Objectives This study aimed to explore the lagged and cumulative effects of risk factors on disability in older adults using distributed lag non-linear models(DLNMs).Methods We utilized data from the China Health and ...Objectives This study aimed to explore the lagged and cumulative effects of risk factors on disability in older adults using distributed lag non-linear models(DLNMs).Methods We utilized data from the China Health and Retirement Longitudinal Study(CHARLS).After feature selection via Elastic Net Regularization,we applied DLNMs to evaluate the lagged effects of risk factors.Disability was defined as the presence of any difficulties in basic activities of daily living(BADL).The cumulative relative risk(CRR)was calculated by summing the lag-specific risk estimates,representing the cumulative disability risk over the specified lag period.Effect modifications and sensitivity analyses were also performed.Results This study included a total of 2,318 participants.Early-phase lag factors,such as the difficulty in stooping(CRR=3.58;95%CI:2.31-5.55;P<0.001)and walking(CRR=2.77;95%CI:1.39-5.55;P<0.001),exerted the strongest effects immediately upon occurrence.Mid-phase lag factors,such as arthritis(CRR=1.51;95%CI:1.10-2.06;P=0.001),showed a resurgence in disability risk within 2-3 years.Late-phase lag factors,including depressive symptoms(CRR=2.38;95%CI:1.30-4.35;P<0.001)and elevated systolic blood pressure(CRR=1.64;95%CI:1.06-2.79;P=0.02),exhibited significant long-term cumulative risks.Conversely,grip strength(CRR=0.80;95%CI:0.54-0.95;P=0.02)and social participation(CRR=0.89;95%CI:0.73-0.99;P=0.04)were significant protective factors.Conclusions The findings underscore the importance of tailored interventions that account for various lag characteristics of different factors to effectively mitigate disability risk.Future studies should explore the underlying biological and sociological mechanisms of these lagged effects,identify intervention strategies that target risk factors with different lagged patterns,and evaluate their effectiveness.展开更多
Spinal and bulbar muscular atrophy is a neurodegenerative disease caused by extended CAG trinucleotide repeats in the androgen receptor gene,which encodes a ligand-dependent transcription facto r.The mutant androgen r...Spinal and bulbar muscular atrophy is a neurodegenerative disease caused by extended CAG trinucleotide repeats in the androgen receptor gene,which encodes a ligand-dependent transcription facto r.The mutant androgen receptor protein,characterized by polyglutamine expansion,is prone to misfolding and forms aggregates in both the nucleus and cytoplasm in the brain in spinal and bulbar muscular atrophy patients.These aggregates alter protein-protein interactions and compromise transcriptional activity.In this study,we reported that in both cultured N2a cells and mouse brain,mutant androgen receptor with polyglutamine expansion causes reduced expression of mesencephalic astrocyte-de rived neurotrophic factor.Overexpressio n of mesencephalic astrocyte-derived neurotrophic factor amelio rated the neurotoxicity of mutant androgen receptor through the inhibition of mutant androgen receptor aggregation.Conversely.knocking down endogenous mesencephalic astrocyte-derived neurotrophic factor in the mouse brain exacerbated neuronal damage and mutant androgen receptor aggregation.Our findings suggest that inhibition of mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor is a potential mechanism underlying neurodegeneration in spinal and bulbar muscular atrophy.展开更多
As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
BACKGROUND Anxiety and depression are highly prevalent among patients with cervical cancer(CC).However,few studies have systematically analyzed the psychological effects of tumor stage,treatment methods,and related fa...BACKGROUND Anxiety and depression are highly prevalent among patients with cervical cancer(CC).However,few studies have systematically analyzed the psychological effects of tumor stage,treatment methods,and related factors on these patients,or developed predictive models for these outcomes.AIM To identify factors influencing anxiety and depression in patients with CC and construct predictive models.METHODS We retrospectively analyzed data from 119 patients with CC treated at the Gynecology Department of Suzhou Ninth People’s Hospital between January 2017 and May 2025.Clinical data,psychological hope levels at diagnosis,and Self-Rating Anxiety Scale and Self-Rating Depression Scale scores during treatment were collected.Influencing factors were identified,and predictive models were developed.The model performance was evaluated using receiver operating characteristic(ROC)curves and the Hosmer-Lemeshow goodness-of-fit test.RESULTS During treatment,64.71%of the patients experienced anxiety and 52.10%experienced depression.Significant differences in family income,tumor stage,treatment modality,and hope level were observed between patients with and without anxiety/depression(P<0.05).Multivariate analysis showed that a family monthly income<5000 yuan,stage III-IV tumor,comprehensive treatment,and low hope level were independent risk factors(P<0.05).The predictive formula for anxiety was as follows:Logit(P)=0.795×monthly income+0.594×tumor stage+1.095×treatment method+1.184×hope level−9.176;for depression:Logit(P)=0.432×monthly income+0.518×tumor stage+0.727×treatment method+1.095×hope level−8.541.The area under the ROC curves were 0.865 for anxiety and 0.837 for depression.Goodness-of-fit test confirmed no overfitting(P>0.05).CONCLUSION Family income,tumor stage,treatment method,and hope level are key determinants of anxiety and depression in patients with CC.Predictive models incorporating these factors can effectively assess risk of anxiety and depression during treatment.展开更多
BACKGROUND Sepsis is a severe complication in hospitalized patients with diabetic foot(DF),often associated with high morbidity and mortality.Despite its clinical significance,limited tools exist for early risk predic...BACKGROUND Sepsis is a severe complication in hospitalized patients with diabetic foot(DF),often associated with high morbidity and mortality.Despite its clinical significance,limited tools exist for early risk prediction.AIM To identify key risk factors and evaluate the predictive value of a nomogram model for sepsis in this population.METHODS This retrospective study included 216 patients with DF admitted from January 2022 to June 2024.Patients were classified into sepsis(n=31)and non-sepsis(n=185)groups.Baseline characteristics,clinical parameters,and laboratory data were analyzed.Independent risk factors were identified through multivariable logistic regression,and a nomogram model was developed and validated.The model's performance was assessed by its discrimination(AUC),calibration(Hosmer-Lemeshow test,calibration plots),and clinical utility[decision curve analysis(DCA)].RESULTS The multivariable analysis identified six independent predictors of sepsis:Diabetes duration,DF Texas grade,white blood cell count,glycated hemoglobin,Creactive protein,and albumin.A nomogram integrating these factors achieved excellent diagnostic performance,with an AUC of 0.908(95%CI:0.865-0.956)and robust internal validation(AUC:0.906).Calibration results showed strong agreement between predicted and observed probabilities(Hosmer-Lemeshow P=0.926).DCA demonstrated superior net benefit compared to extreme intervention scenarios,highlighting its clinical utility.CONCLUSION The nomogram prediction model,based on six key risk factors,demonstrates strong predictive value,calibration,and clinical utility for sepsis in patients with DF.This tool offers a practical approach for early risk stratification,enabling timely interventions and improved clinical management in this high-risk population.展开更多
