Risk factors for congenital nasolacrimal duct obstruction in children under two years of age

·AIM:To identify various risk factors that may play a significant role in the development of congenital nasolacrimal duct obstruction(CNLDO).·METHODS:This observational case-control study included a case group of 122 children less than two years of age with CNLDO who underwent probing and irrigation treatment at the ophthalmology department of Imam Khomeini Hospital in Ahvaz,Iran,from June 2022 to June2024.A control group of 122 age-matched children without CNLDO was also included for comparison.Data was collected from the children's medical records.·RESULTS:The study found a significant correlation between the occurrence of CNLDO and several maternal factors,such as preeclampsia,the use of levothyroxine,hypothyroidism,having more than three pregnancies(gravidity>3),natural pregnancy,and gestational diabetes mellitus.Additionally,in children,factors,such as oxygen therapy,anemia,reflux,jaundice,and a family history of CNLDO in first-degree relatives were associated with CNLDO,and maternal preeclampsia and hypothyroidism were found to significantly increase the risk of developing CNLDO in children.·CONCLUSION:Given that CNLDO affects both premature and full-term children,the present findings may potentially facilitate the early identification of children and infants at risk of nasolacrimal duct obstruction,thereby preventing the onset of chronic dacryocystitis.

Association between autoimmune gastritis and gastric polyps:Clinical characteristics and risk factors

BACKGROUND The relationship between autoimmune gastritis(AIG)and gastric polyps(GPs)is not well understood.AIM To explore the clinical characteristics and risk factors of AIG with GPs in patients.METHODS This double center retrospective study included 530 patients diagnosed with AIG from July 2019 to July 2023.We collected clinical,biochemical,serological,and demographic data were of each patient.Logistic regression analyses,both multivariate and univariate,were conducted to pinpoint independent risk factors for GPs in patients with AIG patients.Receiver operating characteristic curves were utilized to establish the optimal cutoff values,sensitivity,and specificity of these risk factors for predicting GPs in patients with AIG.RESULTS Patients with GPs had a higher median age than those without GPs[61(52.25-69)years vs 58(47-66)years,P=0.006].The gastrin-17 levels were significantly elevated in patients with GPs compared with those without GPs[91.9(34.2-138.9)pmol/mL vs 60.9(12.6-98.4)pmol/mL,P<0.001].Additionally,the positive rate of parietal cell antibody(PCA)antibody was higher in these patients than in those without GPs(88.6%vs 73.6%,P<0.001).Multivariate and univariate analyses revealed that PCA positivity[odds ratio(OR)=2.003,P=0.017],pepsinogen II(OR=1.053,P=0.015),and enterochromaffin like cells hyperplasia(OR=3.116,P<0.001)were significant risk factors for GPs,while pepsinogen I was identified as a protective factor.CONCLUSION PCA positivity and enterochromaffin like cells hyperplasia are significant risk factor for the development of GPs in patients with AIG.Elevated gastrin-17 levels may also play a role in this process.These findings suggest potential targets for further research and therapeutic intervention in managing GPs in patients with AIG.

Social function scores and influencing factors in patients with residual depressive symptoms

BACKGROUND At present,the influencing factors of social function in patients with residual depressive symptoms are still unclear.Residual depressive symptoms are highly harmful,leading to low mood in patients,affecting work and interpersonal communication,increasing the risk of recurrence,and adding to the burden on families.Studying the influencing factors of their social function is of great significance.AIM To explore the social function score and its influencing factors in patients with residual depressive symptoms.METHODS This observational study surveyed patients with residual depressive symptoms(case group)and healthy patients undergoing physical examinations(control group).Participants were admitted between January 2022 and December 2023.Social functioning was assessed using the Sheehan Disability Scale(SDS),and scores were compared between groups.Factors influencing SDS scores in patients with residual depressive symptoms were analyzed by applying multiple linear regression while using the receiver operating characteristic curve,and these RESULTS The SDS scores of the 158 patients with depressive symptoms were 11.48±3.26.Compared with the control group,the SDS scores and all items in the case group were higher.SDS scores were higher in patients with relapse,discon-tinuous medication,drug therapy alone,severe somatic symptoms,obvious residual symptoms,and anxiety scores≥8.Disease history,medication compliance,therapy method,and residual symptoms correlated positively with SDS scores(r=0.354,0.414,0.602,and 0.456,respectively).Independent influencing factors included disease history,medication compliance,therapy method,somatic symptoms,residual symptoms,and anxiety scores(P<0.05).The areas under the curve for predicting social functional impairment using these factors were 0.713,0.559,0.684,0.729,0.668,and 0.628,respectively,with sensitivities of 79.2%,61.8%,76.8%,81.7%,63.6%,and 65.5%and specificities of 83.3%,87.5%,82.6%,83.3%,86.7%,and 92.1%,respectively.CONCLUSION The social function scores of patients with residual symptoms of depression are high.They are affected by disease history,medication compliance,therapy method,degree of somatic symptoms,residual symptoms,and anxiety.

