formations,particu-larly the Apollonian Formation,exhibit a strong influence on borehole yields.The study identified areas with high transmissivity and hydraulic conductivity in the southern and eastern regions,suggesting good groundwater avail-ability and suitability for sustainable water supply.Sec-ondly,the research investigated the groundwater quality and observed that the majority of borehole samples fall within WHO(Guidelines for Drinking-water Quality,Environmental Health Criteria,Geneva,2011,2017.http://www.who.int)limit.However,some samples have pH levels below the standards,although the groundwater generally qualifies as freshwater.The study further explores hydrochemical facies and health risk assessment,highlighting the dominance of Ca–HCO3 water type.Trace element analysis reveals minimal health risks from most elements,with chromium(Cr)as the primary contributor to chronic health risk.Overall,this study has provided a key insights into the Tano River Basin’s hydrogeology and associated health risks.The outcome of this research has contributed to the broader understanding of hydrogeologi-cal dynamics and the importance of managing groundwater resources sustainably in complex geological environments.
基金National Key Research and Development Program of China under Grant No.2022YFC3803004Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant No.SJCX20_0031Fundamental Research Funds for the Central Universities under Grant No.3205002108D。
文摘A resilience-incorporated risk assessment framework is proposed and demonstrated in this study to manifest the advantageous seismic resilience of precast concrete frame(PCF)structures with“dry”connections in terms of their low damage and rapid recovery.The framework integrates various uncertainties in the seismic hazard,fragility,capacity,demand,loss functions,and post-earthquake recovery.In this study,the PCF structures are distinguished from ordinary reinforced concrete frame(RCF)structures by characterizing multiple limit states for the PCF based on its unique damage mechanisms.Accordingly,probabilistic story-wise pushover analyses are performed to yield story-wise capacities for the predefined limit states.In the seismic resilience analysis,a step-wise recovery model is proposed to idealize the functionality recovery process,with separate considerations of the repair and non-repair events.The recovery model leverages the economic loss and downtime to delineate the stochastic post-earthquake recovery curves for the resilience loss estimation.As such,contingencies in the probabilistic post-earthquake repairs are incorporated and the empirical judgments on the recovery parameters are largely circumvented.The proposed framework is demonstrated through a comparative study between two“dry”connected PCFs and one RCF designed as alternative structural systems for a prototype building.The results from the risk quantification indicate that the PCFs show reduced loss hazards and lower expected losses relative to the RCF.Particularly,the PCF equipped with energy dissipation devices at the“dry”connections largely reduces the expected economic loss,downtime,and resilience loss by 29%,56%,and 60%,respectively,compared to the RCF.
基金This work is supported by the National Natural Science Foundation of China(Nos.72071150,71871174).
文摘Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In this paper,a machine learning-based decision-making mechanism for risk assessment of CVD is designed.In this mechanism,the logistics regression analysismethod and factor analysismodel are used to select age,obesity degree,blood pressure,blood fat,blood sugar,smoking status,drinking status,and exercise status as the main pathogenic factors of CVD,and an index systemof risk assessment for CVD is established.Then,a two-stage model combining K-means cluster analysis and random forest(RF)is proposed to evaluate and predict the risk of CVD,and the predicted results are compared with the methods of Bayesian discrimination,K-means cluster analysis and RF.The results show that thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results.
基金supported by the National Natural Science Foundation of China (No.72071150).
文摘Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.
基金Supported by the Natural Science Foundation of China(12071487,11671404)the Natural Science Foundation of Anhui Province(2208085MA06)+1 种基金the Provincial Natural Science Research Project of Anhui Colleges(KJ2021A0049,KJ2021A0060)Hunan Provincial Innovation Foundation for Postgraduate(CX20200146)。
文摘Consider a nonstandard continuous-time bidimensional risk model with constant force of interest,in which the two classes of claims with subexponential distributions satisfy a general dependence structure and each pair of the claim-inter-arrival times is arbitrarily dependent.Under some mild conditions,we achieve a locally uniform approximation of the finite-time ruin probability for all time horizon within a finite interval.If we further assume that each pair of the claim-inter-arrival times is negative quadrant dependent and the two classes of claims are consistently-varying-tailed,it shows that the above obtained approximation is also globally uniform for all time horizon within an infinite interval.
基金supported by the National Natural Science Foundation of China[no.81903336,Yi-de Yang]the Health Research Project of Hunan Provincial Health Commission[no.202112031516,Yi-de Yang]+3 种基金Scientific Research Fund of Hunan Provincial Education Department[no.22B0038,Yi-de Yang]the Research Team for Reproduction Health and Translational Medicine of Hunan Normal University[2023JC101]Key Project of Developmental Biology and Breeding from Hunan Province[no.2022XKQ0205]Open Project for Postgraduates of Hunan Normal University[no.KF2022019,Tianli Xiao].
文摘Objective Hypertriglyceridemic waist(HW),hypertriglyceridemic waist-to-height ratio(HWHtR),and waist-to-hip ratio(WHR)have been shown to be indicators of cardiometabolic risk factors.However,it is not clear which indicator is more suitable for children and adolescents.We aimed to investigate the relationship between HW,HWHtR,WHR,and cardiovascular risk factors clustering to determine the best screening tools for cardiometabolic risk in children and adolescents.Methods This was a national cross-sectional study.Anthropometric and biochemical variables were assessed in approximately 70,000 participants aged 6–18 years from seven provinces in China.Demographics,physical activity,dietary intake,and family history of chronic diseases were obtained through questionnaires.ANOVA,χ2 and logistic regression analysis was conducted.Results A significant sex difference was observed for HWHtR and WHR,but not for HW phenotype.The risk of cardiometabolic health risk factor clustering with HW phenotype or the HWHtR phenotype was significantly higher than that with the non-HW or non-HWHtR phenotypes among children and adolescents(HW:OR=12.22,95%CI:9.54-15.67;HWHtR:OR=9.70,95%CI:6.93-13.58).Compared with the HW and HWHtR phenotypes,the association between risk of cardiometabolic health risk factors(CHRF)clustering and high WHR was much weaker and not significant(WHR:OR=1.14,95%CI:0.97-1.34).Conclusion Compared with HWHtR and WHR,the HW phenotype is a more convenient indicator with higher applicability to screen children and adolescents for cardiovascular risk factors.