BACKGROUND In cases of colorectal cancer(CRC)with obstruction,patients experience local tissue edema due to intestinal obstruction.This condition stimulates the accumulation of inflammatory factors,activates cancer ce...BACKGROUND In cases of colorectal cancer(CRC)with obstruction,patients experience local tissue edema due to intestinal obstruction.This condition stimulates the accumulation of inflammatory factors,activates cancer cells,and increases the risk of tumor recurrence.At present,analyses and evaluation tools for factors influencing early postoperative recurrence in patients with CRC and obstruction are limited.AIM To explore the influencing factors and construct a predictive model of the early postoperative recurrence of CRC with obstruction.METHODS Data from 181 patients with CRC and obstruction who underwent surgery in the Department of Gastrointestinal Surgery,Suzhou Ninth Hospital Affiliated to Soochow University,between January 2017 and May 2023 were retrospectively collected.Patients with CRC and obstruction were divided into a recurrence group and a non-recurrence group based on whether recurrence occurred during the 2-year follow-up after surgery.Datasets from the two groups were compared.Subsequently,multiple logistic regression was employed to analyze the influencing factors of the early postoperative recurrence of CRC with obstruction.The nomogram prediction model was drawn using R software,and its performance was evaluated by the goodness of fit test and receiver operating characteristic(ROC)curve analysis.The clinical benefit rate of the model was evaluated by decision curves.RESULTS Among the 181 patients with CRC and obstruction,52(28.73%)experienced tumor recurrence within 2 years after surgery.Significant differences were observed in preoperative carcinoembryonic antigen(CEA),preoperative systemic immuneinflammation index(SII),tumor,node,and metastasis(TNM)stage,differentiation degree,nerve infiltration,and Ki-67 expression between the recurrence and non-recurrence groups(P<0.05).Multivariate logistic regression analysis showed that high preoperative CEA(OR=2.094,P=0.008),high preoperative SII(OR=2.795,P<0.001),TNM stage III(OR=1.644,P=0.027),poor differentiation(OR=1.861,P=0.035),and high Ki-67 expression(OR=2.467,P=0.001)were all influencing factors for early postoperative recurrence of CRC with obstruction.The area under the ROC curve of the nomograph model constructed based on this was 0.890,the goodness of fit deviation test was conducted(χ^(2)=3.903,P=0.866),and the decision curve display model demonstrated practical value in clinical practice.CONCLUSION The early recurrence rate of CRC with obstruction is high.CEA,SII,TNM staging,differentiation degree,and Ki-67 expression are factors related to early postoperative recurrence.A nomogram prediction model incorporating these factors can effectively evaluate the risk of early postoperative recurrence in patients with CRC.展开更多
BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk...BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk factors for recurrence remain unknown.AIM To comprehensively explore risk factors for short-term recurrence of CPs after endoscopic surgery and develop a nomogram prediction model.METHODS Overall,362 patients who underwent endoscopic polypectomy between January 2022 and January 2024 at Nanjing Jiangbei Hospital were included.We screened basic demographic data,clinical and polyp characteristics,surgery-related information,and independent risk factors for CPs recurrence using univariate and multivariate logistic regression analyses.The multivariate analysis results were used to construct a nomogram prediction model,internally validated using Bootstrapping,with performance evaluated using area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS CP re-occurred in 166(45.86%)of the 362 patients within 1 year post-surgery.Multivariate logistic regression analysis showed that age(OR=1.04,P=0.002),alcohol consumption(OR=2.07,P=0.012),Helicobacter pylori infection(OR=2.34,P<0.001),polyp number>2(OR=1.98,P=0.005),sessile polyps(OR=2.10,P=0.006),and adenomatous pathological type(OR=3.02,P<0.001)were independent risk factors for post-surgery recurrence.The nomogram prediction model showed good discriminatory(AUC=0.73)and calibrating power,and decision curve analysis showed that the model had good clinical benefit at risk probabilities>20%.CONCLUSION We identified multiple independent risk factors for short-term recurrence after endoscopic surgery.The nomogram prediction model showed a certain degree of differentiation,calibration,and potential clinical applicability.展开更多
BACKGROUND Rabies is a zoonotic viral disease affecting the central nervous system,caused by the rabies virus,with a case-fatality rate of 100%once symptoms appear.AIM To analyze high-risk factors associated with ment...BACKGROUND Rabies is a zoonotic viral disease affecting the central nervous system,caused by the rabies virus,with a case-fatality rate of 100%once symptoms appear.AIM To analyze high-risk factors associated with mental disorders induced by rabies vaccination and to construct a risk prediction model to inform strategies for improving patients’mental health.METHODS Patients who received rabies vaccinations at the Department of Infusion Yiwu Central Hospital between August 2024 and July 2025 were included,totaling 384 cases.Data were collected from medical records and included demographic characteristics(age,gender,occupation),lifestyle habits,and details regarding vaccine type,dosage,and injection site.The incidence of psychiatric disorders following vaccination was assessed using standardized anxiety and depression rating scales.Patients were categorized into two groups based on the presence or absence of anxiety and depression symptoms:The psychiatric disorder group and the non-psychiatric disorder group.Differences between the two groups were compared,and high-risk factors were identified using multivariate logistic regression analysis.A predictive model was then developed based on these factors to evaluate its predictive performance.RESULTS Among the 384 patients who received rabies vaccinations,36 cases(9.38%)were diagnosed with anxiety,52 cases(13.54%)with depression,and 88 cases(22.92%)with either condition.Logistic regression analysis identified the following signi ficant risk factors for psychiatric disorders:Education level of primary school or below,exposure site at the head and neck,exposure classified as grade III,family status of divorced/widowed/unmarried/living alone,number of wounds greater than one,and low awareness of rabies prevention and control(P<0.05).The risk prediction model demonstrated good performance,with an area under the receiver operating characteristic curve of 0.859,a specificity of 74.42%,and a sensitivity of 93.02%.CONCLUSION In real-world settings,psychiatric disorders following rabies vaccination are relatively common and are associated with factors such as lower education level,higher exposure severity,vulnerable family status,and limited awareness of rabies prevention and control.The developed risk prediction model may aid in early identification of high-risk individuals and support timely clinical intervention.展开更多
BACKGROUND Type 2 diabetes mellitus(T2DM)is a prevalent metabolic disorder increasingly linked with hypertension,posing significant health risks.The need for a predictive model tailored for T2DM patients is evident,as...BACKGROUND Type 2 diabetes mellitus(T2DM)is a prevalent metabolic disorder increasingly linked with hypertension,posing significant health risks.The need for a predictive model tailored for T2DM patients is evident,as current tools may not fully capture the unique risks in this population.This study hypothesizes that a nomogram incorporating specific risk factors will improve hypertension risk prediction in T2DM patients.AIM To develop and validate a nomogram prediction model for hypertension in T2DM patients.METHODS A retrospective observational study was conducted using data from 26850 T2DM patients from the Anhui Provincial Primary Medical and Health Information Management System(2022 to 2024).The study included patients aged 18 and above with available data on key variables.Exclusion criteria were type 1 diabetes,gestational diabetes,insufficient data,secondary hypertension,and abnormal liver and kidney function.The Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression were used to construct the nomogram,which was validated on separate datasets.RESULTS The developed nomogram for T2DM patients incorporated age,low-density lipoprotein,body mass index,diabetes duration,and urine protein levels as key predictive factors.In the training dataset,the model demonstrated a high discriminative power with an area under the receiver operating characteristic curve(AUC)of 0.823,indicating strong predictive accuracy.The validation dataset confirmed these findings with an AUC of 0.812.The calibration curve analysis showed excellent agreement between predicted and observed outcomes,with absolute errors of 0.017 for the training set and 0.031 for the validation set.The Hosmer-Lemeshow test yielded non-significant results for both sets(χ^(2)=7.066,P=0.562 for training;χ^(2)=6.122,P=0.709 for validation),suggesting good model fit.CONCLUSION The nomogram effectively predicts hypertension risk in T2DM patients,offering a valuable tool for personalized risk assessment and guiding targeted interventions.This model provides a significant advancement in the management of T2DM and hypertension comorbidity.展开更多