Burden of mental disorders and risk factors in the Western Pacific region from 1990 to 2021

BACKGROUND The burden of mental disorders(MD)in the Western Pacific Region(WPR)re-mains a critical public health concern,with substantial variations across demogra-phics and countries.AIM To analyze the burden of MD in the WPR from 1990 to 2021,along with associated risk factors,to reveal changing trends and emerging challenges.METHODS We used data from the Global Burden of Disease 2021,analyzing prevalence,incidence,and disability-adjusted life years(DALYs)of MD from 1990 to 2021.Statistical methods included age-standardisation and uncertainty analysis to address variations in population structure and data completeness.RESULTS Between 1990 and 2021,the prevalence of MD rose from 174.40 million cases[95%uncertainty interval(UI):160.17-189.84]to 234.90 million cases(95%UI:219.04-252.50),with corresponding DALYs increasing from 22.8 million(95%UI:17.22-28.79)to 32.07 million(95%UI:24.50-40.68).During this period,the burden of MD shifted towards older age groups.Depressive and anxiety disorders were predominant,with females showing higher DALYs for depressive and anxiety disorders,and males more affected by conduct disorders,attention-deficit hyperactivity disorder,and autism spectrum disorders.Australia,New Zealand,and Malaysia reported the highest burdens,whereas Vietnam,China,and Brunei Darussalam reported the lowest.Additionally,childhood sexual abuse and bullying,and intimate partner violence emerged as significant risk factors.CONCLUSION This study highlights the significant burden of MD in the WPR,with variations by age,gender,and nation.The coronavirus disease 2019 pandemic has exacerbated the situation,emphasizing the need for a coordinated response.

Spatial Pattern and Influencing Factors of China’s Digital Trade Resilience under the Impact of Trade Frictions

In the era of the digital economy,digital trade has demonstrated strong vitality,becoming a crucial driving force for the highquality development of national and regional economies.However,understanding the resilience of digital trade in the face of external crises is an important topic.Taking the backdrop of Sino-US trade friction,this paper constructs a resilience index system for digital trade.It utilizes entropy method,kernel density estimation,and ArcGIS mapping to calculate and visually analyze the resilience of China’s digital trade from 2017 to 2021.Additionally,a Tobit model is constructed to explore the main influencing factors of digital trade resilience patterns.The research findings indicate:1)temporally,during the period of Sino-US trade friction,China’s digital trade resilience shows an overall upward trend,but there are regional differences in resilience levels across the country,with a severe polarization phenomenon.2)Spatially,high resilience is observed in the eastern and central regions of China,while the western and northeastern regions exhibit low resilience.3)From a dimensional perspective,the resistance of digital trade resilience displays a spatial distribution of high values in the east and low values in the west.The recovery force is aggregated along coastal areas,and the renewal force tends to aggregate along the eastern coastline.4)Factors such as economic scale,industrial structure,urbanization rate,government fiscal expenditure,and technological talents significantly promote the enhancement of digital trade resilience.This study reveals the dynamic characteristics and influencing factors of digital trade resilience in responding to external shocks,providing theoretical basis and policy suggestions for enhancing digital trade resilience,and promoting high-quality economic development in China.