基金Under the auspices of the National Natural Science Foundation of China(No.42271234,42101246,42101223)Hong Kong Research Grants Council General Research Fund Grant(No.14605920,14611621,14606922)+1 种基金Hong Kong Research Grants Council Collaborative Research Fund Grant(No.C4023-20GF)Hong Kong Research Grants Council Research Matching Grants RMG(No.8601219,8601242)。
文摘It is common to observe the epidemic risk perception(ERP)and a decline in subjective well-being(SWB)in the context of public health events,such as Corona Virus Disease 2019(COVID-19).However,there have been few studies exploring the impact of individuals’ERP within living space on their SWB,especially from a geographical and daily activity perspective after the resumption of work and other activities following a wave of the pandemic.In this paper,we conducted a study with 789 participants in urban China,measuring their ERP within living space and examining its influence on their SWB using path analysis.The results indicated that individuals’ERP within their living space had a significant negative effect on their SWB.The density of certain types of facilities within their living space,such as bus stops,subway stations,restaurants,fast food shops,convenience shops,hospitals,and public toilets,had a significantly negative impact on their SWB,mediated by their ERP within living space.Additionally,participation in out-of-home work and other activities not only increased individuals’ERP within living space,but also strengthened its negative effect on their SWB.
基金The Shanxi Provincial Administration of Traditional Chinese Medicine,No.2023ZYYDA2005.
文摘BACKGROUND Deep learning provides an efficient automatic image recognition method for small bowel(SB)capsule endoscopy(CE)that can assist physicians in diagnosis.However,the existing deep learning models present some unresolved challenges.AIM To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks,and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups.METHODS The proposed model represents a two-stage method that combined image classification with object detection.First,we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images,normal SB mucosa images,and invalid images.Then,the improved YOLO-V5 detection model was utilized to detect the type of lesion and its risk of bleeding,and the location of the lesion was marked.We constructed training and testing sets and compared model-assisted reading with physician reading.RESULTS The accuracy of the model constructed in this study reached 98.96%,which was higher than the accuracy of other systems using only a single module.The sensitivity,specificity,and accuracy of the model-assisted reading detection of all images were 99.17%,99.92%,and 99.86%,which were significantly higher than those of the endoscopists’diagnoses.The image processing time of the model was 48 ms/image,and the image processing time of the physicians was 0.40±0.24 s/image(P<0.001).CONCLUSION The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images,which enhances the diagnostic efficiency of physicians and improves the ability of physicians to identify high-risk bleeding groups.
基金supported by the 2022 National Natural Science Foundation of China(No.62277002)the National Key Research and Development Program of China(2022YFC3303500).
文摘The social transformation brought aboutby digital technology is deeply impacting various industries.Digital education products, with core technologiessuch as 5G, AI, IoT (Internet of Things),etc., are continuously penetrating areas such as teaching,management, and evaluation. Apps, miniprograms,and emerging large-scale models are providingexcellent knowledge performance and flexiblecross-media output. However, they also exposerisks such as content discrimination and algorithmcommercialization. This paper conducts anevidence-based analysis of digital education productrisks from four dimensions: “digital resourcesinformationdissemination-algorithm design-cognitiveassessment”. It breaks through corresponding identificationtechnologies and, relying on the diverse characteristicsof governance systems, explores governancestrategies for digital education products from the threedomains of “regulators-developers-users”.
基金Supported by Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences,No.CI2023C015YLNational Natural Science Foundation of China,No.82174352.
文摘BACKGROUND The lack of specific symptoms of gastric cancer(GC)causes great challenges in its early diagnosis.Thus it is essential to identify the risk factors for early diagnosis and treatment of GC and to improve the survival rates.AIM To assist physicians in identifying changes in the output of publications and research hotspots related to risk factors for GC,constructing a list of key risk factors,and providing a reference for early identification of patients at high risk for GC.METHODS Research articles on risk factors for GC were searched in the Web of Science core collection,and relevant information was extracted after screening.The literature was analyzed using Microsoft Excel 2019,CiteSpace V,and VOSviewer 1.6.18.RESULTS A total of 2514 papers from 72 countries and 2507 research institutions were retrieved.China(n=1061),National Cancer Center(n=138),and Shoichiro Tsugane(n=36)were the most productive country,institution,or author,respectively.The research hotspots in the study of risk factors for GC are summarized in four areas,namely:Helicobacter pylori(H.pylori)infection,single nucleotide polymorphism,bio-diagnostic markers,and GC risk prediction models.CONCLUSION In this study,we found that H.pylori infection is the most significant risk factor for GC;single-nucleotide polymorphism(SNP)is the most dominant genetic factor for GC;bio-diagnostic markers are the most promising diagnostic modality for GC.GC risk prediction models are the latest current research hotspot.We conclude that the most important risk factors for the development of GC are H.pylori infection,SNP,smoking,diet,and alcohol.