In this paper,we study the ρ-meson electromagnetic form factors(EMFFs)within the framework of the light-front quark model.The physical form factors G_(C,M,Q)(Q^(2))of the ρ-meson,as well as the charged square radius...In this paper,we study the ρ-meson electromagnetic form factors(EMFFs)within the framework of the light-front quark model.The physical form factors G_(C,M,Q)(Q^(2))of the ρ-meson,as well as the charged square radius<r^(2)>,the magnetic moment μ,and the quadrupole moment Q,are calculated,which describe the behaviors of EMFFs at zero momentum transfer.Using the type-Ⅱ replacement,we find that the zero-mode does contribute zero to the matrix element S_(00)^(+).It is found that the“M→M_(0)”replacement improves the angular condition remarkably,which permits different prescriptions of ρ-meson EMFFs to give the consistent results.The residual tiny violation of angular condition needs other explanations beyond the zero-mode contributions.Our results indicate that the relativistic effects or interaction internal structure are weaken in the zero-binding limit.This work is also applicable to other spin-1 particles.展开更多
BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited rese...BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited research on contributing factors in other types of surgeries.AIM To identify the risk factors for early postoperative abnormal liver function in multiple surgery types and construct a risk prediction model.METHODS This retrospective cohort study involved 3720 surgical patients from 5 surgical departments at Guangdong Provincial Hospital of Traditional Chinese Medicine.Patients were divided into abnormal(n=108)and normal(n=3612)groups based on liver function post-surgery.Univariate analysis and LASSO regression screened variables,followed by logistic regression to identify risk factors.A prediction model was constructed based on the variables selected via logistic re-gression.The goodness-of-fit of the model was evaluated using the Hosm-er–Lemeshow test,while discriminatory ability was measured by the area under the receiver operating characteristic curve.Calibration curves were plotted to visualize the consistency between predicted probabilities and observed outcomes.RESULTS The key factors contributing to abnormal liver function after surgery include elevated aspartate aminotransferase and alanine aminotransferase levels and reduced platelet counts pre-surgery,as well as the sevoflurane use during the procedure,among others.CONCLUSION The above factors collectively represent notable risk factors for postoperative liver function injury,and the prediction model developed based on these factors demonstrates strong predictive efficacy.展开更多
In the context of globalization and digitalization,cultural and artistic management and educational model innovation have become the core driving force for the sustainable development of the industry.This article syst...In the context of globalization and digitalization,cultural and artistic management and educational model innovation have become the core driving force for the sustainable development of the industry.This article systematically sorts out the six key success factors of strategic planning,content innovation,organizational change,user orientation,and dynamic evaluation through case analysis and theoretical discussion.These factors work together to provide a clear path and impetus for the sustainable development of the cultural and arts industry.展开更多
PM_(1.0),particulate matter with an aerodynamic diameter smaller than 1.0μm,can adversely affect human health.However,fewer stations are capable of measuring PM_(1.0) concentrations than PM2.5 and PM10 concentrations...PM_(1.0),particulate matter with an aerodynamic diameter smaller than 1.0μm,can adversely affect human health.However,fewer stations are capable of measuring PM_(1.0) concentrations than PM2.5 and PM10 concentrations in real time(i.e.,only 9 locations for PM_(1.0) vs.623 locations for PM2.5 or PM10)in South Korea,making it impossible to conduct a nationwide health risk analysis of PM_(1.0).Thus,this study aimed to develop a PM_(1.0) prediction model using a random forest algorithm based on PM_(1.0) data from the nine measurement stations and various environmental input factors.Cross validation,in which the model was trained in eight stations and tested in the remaining station,achieved an average R^(2) of 0.913.The high R^(2) value achieved undermutually exclusive training and test locations in the cross validation can be ascribed to the fact that all the locations had similar relationships between PM_(1.0) and the input factors,which were captured by our model.Moreover,results of feature importance analysis showed that PM2.5 and PM10 concentrations were the two most important input features in predicting PM_(1.0) concentration.Finally,the model was used to estimate the PM_(1.0) concentrations in 623 locations,where input factors such as PM2.5 and PM10 can be obtained.Based on the augmented profile,we identified Seoul and Ansan to be PM_(1.0) concentration hotspots.These regions are large cities or the center of anthropogenic and industrial activities.The proposed model and the augmented PM_(1.0) profiles can be used for large epidemiological studies to understand the health impacts of PM_(1.0).展开更多
Green innovation is an important driving force for high-quality development and an important guarantee for the revitalization of the old industrial base in Northeast China.However,research on green innovation is still...Green innovation is an important driving force for high-quality development and an important guarantee for the revitalization of the old industrial base in Northeast China.However,research on green innovation is still insufficient.Using the super-efficiency epsilon-based measure Malmquist model,kernel density estimation,and spatial econometric model,this study investigated the spatiotemporal evolution characteristics and influencing factors of green innovation efficiency(GIE)in Northeast China from 2005 to 2020.The results reveal that:1)The GIE in Northeast China has obvious phased characteristics,where 2005-2011 was a period of fluctuating decline while 2012-2020 was a period of fluctuating increase,reflecting the severe resource and environmental constraints faced by the green innovation process.2)The GIE in the Northeast China has a significant spatial dependence,which has not formed a relatively stable spatial club feature.The process for improving the GIE in the Northeast China in the future is still arduous and far off.3)The interweaving and mutual influence of nonequilibrium factors have led to the diversity and complexity of the spatiotemporal pattern evolution of GIE.Overall,the level of economic development and industrial structure has a positive effect,while foreign investment and industrial agglomeration have a negative effect.The direct effects of government regulation,resource endowment,science and technology,environmental regulation,and urbanization are not significant.The research conclusion of this article can provide important reference for the revitalization of Northeast China.展开更多
BACKGROUND Acute myocardial infarction(AMI)combined with ventricular septal perforation(VSR)is still a highly fatal condition in the era of reperfusion therapy.The incidence rate has decreased to 0.2%-0.4%due to the p...BACKGROUND Acute myocardial infarction(AMI)combined with ventricular septal perforation(VSR)is still a highly fatal condition in the era of reperfusion therapy.The incidence rate has decreased to 0.2%-0.4%due to the popularization of percutaneous coronary intervention.However,the risk is significantly increased for those who fail to undergo revascularization in time,and the mortality rate remains high.The current core contradiction in clinical practice lies in the selection of surgical timing,and the disparity in medical resources significantly affects prognosis.There is an urgent need to optimize the identification of high-risk populations and individualized treatment strategies.AIM To investigate the clinical features,determine the prognostic factors,and develop a predictive model for 30-day mortality in patients with acute myocardial infarction complicated by ventricular septal rupture(AMI-VSR)residing in high-altitude regions.METHODS This study retrospectively analyzed 48 AMI-VSR patients admitted to a Yunnan hospital from 2017 to 2024,with the establishment of survival(n=30)and mortality(n=18)groups based on patients’survival status.Risk factors were identified by univariate and multivariate logistic regression analyses.A nomogram model was developed using R software and validated via receiver operating characteristic(ROC)analysis and calibration curves.RESULTS Age,uric acid(UA),interleukin-6(IL-6),and low hemoglobin(Hb)were independent risk factors for 30-day mortality(odds ratios:1.147,1.006,1.034,and 0.941,respectively;P<0.05).The nomogram demonstrated excellent discrimination(area under the ROC curve=0.939)and calibration(Hosmer-Lemeshowχ²=2.268,P=0.971).In addition,patients’poor outcomes could be synergistically predicted by IL-6 and UA,advanced age,and reduced Hb.CONCLUSION This study highlights age,UA,IL-6,and Hb as critical predictors of mortality in AMI-VSR patients at high altitudes.The validated nomogram provides a practical tool for early risk stratification and tailored interventions,addressing gaps in managing this high-risk population in resource-limited settings.展开更多