Water Quality,Influential Factors,and Management Strategies from 2016 to 2020 in the Yangtze River Economic Belt,China

The Yangtze River economic belt(YREB),China is important to the Chinese economy and for supporting sustainable development.Clarifying the relationship between water quality indices and socioeconomic indicators could help improve aquatic environment management in the YREB and our understanding of the causes and effects of water quality variations in other large river basins.In this study,river water quality,factors affecting water quality,and management strategies,and correlations between water quality indices and socioeconomic indicators in the YREB during the 13th Five-Year Plan period(2016-2020)were assessed.The single-factor evaluation method,constant price for GDP,and correlation analyses were adopted.The results showed that:1)water quality in the YREB improved during the 13th Five-Year Plan period.The number of aquatic environment sections meeting GradeⅠ-Ⅲwater quality standards increased by 13.1%and the number below Grade V decreased by 2.9%.2)The values of 12 indicators in the YREB exceeded relevant standards.The indicators with highest concentreation were the total phosphorus,chemical oxygen demand,ammonia nitrogen,and permanganate index,which were relatively high in downstream regions in Anhui Province,Jiangsu Province,and Shanghai Municipality.3)Ammonia nitrogen,chemical oxygen demand,and total phosphorus emissions per unit area and water extraction per unit area are relatively high in the three downstream regions mentioned above.4)Increased domestic sewage discharges have increased total wastewater discharges in the YREB.5)River water quality in the YREB strongly correlated with population,economic,and water resource indices and less strongly correlated with government investment,agriculture,meteorology,energy,and forestry indices.This confirmed the need to decrease wastewater discharges and non-point-source pollutant emissions.The aquatic environment could be improved by taking reasonable measures to control population growth,adjusting the industrial structure to accelerate industrial transformation and increase the proportion of tertiary industries,and investing in technological innovations to protect the environment.

Spatiotemporal Variation and Influencing Factors of Atmospheric CO_(2) Concentration in China

Rapid increases in Carbon dioxide(CO_(2))levels could trigger unpredictable climate change.The assessment of spatiotempor-al variation and influencing factors of CO_(2) concentration are helpful in understanding the source/sink balance and supporting the formu-lation of climate policy.In this study,Greenhouse Gases Observing Satellite(GOSAT)data were used to explore the variability of CO_(2) concentrations in China from 2009 to 2020.Meteorological parameters,vegetation cover,and anthropogenic activities were combined to explain the increase in CO_(2) concentration,using pixel-based correlations and Covariance Based Structural Equation Modeling(CB-SEM)analysis.The results showed that the influence of vertical CO_(2) transport diminished with altitude,with a distinct inter-annual in-crease in CO_(2) concentrations at 17 vertical levels.Spatially,the highest values were observed in East China,whereas the lowest were observed in Northwest China.There were significant seasonal variations in CO_(2) concentration,with maximum and minimum values in spring(April)and summer(August),respectively.According to the pixel-based correlation analysis,the near-surface CO_(2) concentration was positively correlated with population(r=0.99,P<0.001),Leaf Area Index(LAI,r=0.95,P<0.001),emissions(r=0.91,P<0.001),temperature(r=0.60,P<0.05),precipitation(r=0.34,P>0.05),soil water(r=0.29,P>0.05),nightlight(r=0.28,P>0.05);and negatively correlated with wind speed(r=−0.58,P<0.05).CB-SEM analysis revealed that LAI was the most important con-trolling factor explaining CO_(2) concentration variation(total effect of 0.66),followed by emissions(0.58),temperature(0.45),precipita-tion(0.30),wind speed(−0.28),and soil water(−0.07).The model explained 93% of the increase in CO_(2) concentration.Our results provide crucial information on the patterns of CO_(2) concentrations and their driving mechanisms,which are particularly significant in the context of climate change.

Spatial Patterns and Controlling Factors of Soil Organic Carbon and Total Nitrogen in the Three River Headwaters Region,China

The alpine ecosystem has great potential for carbon sequestration.Soil organic carbon(SOC)and total nitrogen(TN)are highly sensitive to climate change,and their dynamics are crucial to revealing the effect of climate change on the structure,function,and services of the ecosystem.However,the spatial distribution and controlling factors of SOC and TN across various soil layers and vegetation types within this unique ecosystem remain inadequately understood.In this study,256 soil samples in 89 sites were collected from the Three River Headwaters Region(TRHR)in China to investigate SOC and TN and to explore the primary factors affecting their distribution,including soil,vegetation,climate,and geography factors.The results show that SOC and TN contents in 0-20,20-40,40-60,and 60-80 cm soil layers are 24.40,18.03,14.04,12.40 g/kg and 2.46,1.90,1.51,1.17 g/kg,respectively;with higher concentrations observed in the southeastern region compared to the northwest of the TRHR.One-way analysis of variance reveals that SOC and TN levels are elevated in the alpine meadow and the alpine shrub relative to the alpine steppe in the 0-60 cm soil layers.The structural equation model explores that soil water content is the main controlling factor affecting the variation of SOC and TN.Moreover,the geography,climate,and vegetation factors notably indirectly affect SOC and TN through soil factors.Therefore,it can effectively improve soil water and nutrient conditions through vegetation restoration,soil improvement,and grazing management,and the change of SOC and TN can be fully understood by establishing monitoring networks to better protect soil carbon and nitrogen.

Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning

BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.

Disinfection Byproducts and Their Precursors in Drinking Water Sources:Origins,Influencing Factors,and Environmental Insights

Tracing the contamination origins in water sources and identifying the impacts of natural and human processes are essential for ecological safety and public health.However,current analysis approaches are not ideal,as they tend to be laborious,time-consuming,or technically difficult.Disinfection byproducts(DBPs)are a family of well-known secondary pollutants formed by the reactions of chemical disinfectants with DBP precursors during water disinfection treatment.Since DBP precursors have various origins(e.g.,natural,domestic,industrial,and agricultural sources),and since the formation of DBPs from different precursors in the presence of specific disinfectants is distinctive,we argue that DBPs and DBP precursors can serve as alternative indicators to assess the contamination in water sources and identify pollution origins.After providing a retrospective of the origins of DBPs and DBP precursors,as well as the specific formation patterns of DBPs from different precursors,this article presents an overview of the impacts of various natural and anthropogenic factors on DBPs and DBP precursors in drinking water sources.In practice,the DBPs(i.e.,their concentration and speciation)originally present in source water and the DBP precursors determined using DBP formation potential tests—in which water samples are dosed with a stoichiometric excess of specific disinfectants in order to maximize DBP formation under certain reaction conditions—can be considered as alternative metrics.When jointly used with other water quality parameters(e.g.,dissolved organic carbon,dissolved organic nitrogen,fluorescence,and molecular weight distribution)and specific contaminants of emerging concern(e.g.,certain pharmaceuticals and personal care products),DBPs and DBP precursors in drinking water sources can provide a more comprehensive picture of water pollution for better managing water resources and ensuring human health.

Spatiotemporal deformation characteristics of Outang landslide and identification of triggering factors using data mining

Since the impoundment of Three Gorges Reservoir(TGR)in 2003,numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall.One case is the Outang landslide,a large-scale and active landslide,on the south bank of the Yangtze River.The latest monitoring data and site investigations available are analyzed to establish spatial and temporal landslide deformation characteristics.Data mining technology,including the two-step clustering and Apriori algorithm,is then used to identify the dominant triggers of landslide movement.In the data mining process,the two-step clustering method clusters the candidate triggers and displacement rate into several groups,and the Apriori algorithm generates correlation criteria for the cause-and-effect.The analysis considers multiple locations of the landslide and incorporates two types of time scales:longterm deformation on a monthly basis and short-term deformation on a daily basis.This analysis shows that the deformations of the Outang landslide are driven by both rainfall and reservoir water while its deformation varies spatiotemporally mainly due to the difference in local responses to hydrological factors.The data mining results reveal different dominant triggering factors depending on the monitoring frequency:the monthly and bi-monthly cumulative rainfall control the monthly deformation,and the 10-d cumulative rainfall and the 5-d cumulative drop of water level in the reservoir dominate the daily deformation of the landslide.It is concluded that the spatiotemporal deformation pattern and data mining rules associated with precipitation and reservoir water level have the potential to be broadly implemented for improving landslide prevention and control in the dam reservoirs and other landslideprone areas.

Spatial-temporal differentiation and influencing factors of rural settlements in mountainous areas: an example of Liangshan Yi Autonomous Prefecture, Southwestern China