Dentistry is a profession with a high prevalence of work-related musculoskeletal disorders(WMSDs),with symptoms often appearing very early in one’s career[1].WMSDs are conditions affecting the muscles,bones,and nervo...Dentistry is a profession with a high prevalence of work-related musculoskeletal disorders(WMSDs),with symptoms often appearing very early in one’s career[1].WMSDs are conditions affecting the muscles,bones,and nervous system due to occupational factors.In 2002,the International Labor Organization included musculoskeletal diseases in the International List of Occupational Diseases.China’s recently updated Classification and Catalog of Occupational Diseases has introduced two new categories of occupational illnesses,including occupational musculoskeletal disorders.WMSDs significantly impact the health and work of dentists,reducing their quality of life and causing economic losses.These disorders are multifactorial in nature,influenced by personal,psychosocial,biomechanical,and environmental factors.Dentists frequently maintain static or awkward postures during procedures,which leads to musculoskeletal strain and discomfort;additionally,long working hours contribute to psychological stress,further increasing the risk of WMSDs[2].展开更多
Video action recognition(VAR)aims to analyze dynamic behaviors in videos and achieve semantic understanding.VAR faces challenges such as temporal dynamics,action-scene coupling,and the complexity of human interactions...Video action recognition(VAR)aims to analyze dynamic behaviors in videos and achieve semantic understanding.VAR faces challenges such as temporal dynamics,action-scene coupling,and the complexity of human interactions.Existing methods can be categorized into motion-level,event-level,and story-level ones based on spatiotemporal granularity.However,single-modal approaches struggle to capture complex behavioral semantics and human factors.Therefore,in recent years,vision-language models(VLMs)have been introduced into this field,providing new research perspectives for VAR.In this paper,we systematically review spatiotemporal hierarchical methods in VAR and explore how the introduction of large models has advanced the field.Additionally,we propose the concept of“Factor”to identify and integrate key information from both visual and textual modalities,enhancing multimodal alignment.We also summarize various multimodal alignment methods and provide in-depth analysis and insights into future research directions.展开更多
Rank determination issue is one of the most significant issues in non-negative matrix factorization (NMF) research. However, rank determination problem has not received so much emphasis as sparseness regularization pr...Rank determination issue is one of the most significant issues in non-negative matrix factorization (NMF) research. However, rank determination problem has not received so much emphasis as sparseness regularization problem. Usually, the rank of base matrix needs to be assumed. In this paper, we propose an unsupervised multi-level non-negative matrix factorization model to extract the hidden data structure and seek the rank of base matrix. From machine learning point of view, the learning result depends on its prior knowledge. In our unsupervised multi-level model, we construct a three-level data structure for non-negative matrix factorization algorithm. Such a construction could apply more prior knowledge to the algorithm and obtain a better approximation of real data structure. The final bases selection is achieved through L2-norm optimization. We implement our experiment via binary datasets. The results demonstrate that our approach is able to retrieve the hidden structure of data, thus determine the correct rank of base matrix.展开更多
The construction of intercontinental power grid interconnection projects is key to realizing the vision of Global Energy Interconnection,which is to solve global energy problems in a clean and sustainable manner.These...The construction of intercontinental power grid interconnection projects is key to realizing the vision of Global Energy Interconnection,which is to solve global energy problems in a clean and sustainable manner.These projects may be influenced by a few factors that are neither technological nor economic,such as political,social,and international factors.This paper thus presents a multi-level model for recognizing which factor from a compiled list of 14 would impact a particular intercontinental interconnection project and for assessing the degree of the factor’s influence.In the first part of the model,the Analytic Hierarchy Process(AHP)method is used to recognize the project’s most significant impact factors.Using the recognition results,the second part of the model can assess the degree of the factor’s influence on the project based on ratings provided by experts.A comprehensive evaluation can thus be provided.As a case study,the proposed Saudi Arabia-Ethiopia power grid interconnection project connecting Asia and Africa was analyzed.Derived from a combination of multiple opinions from experts,evaluations from the model will be of direct benefit to decision-makers,investors,project implementers,and engineers,providing them with a deeper insight into the project.展开更多
AIM:To assess risk factors for epiretinal membranes(ERM)and examine their interactions in a nationally representative U.S.dataset.METHODS:Data from the 2005–2008 National Health and Nutrition Examination Survey(NHANE...AIM:To assess risk factors for epiretinal membranes(ERM)and examine their interactions in a nationally representative U.S.dataset.METHODS:Data from the 2005–2008 National Health and Nutrition Examination Survey(NHANES)were analyzed,a nationally representative U.S.dataset.ERM was identified via retinal imaging based on the presence of cellophane changes.Key predictors included age group,eye surgery history,and refractive error,with additional demographic and health-related covariates.Weighted univariate and multiple logistic regression models were used to assess associations and interaction effects between eye surgery and refractive error.RESULTS:Totally 3925 participants were analyzed.Older age,eye surgery,and refractive errors were significantly associated with ERM.Compared to those under 65y,the odds ratio(OR)for ERM was 3.08 for ages 65–75y(P=0.0014)and 4.76 for ages 75+years(P=0.0069).Eye surgery increased ERM risk(OR=3.48,P=0.0018).Moderate to high hyperopia and myopia were also associated with ERM(OR=2.65 and 1.80,respectively).A significant interaction between refractive error and eye surgery was observed(P<0.0001).Moderate to high myopia was associated with ERM only in those without eye surgery(OR=1.92,P=0.0443).Eye surgery was most strongly associated with ERM in the emmetropic group(OR=3.60,P=0.0027),followed by the moderate to high myopia group(OR=3.01,P=0.0031).CONCLUSION:ERM is significantly associated with aging,eye surgery,and refractive errors.The interaction between eye surgery and refractive error modifies ERM risk and highlights the importance of considering combined effects in clinical risk assessments.These findings may help guide individualized ERM risk assessment that may inform personalized approaches to ERM prevention and management.展开更多
基金supported by ScientificResearch Fund of National Health Commission of the People’s Republic of China-Major Science and Technology Program for Medicine and Health in Zhejiang Province(WKJ-ZJ-2406).
文摘Objectives This study aimed to explore the lagged and cumulative effects of risk factors on disability in older adults using distributed lag non-linear models(DLNMs).Methods We utilized data from the China Health and Retirement Longitudinal Study(CHARLS).After feature selection via Elastic Net Regularization,we applied DLNMs to evaluate the lagged effects of risk factors.Disability was defined as the presence of any difficulties in basic activities of daily living(BADL).The cumulative relative risk(CRR)was calculated by summing the lag-specific risk estimates,representing the cumulative disability risk over the specified lag period.Effect modifications and sensitivity analyses were also performed.Results This study included a total of 2,318 participants.Early-phase lag factors,such as the difficulty in stooping(CRR=3.58;95%CI:2.31-5.55;P<0.001)and walking(CRR=2.77;95%CI:1.39-5.55;P<0.001),exerted the strongest effects immediately upon occurrence.Mid-phase lag factors,such as arthritis(CRR=1.51;95%CI:1.10-2.06;P=0.001),showed a resurgence in disability risk within 2-3 years.Late-phase lag factors,including depressive symptoms(CRR=2.38;95%CI:1.30-4.35;P<0.001)and elevated systolic blood pressure(CRR=1.64;95%CI:1.06-2.79;P=0.02),exhibited significant long-term cumulative risks.Conversely,grip strength(CRR=0.80;95%CI:0.54-0.95;P=0.02)and social participation(CRR=0.89;95%CI:0.73-0.99;P=0.04)were significant protective factors.Conclusions The findings underscore the importance of tailored interventions that account for various lag characteristics of different factors to effectively mitigate disability risk.Future studies should explore the underlying biological and sociological mechanisms of these lagged effects,identify intervention strategies that target risk factors with different lagged patterns,and evaluate their effectiveness.
基金supported by the National Key R&D Program of China,No.2021YFA0805200(to SY)the National Natural Science Foundation of China,No.31970954(to SY)two grants from the Department of Science and Technology of Guangdong Province,Nos.2021ZT09Y007,2020B121201006(both to XJL)。
文摘Spinal and bulbar muscular atrophy is a neurodegenerative disease caused by extended CAG trinucleotide repeats in the androgen receptor gene,which encodes a ligand-dependent transcription facto r.The mutant androgen receptor protein,characterized by polyglutamine expansion,is prone to misfolding and forms aggregates in both the nucleus and cytoplasm in the brain in spinal and bulbar muscular atrophy patients.These aggregates alter protein-protein interactions and compromise transcriptional activity.In this study,we reported that in both cultured N2a cells and mouse brain,mutant androgen receptor with polyglutamine expansion causes reduced expression of mesencephalic astrocyte-de rived neurotrophic factor.Overexpressio n of mesencephalic astrocyte-derived neurotrophic factor amelio rated the neurotoxicity of mutant androgen receptor through the inhibition of mutant androgen receptor aggregation.Conversely.knocking down endogenous mesencephalic astrocyte-derived neurotrophic factor in the mouse brain exacerbated neuronal damage and mutant androgen receptor aggregation.Our findings suggest that inhibition of mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor is a potential mechanism underlying neurodegeneration in spinal and bulbar muscular atrophy.
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
基金Supported by 2024 Hospital-Level Research Start-up Fund,No.YK202426Suzhou Wujiang District"Science and Education for Health"Project,No.WWK202201Development Fund Project of the Affiliated Hospital of Xuzhou Medical University,No.XYFY202423.
文摘BACKGROUND Anxiety and depression are highly prevalent among patients with cervical cancer(CC).However,few studies have systematically analyzed the psychological effects of tumor stage,treatment methods,and related factors on these patients,or developed predictive models for these outcomes.AIM To identify factors influencing anxiety and depression in patients with CC and construct predictive models.METHODS We retrospectively analyzed data from 119 patients with CC treated at the Gynecology Department of Suzhou Ninth People’s Hospital between January 2017 and May 2025.Clinical data,psychological hope levels at diagnosis,and Self-Rating Anxiety Scale and Self-Rating Depression Scale scores during treatment were collected.Influencing factors were identified,and predictive models were developed.The model performance was evaluated using receiver operating characteristic(ROC)curves and the Hosmer-Lemeshow goodness-of-fit test.RESULTS During treatment,64.71%of the patients experienced anxiety and 52.10%experienced depression.Significant differences in family income,tumor stage,treatment modality,and hope level were observed between patients with and without anxiety/depression(P<0.05).Multivariate analysis showed that a family monthly income<5000 yuan,stage III-IV tumor,comprehensive treatment,and low hope level were independent risk factors(P<0.05).The predictive formula for anxiety was as follows:Logit(P)=0.795×monthly income+0.594×tumor stage+1.095×treatment method+1.184×hope level−9.176;for depression:Logit(P)=0.432×monthly income+0.518×tumor stage+0.727×treatment method+1.095×hope level−8.541.The area under the ROC curves were 0.865 for anxiety and 0.837 for depression.Goodness-of-fit test confirmed no overfitting(P>0.05).CONCLUSION Family income,tumor stage,treatment method,and hope level are key determinants of anxiety and depression in patients with CC.Predictive models incorporating these factors can effectively assess risk of anxiety and depression during treatment.
文摘BACKGROUND Sepsis is a severe complication in hospitalized patients with diabetic foot(DF),often associated with high morbidity and mortality.Despite its clinical significance,limited tools exist for early risk prediction.AIM To identify key risk factors and evaluate the predictive value of a nomogram model for sepsis in this population.METHODS This retrospective study included 216 patients with DF admitted from January 2022 to June 2024.Patients were classified into sepsis(n=31)and non-sepsis(n=185)groups.Baseline characteristics,clinical parameters,and laboratory data were analyzed.Independent risk factors were identified through multivariable logistic regression,and a nomogram model was developed and validated.The model's performance was assessed by its discrimination(AUC),calibration(Hosmer-Lemeshow test,calibration plots),and clinical utility[decision curve analysis(DCA)].RESULTS The multivariable analysis identified six independent predictors of sepsis:Diabetes duration,DF Texas grade,white blood cell count,glycated hemoglobin,Creactive protein,and albumin.A nomogram integrating these factors achieved excellent diagnostic performance,with an AUC of 0.908(95%CI:0.865-0.956)and robust internal validation(AUC:0.906).Calibration results showed strong agreement between predicted and observed probabilities(Hosmer-Lemeshow P=0.926).DCA demonstrated superior net benefit compared to extreme intervention scenarios,highlighting its clinical utility.CONCLUSION The nomogram prediction model,based on six key risk factors,demonstrates strong predictive value,calibration,and clinical utility for sepsis in patients with DF.This tool offers a practical approach for early risk stratification,enabling timely interventions and improved clinical management in this high-risk population.