Rural settlement is the basic spatial unit for compact communities in rural area. Scientific exploration of spatial-temporal differentiation and its influencing factors is the premise of spatial layout rationalization. Based on land use data of Liangshan Yi Autonomous Prefecture(hereinafter referred to as Liangshan Prefecture) in Sichuan Province, China from 1980 to 2020, compactness index, fractal dimension, imbalance index, location entropy and the optimal parameters-based geographical detector(OPGD) model are used to analyze the spatial-temporal evolution of the morphological characteristics of rural settlements, and to explore the influence of natural geographical factors, socioeconomic factors, and policy factors on the spatial differentiation of rural settlements. The results show that:(1) From 1980 to 2020, the rural settlements area in Liangshan Prefecture increased by 15.96 km^(2). In space, the rural settlements are generally distributed in a local aggregation, dense in the middle and sparse around the periphery. In 2015, the spatial density and expansion index of rural settlements reached the peak.(2) From 1980 to 2020, the compactness index decreased from 0.7636 to 0.7496, the fractal dimension increased from 1.0283 to 1.0314, and the fragmentation index decreased from 0.1183 to 0.1047. The spatial morphological structure of rural settlements tended to be loose, the shape contour tended to be complex, the degree of fragmentation decreased, and the spatial distribution was significantly imbalanced.(3) The results of OPGD detection in 2015 show that the influence of each factor is slope(0.2371) > traffic accessibility(0.2098) > population(0.1403) > regional GDP(0.1325) > elevation(0.0987) > poverty alleviation(0). The results of OPGD detection in 2020 show that the influence of each factor is slope(0.2339) > traffic accessibility(0.2198) > population(0.1432) > regional GDP(0.1219) > poverty alleviation(0.0992) > elevation(0.093). Natural geographical factors(slope and elevation) are the basic factors affecting the spatial distribution of rural settlements, and rural settlements are widely distributed in the river valley plain and the second half mountain area. Socioeconomic factors(traffic accessibility, population, and regional GDP) have a greater impact on the spatial distribution of rural settlements, which is an important factor affecting the spatial distribution of rural settlements. Policy factors such as poverty alleviation relocation have an indispensable impact on the spatial distribution of rural settlements. The research results can provide decisionmaking basis for the spatial arrangement of rural settlements in Liangshan Prefecture, and optimize the implementation of rural revitalization policies.

Biological factors driving colorectal cancer metastasis

Approximately 20%of colorectal cancer(CRC)patients present with metastasis at diagnosis.Among Stage I-III CRC patients who undergo surgical resection,18%typically suffer from distal metastasis within the first three years following initial treatment.The median survival duration after the diagnosis of metastatic CRC(mCRC)is only 9 mo.mCRC is traditionally considered to be an advanced stage malignancy or is thought to be caused by incomplete resection of tumor tissue,allowing cancer cells to spread from primary to distant organs;however,increa-sing evidence suggests that the mCRC process can begin early in tumor development.CRC patients present with high heterogeneity and diverse cancer phenotypes that are classified on the basis of molecular and morphological alterations.Different genomic and nongenomic events can induce subclone diversity,which leads to cancer and metastasis.Throughout the course of mCRC,metastatic cascades are associated with invasive cancer cell migration through the circulatory system,extravasation,distal seeding,dormancy,and reactivation,with each step requiring specific molecular functions.However,cancer cells presenting neoantigens can be recognized and eliminated by the immune system.In this review,we explain the biological factors that drive CRC metastasis,namely,genomic instability,epigenetic instability,the metastatic cascade,the cancer-immunity cycle,and external lifestyle factors.Despite remarkable progress in CRC research,the role of molecular classification in therapeutic intervention remains unclear.This review shows the driving factors of mCRC which may help in identifying potential candidate biomarkers that can improve the diagnosis and early detection of mCRC cases.

Risk Factors of Enternal Nutrition Intolerance in Septic Patients:A Case-control Study

Objective:This study aimed to investigate the incidence of enteral nutrition intolerance(ENI)in patients with sepsis and explore potential risk factors.Methods:A case-control study was conducted in patients with sepsis who were receiving enteral nutrition(EN)at a tertiary hospital in China.The included patients were divided into the ENI group and the non-ENI group.Univariate and multivariate analyses were performed to identify the risk factors for ENI.Results:A total of 859 patients were included in the study.Among them,288(33.53%)patients experienced symptoms of ENI,including diarrhea,vomiting,bloating,and gastric retention.Logistic regression analysis revealed that the Acute Physiology and Chronic Health Evaluation H(APACHE H)score,thoracocentesis,and usage of cardiotonic drugs(namely,inotropes)were independent predictors of the ENI.Conclusion:The incidence of ENI is relatively high in patients with sepsis,especially in those who have higher APACHE H scores,have undergone thoracocentesis,and have received inotropes.