文摘BACKGROUND In cases of colorectal cancer(CRC)with obstruction,patients experience local tissue edema due to intestinal obstruction.This condition stimulates the accumulation of inflammatory factors,activates cancer cells,and increases the risk of tumor recurrence.At present,analyses and evaluation tools for factors influencing early postoperative recurrence in patients with CRC and obstruction are limited.AIM To explore the influencing factors and construct a predictive model of the early postoperative recurrence of CRC with obstruction.METHODS Data from 181 patients with CRC and obstruction who underwent surgery in the Department of Gastrointestinal Surgery,Suzhou Ninth Hospital Affiliated to Soochow University,between January 2017 and May 2023 were retrospectively collected.Patients with CRC and obstruction were divided into a recurrence group and a non-recurrence group based on whether recurrence occurred during the 2-year follow-up after surgery.Datasets from the two groups were compared.Subsequently,multiple logistic regression was employed to analyze the influencing factors of the early postoperative recurrence of CRC with obstruction.The nomogram prediction model was drawn using R software,and its performance was evaluated by the goodness of fit test and receiver operating characteristic(ROC)curve analysis.The clinical benefit rate of the model was evaluated by decision curves.RESULTS Among the 181 patients with CRC and obstruction,52(28.73%)experienced tumor recurrence within 2 years after surgery.Significant differences were observed in preoperative carcinoembryonic antigen(CEA),preoperative systemic immuneinflammation index(SII),tumor,node,and metastasis(TNM)stage,differentiation degree,nerve infiltration,and Ki-67 expression between the recurrence and non-recurrence groups(P<0.05).Multivariate logistic regression analysis showed that high preoperative CEA(OR=2.094,P=0.008),high preoperative SII(OR=2.795,P<0.001),TNM stage III(OR=1.644,P=0.027),poor differentiation(OR=1.861,P=0.035),and high Ki-67 expression(OR=2.467,P=0.001)were all influencing factors for early postoperative recurrence of CRC with obstruction.The area under the ROC curve of the nomograph model constructed based on this was 0.890,the goodness of fit deviation test was conducted(χ^(2)=3.903,P=0.866),and the decision curve display model demonstrated practical value in clinical practice.CONCLUSION The early recurrence rate of CRC with obstruction is high.CEA,SII,TNM staging,differentiation degree,and Ki-67 expression are factors related to early postoperative recurrence.A nomogram prediction model incorporating these factors can effectively evaluate the risk of early postoperative recurrence in patients with CRC.
文摘BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk factors for recurrence remain unknown.AIM To comprehensively explore risk factors for short-term recurrence of CPs after endoscopic surgery and develop a nomogram prediction model.METHODS Overall,362 patients who underwent endoscopic polypectomy between January 2022 and January 2024 at Nanjing Jiangbei Hospital were included.We screened basic demographic data,clinical and polyp characteristics,surgery-related information,and independent risk factors for CPs recurrence using univariate and multivariate logistic regression analyses.The multivariate analysis results were used to construct a nomogram prediction model,internally validated using Bootstrapping,with performance evaluated using area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS CP re-occurred in 166(45.86%)of the 362 patients within 1 year post-surgery.Multivariate logistic regression analysis showed that age(OR=1.04,P=0.002),alcohol consumption(OR=2.07,P=0.012),Helicobacter pylori infection(OR=2.34,P<0.001),polyp number>2(OR=1.98,P=0.005),sessile polyps(OR=2.10,P=0.006),and adenomatous pathological type(OR=3.02,P<0.001)were independent risk factors for post-surgery recurrence.The nomogram prediction model showed good discriminatory(AUC=0.73)and calibrating power,and decision curve analysis showed that the model had good clinical benefit at risk probabilities>20%.CONCLUSION We identified multiple independent risk factors for short-term recurrence after endoscopic surgery.The nomogram prediction model showed a certain degree of differentiation,calibration,and potential clinical applicability.
基金Supported by the 2024 Yiwu City Research Plan Project,No.24-3-102.
文摘BACKGROUND Rabies is a zoonotic viral disease affecting the central nervous system,caused by the rabies virus,with a case-fatality rate of 100%once symptoms appear.AIM To analyze high-risk factors associated with mental disorders induced by rabies vaccination and to construct a risk prediction model to inform strategies for improving patients’mental health.METHODS Patients who received rabies vaccinations at the Department of Infusion Yiwu Central Hospital between August 2024 and July 2025 were included,totaling 384 cases.Data were collected from medical records and included demographic characteristics(age,gender,occupation),lifestyle habits,and details regarding vaccine type,dosage,and injection site.The incidence of psychiatric disorders following vaccination was assessed using standardized anxiety and depression rating scales.Patients were categorized into two groups based on the presence or absence of anxiety and depression symptoms:The psychiatric disorder group and the non-psychiatric disorder group.Differences between the two groups were compared,and high-risk factors were identified using multivariate logistic regression analysis.A predictive model was then developed based on these factors to evaluate its predictive performance.RESULTS Among the 384 patients who received rabies vaccinations,36 cases(9.38%)were diagnosed with anxiety,52 cases(13.54%)with depression,and 88 cases(22.92%)with either condition.Logistic regression analysis identified the following signi ficant risk factors for psychiatric disorders:Education level of primary school or below,exposure site at the head and neck,exposure classified as grade III,family status of divorced/widowed/unmarried/living alone,number of wounds greater than one,and low awareness of rabies prevention and control(P<0.05).The risk prediction model demonstrated good performance,with an area under the receiver operating characteristic curve of 0.859,a specificity of 74.42%,and a sensitivity of 93.02%.CONCLUSION In real-world settings,psychiatric disorders following rabies vaccination are relatively common and are associated with factors such as lower education level,higher exposure severity,vulnerable family status,and limited awareness of rabies prevention and control.The developed risk prediction model may aid in early identification of high-risk individuals and support timely clinical intervention.
文摘BACKGROUND Type 2 diabetes mellitus(T2DM)is a prevalent metabolic disorder increasingly linked with hypertension,posing significant health risks.The need for a predictive model tailored for T2DM patients is evident,as current tools may not fully capture the unique risks in this population.This study hypothesizes that a nomogram incorporating specific risk factors will improve hypertension risk prediction in T2DM patients.AIM To develop and validate a nomogram prediction model for hypertension in T2DM patients.METHODS A retrospective observational study was conducted using data from 26850 T2DM patients from the Anhui Provincial Primary Medical and Health Information Management System(2022 to 2024).The study included patients aged 18 and above with available data on key variables.Exclusion criteria were type 1 diabetes,gestational diabetes,insufficient data,secondary hypertension,and abnormal liver and kidney function.The Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression were used to construct the nomogram,which was validated on separate datasets.RESULTS The developed nomogram for T2DM patients incorporated age,low-density lipoprotein,body mass index,diabetes duration,and urine protein levels as key predictive factors.In the training dataset,the model demonstrated a high discriminative power with an area under the receiver operating characteristic curve(AUC)of 0.823,indicating strong predictive accuracy.The validation dataset confirmed these findings with an AUC of 0.812.The calibration curve analysis showed excellent agreement between predicted and observed outcomes,with absolute errors of 0.017 for the training set and 0.031 for the validation set.The Hosmer-Lemeshow test yielded non-significant results for both sets(χ^(2)=7.066,P=0.562 for training;χ^(2)=6.122,P=0.709 for validation),suggesting good model fit.CONCLUSION The nomogram effectively predicts hypertension risk in T2DM patients,offering a valuable tool for personalized risk assessment and guiding targeted interventions.This model provides a significant advancement in the management of T2DM and hypertension comorbidity.
基金supported by the National Natural Science Foundation of China(Grant Nos.11875122,12175025,and 12147102)Tongling University Talent Program(Grant No.R23100)。
文摘In this paper,we study the ρ-meson electromagnetic form factors(EMFFs)within the framework of the light-front quark model.The physical form factors G_(C,M,Q)(Q^(2))of the ρ-meson,as well as the charged square radius<r^(2)>,the magnetic moment μ,and the quadrupole moment Q,are calculated,which describe the behaviors of EMFFs at zero momentum transfer.Using the type-Ⅱ replacement,we find that the zero-mode does contribute zero to the matrix element S_(00)^(+).It is found that the“M→M_(0)”replacement improves the angular condition remarkably,which permits different prescriptions of ρ-meson EMFFs to give the consistent results.The residual tiny violation of angular condition needs other explanations beyond the zero-mode contributions.Our results indicate that the relativistic effects or interaction internal structure are weaken in the zero-binding limit.This work is also applicable to other spin-1 particles.
基金Supported by Guangdong Provincial Hospital of Chinese Medicine Science and Technology Research Special Project,No.YN2023WSSQ01State Key Laboratory of Traditional Chinese Medicine Syndrome.
文摘BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited research on contributing factors in other types of surgeries.AIM To identify the risk factors for early postoperative abnormal liver function in multiple surgery types and construct a risk prediction model.METHODS This retrospective cohort study involved 3720 surgical patients from 5 surgical departments at Guangdong Provincial Hospital of Traditional Chinese Medicine.Patients were divided into abnormal(n=108)and normal(n=3612)groups based on liver function post-surgery.Univariate analysis and LASSO regression screened variables,followed by logistic regression to identify risk factors.A prediction model was constructed based on the variables selected via logistic re-gression.The goodness-of-fit of the model was evaluated using the Hosm-er–Lemeshow test,while discriminatory ability was measured by the area under the receiver operating characteristic curve.Calibration curves were plotted to visualize the consistency between predicted probabilities and observed outcomes.RESULTS The key factors contributing to abnormal liver function after surgery include elevated aspartate aminotransferase and alanine aminotransferase levels and reduced platelet counts pre-surgery,as well as the sevoflurane use during the procedure,among others.CONCLUSION The above factors collectively represent notable risk factors for postoperative liver function injury,and the prediction model developed based on these factors demonstrates strong predictive efficacy.
文摘In the context of globalization and digitalization,cultural and artistic management and educational model innovation have become the core driving force for the sustainable development of the industry.This article systematically sorts out the six key success factors of strategic planning,content innovation,organizational change,user orientation,and dynamic evaluation through case analysis and theoretical discussion.These factors work together to provide a clear path and impetus for the sustainable development of the cultural and arts industry.
基金supported by the Fine Particle Research Initiative in East Asia Considering National Differences Project through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(No.NRF-2023M3G1A1090660)supported by a grant from the National Institute of Environmental Research(NIER),funded by the Ministry of Environment of the Republic of Korea(No.NIER-2023-04-02-056).
文摘PM_(1.0),particulate matter with an aerodynamic diameter smaller than 1.0μm,can adversely affect human health.However,fewer stations are capable of measuring PM_(1.0) concentrations than PM2.5 and PM10 concentrations in real time(i.e.,only 9 locations for PM_(1.0) vs.623 locations for PM2.5 or PM10)in South Korea,making it impossible to conduct a nationwide health risk analysis of PM_(1.0).Thus,this study aimed to develop a PM_(1.0) prediction model using a random forest algorithm based on PM_(1.0) data from the nine measurement stations and various environmental input factors.Cross validation,in which the model was trained in eight stations and tested in the remaining station,achieved an average R^(2) of 0.913.The high R^(2) value achieved undermutually exclusive training and test locations in the cross validation can be ascribed to the fact that all the locations had similar relationships between PM_(1.0) and the input factors,which were captured by our model.Moreover,results of feature importance analysis showed that PM2.5 and PM10 concentrations were the two most important input features in predicting PM_(1.0) concentration.Finally,the model was used to estimate the PM_(1.0) concentrations in 623 locations,where input factors such as PM2.5 and PM10 can be obtained.Based on the augmented profile,we identified Seoul and Ansan to be PM_(1.0) concentration hotspots.These regions are large cities or the center of anthropogenic and industrial activities.The proposed model and the augmented PM_(1.0) profiles can be used for large epidemiological studies to understand the health impacts of PM_(1.0).
基金Under the auspices of the National Natural Science Foundation of China(No.42571228,42401212)National Natural Science Foundation of Shandong(No.ZR2024MD022)。
文摘Green innovation is an important driving force for high-quality development and an important guarantee for the revitalization of the old industrial base in Northeast China.However,research on green innovation is still insufficient.Using the super-efficiency epsilon-based measure Malmquist model,kernel density estimation,and spatial econometric model,this study investigated the spatiotemporal evolution characteristics and influencing factors of green innovation efficiency(GIE)in Northeast China from 2005 to 2020.The results reveal that:1)The GIE in Northeast China has obvious phased characteristics,where 2005-2011 was a period of fluctuating decline while 2012-2020 was a period of fluctuating increase,reflecting the severe resource and environmental constraints faced by the green innovation process.2)The GIE in the Northeast China has a significant spatial dependence,which has not formed a relatively stable spatial club feature.The process for improving the GIE in the Northeast China in the future is still arduous and far off.3)The interweaving and mutual influence of nonequilibrium factors have led to the diversity and complexity of the spatiotemporal pattern evolution of GIE.Overall,the level of economic development and industrial structure has a positive effect,while foreign investment and industrial agglomeration have a negative effect.The direct effects of government regulation,resource endowment,science and technology,environmental regulation,and urbanization are not significant.The research conclusion of this article can provide important reference for the revitalization of Northeast China.
基金Supported by Science and Technology Department of Yunnan Province-Kunming Medical University,Kunming Medical Joint Special Project-Surface Project,No.202401AY070001-164Yunnan Provincial Department of Science and Technology Science and Technology Plan Project-Major Science and Technology Special Projects,No.202405AJ310003+1 种基金Yunnan Provincial Department of Science and Technology Science and Technology Plan Project-Key Research and Development Program,No.202103AC100004Yunnan Province Science and Technology Department Key Research and Development Plan,No.202103AC100002.
文摘BACKGROUND Acute myocardial infarction(AMI)combined with ventricular septal perforation(VSR)is still a highly fatal condition in the era of reperfusion therapy.The incidence rate has decreased to 0.2%-0.4%due to the popularization of percutaneous coronary intervention.However,the risk is significantly increased for those who fail to undergo revascularization in time,and the mortality rate remains high.The current core contradiction in clinical practice lies in the selection of surgical timing,and the disparity in medical resources significantly affects prognosis.There is an urgent need to optimize the identification of high-risk populations and individualized treatment strategies.AIM To investigate the clinical features,determine the prognostic factors,and develop a predictive model for 30-day mortality in patients with acute myocardial infarction complicated by ventricular septal rupture(AMI-VSR)residing in high-altitude regions.METHODS This study retrospectively analyzed 48 AMI-VSR patients admitted to a Yunnan hospital from 2017 to 2024,with the establishment of survival(n=30)and mortality(n=18)groups based on patients’survival status.Risk factors were identified by univariate and multivariate logistic regression analyses.A nomogram model was developed using R software and validated via receiver operating characteristic(ROC)analysis and calibration curves.RESULTS Age,uric acid(UA),interleukin-6(IL-6),and low hemoglobin(Hb)were independent risk factors for 30-day mortality(odds ratios:1.147,1.006,1.034,and 0.941,respectively;P<0.05).The nomogram demonstrated excellent discrimination(area under the ROC curve=0.939)and calibration(Hosmer-Lemeshowχ²=2.268,P=0.971).In addition,patients’poor outcomes could be synergistically predicted by IL-6 and UA,advanced age,and reduced Hb.CONCLUSION This study highlights age,UA,IL-6,and Hb as critical predictors of mortality in AMI-VSR patients at high altitudes.The validated nomogram provides a practical tool for early risk stratification and tailored interventions,addressing gaps in managing this high-risk population in resource-limited settings.
基金supported by the 2021 Shandong Province Higher Education Institutions“Youth Innovation Talent Introduction and Cultivation Plan”(Public Health Safety Risk Assessment and Response Innovation Team)National Traditional Chinese Medicine Comprehensive Reform Demonstration Zone Science and Technology Co construction Project(No.GZYKJSSD-2024-106)Research Project of Shandong Educational Supervision Society(No.SDJYDDXH2023-2159).
文摘Dentistry is a profession with a high prevalence of work-related musculoskeletal disorders(WMSDs),with symptoms often appearing very early in one’s career[1].WMSDs are conditions affecting the muscles,bones,and nervous system due to occupational factors.In 2002,the International Labor Organization included musculoskeletal diseases in the International List of Occupational Diseases.China’s recently updated Classification and Catalog of Occupational Diseases has introduced two new categories of occupational illnesses,including occupational musculoskeletal disorders.WMSDs significantly impact the health and work of dentists,reducing their quality of life and causing economic losses.These disorders are multifactorial in nature,influenced by personal,psychosocial,biomechanical,and environmental factors.Dentists frequently maintain static or awkward postures during procedures,which leads to musculoskeletal strain and discomfort;additionally,long working hours contribute to psychological stress,further increasing the risk of WMSDs[2].
基金supported by the Zhejiang Provincial Natural Science Foundation of China(No.LQ23F030001)the National Natural Science Foundation of China(No.62406280)+5 种基金the Autism Research Special Fund of Zhejiang Foundation for Disabled Persons(No.2023008)the Liaoning Province Higher Education Innovative Talents Program Support Project(No.LR2019058)the Liaoning Province Joint Open Fund for Key Scientific and Technological Innovation Bases(No.2021-KF-12-05)the Central Guidance on Local Science and Technology Development Fund of Liaoning Province(No.2023JH6/100100066)the Key Laboratory for Biomedical Engineering of Ministry of Education,Zhejiang University,Chinain part by the Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning.
文摘Video action recognition(VAR)aims to analyze dynamic behaviors in videos and achieve semantic understanding.VAR faces challenges such as temporal dynamics,action-scene coupling,and the complexity of human interactions.Existing methods can be categorized into motion-level,event-level,and story-level ones based on spatiotemporal granularity.However,single-modal approaches struggle to capture complex behavioral semantics and human factors.Therefore,in recent years,vision-language models(VLMs)have been introduced into this field,providing new research perspectives for VAR.In this paper,we systematically review spatiotemporal hierarchical methods in VAR and explore how the introduction of large models has advanced the field.Additionally,we propose the concept of“Factor”to identify and integrate key information from both visual and textual modalities,enhancing multimodal alignment.We also summarize various multimodal alignment methods and provide in-depth analysis and insights into future research directions.
文摘Rank determination issue is one of the most significant issues in non-negative matrix factorization (NMF) research. However, rank determination problem has not received so much emphasis as sparseness regularization problem. Usually, the rank of base matrix needs to be assumed. In this paper, we propose an unsupervised multi-level non-negative matrix factorization model to extract the hidden data structure and seek the rank of base matrix. From machine learning point of view, the learning result depends on its prior knowledge. In our unsupervised multi-level model, we construct a three-level data structure for non-negative matrix factorization algorithm. Such a construction could apply more prior knowledge to the algorithm and obtain a better approximation of real data structure. The final bases selection is achieved through L2-norm optimization. We implement our experiment via binary datasets. The results demonstrate that our approach is able to retrieve the hidden structure of data, thus determine the correct rank of base matrix.
基金supported by the State Grid Science and Technology Project“Research on Method and Evaluation Principle for the Cross-Continent Power Transmission Planning Scheme”(SGTYHT/16-JS-198)。
文摘The construction of intercontinental power grid interconnection projects is key to realizing the vision of Global Energy Interconnection,which is to solve global energy problems in a clean and sustainable manner.These projects may be influenced by a few factors that are neither technological nor economic,such as political,social,and international factors.This paper thus presents a multi-level model for recognizing which factor from a compiled list of 14 would impact a particular intercontinental interconnection project and for assessing the degree of the factor’s influence.In the first part of the model,the Analytic Hierarchy Process(AHP)method is used to recognize the project’s most significant impact factors.Using the recognition results,the second part of the model can assess the degree of the factor’s influence on the project based on ratings provided by experts.A comprehensive evaluation can thus be provided.As a case study,the proposed Saudi Arabia-Ethiopia power grid interconnection project connecting Asia and Africa was analyzed.Derived from a combination of multiple opinions from experts,evaluations from the model will be of direct benefit to decision-makers,investors,project implementers,and engineers,providing them with a deeper insight into the project.
基金Supported by Chengdu Municipal Science and Technology Bureau Key R&D Support Program(No.2023-YF09-00041-SN)。
文摘AIM:To assess risk factors for epiretinal membranes(ERM)and examine their interactions in a nationally representative U.S.dataset.METHODS:Data from the 2005–2008 National Health and Nutrition Examination Survey(NHANES)were analyzed,a nationally representative U.S.dataset.ERM was identified via retinal imaging based on the presence of cellophane changes.Key predictors included age group,eye surgery history,and refractive error,with additional demographic and health-related covariates.Weighted univariate and multiple logistic regression models were used to assess associations and interaction effects between eye surgery and refractive error.RESULTS:Totally 3925 participants were analyzed.Older age,eye surgery,and refractive errors were significantly associated with ERM.Compared to those under 65y,the odds ratio(OR)for ERM was 3.08 for ages 65–75y(P=0.0014)and 4.76 for ages 75+years(P=0.0069).Eye surgery increased ERM risk(OR=3.48,P=0.0018).Moderate to high hyperopia and myopia were also associated with ERM(OR=2.65 and 1.80,respectively).A significant interaction between refractive error and eye surgery was observed(P<0.0001).Moderate to high myopia was associated with ERM only in those without eye surgery(OR=1.92,P=0.0443).Eye surgery was most strongly associated with ERM in the emmetropic group(OR=3.60,P=0.0027),followed by the moderate to high myopia group(OR=3.01,P=0.0031).CONCLUSION:ERM is significantly associated with aging,eye surgery,and refractive errors.The interaction between eye surgery and refractive error modifies ERM risk and highlights the importance of considering combined effects in clinical risk assessments.These findings may help guide individualized ERM risk assessment that may inform personalized approaches to ERM prevention and management.