Prevalence and risk factors of diabetes mellitus among elderly patients in the Lugu community

BACKGROUND Age is a significant risk factor of diabetes mellitus(DM).With the develop of population aging,the incidence of DM remains increasing.Understanding the epidemiology of DM among elderly individuals in a certain area contributes to the DM interventions for the local elderly individuals with high risk of DM.AIM To explore the prevalence of DM among elderly individuals in the Lugu community and analyze the related risk factors to provide a valid scientific basis for the health management of elderly individuals.METHODS A total of 4816 elderly people who came to the community for physical examination were retrospectively analyzed.The prevalence of DM among the elderly was calculated.The individuals were divided into a DM group and a non-DM group according to the diagnosis of DM to compare the differences in diastolic blood pressure(DBP)and systolic blood pressure(SBP),fasting blood glucose,body mass index(BMI),waist-to-hip ratio(WHR)and incidence of hypertension(HT),coronary heart disease(CHD),and chronic kidney disease(CKD).RESULTS DM was diagnosed in 32.70%of the 4816 elderly people.The BMI of the DM group(25.16±3.35)was greater than that of the non-DM group(24.61±3.78).The WHR was 0.90±0.04 in the non-DM group and 0.90±0.03 in the DM group,with no significant difference.The left SBP and SBP in the DM group were 137.9 mmHg±11.92 mmHg and 69.95 mmHg±7.75 mmHg,respectively,while they were 126.6 mmHg±12.44 mmHg and 71.15 mmHg±12.55 mmHg,respectively,in the non-DM group.These findings indicate higher SBP and lower DBP in DM patients than in those without DM.In the DM group,1274 patients were diagnosed with HT,accounting for 80.89%.Among the 3241 non-DM patients,1743(53.78%)were hypertensive and 1498(46.22%)were nonhypertensive.The DM group had more cases of HT than did the non-DM group.There were more patients with CHD or CKD in the DM group than in the non-DM group.There were more patients who drank alcohol more frequently(≥3 times)in the DM group than in the non-DM group.CONCLUSION Older adults in the Lugu community are at a greater risk of DM.In elderly individuals,DM is closely related to high BMI and HT,CHD,and CKD.Physical examinations should be actively carried out for elderly people to determine their BMI,SBP,DBP,and other signs,and sufficient attention should be given to abnormalities in the above signs before further diagnosis.

Analysis of the influencing factors and clinical related characteristics of pulmonary tuberculosis in patients with type 2 diabetes mellitus

BACKGROUND In China,the prevalence of type 2 diabetes mellitus(T2DM)among diabetic patients is estimated to be between 90%-95%.Additionally,China is among the 22 countries burdened by a high number of tuberculosis cases,with approximately 4.5 million individuals affected by active tuberculosis.Notably,T2DM poses a significant risk factor for the development of tuberculosis,as evidenced by the increased incidence of T2DM coexisting with pulmonary tuberculosis(T2DMPTB),which has risen from 19.3%to 24.1%.It is evident that these two diseases are intricately interconnected and mutually reinforcing in nature.AIM To elucidate the clinical features of individuals diagnosed with both T2DM and tuberculosis(T2DM-PTB),as well as to investigate the potential risk factors associated with active tuberculosis in patients with T2DM.METHODS T2DM-PTB patients who visited our hospital between January 2020 and January 2023 were selected as the observation group,Simple DM patients presenting to our hospital in the same period were the control group,Controls and case groups were matched 1:2 according to the principle of the same sex,age difference(±3)years and disease duration difference(±5)years,patients were investigated for general demographic characteristics,diabetes-related characteristics,body immune status,lifestyle and behavioral habits,univariate and multivariate analysis of the data using conditional logistic regression,calculate the odds ratio(OR)values and 95%CI of OR values.RESULTS A total of 315 study subjects were included in this study,including 105 subjects in the observation group and 210 subjects in the control group.Comparison of the results of both anthropometric and biochemical measures showed that the constitution index,systolic blood pressure,diastolic blood pressure and lymphocyte count were significantly lower in the case group,while fasting blood glucose and high-density lipoprotein cholesterol levels were significantly higher than those in the control group.The results of univariate analysis showed that poor glucose control,hypoproteinemia,lymphopenia,TB contact history,high infection,smoking and alcohol consumption were positively associated with PTB in T2DM patients;married,history of hypertension,treatment of oral hypoglycemic drugs plus insulin,overweight,obesity and regular exercise were negatively associated with PTB in T2DM patients.Results of multivariate stepwise regression analysis found lymphopenia(OR=17.75,95%CI:3.40-92.74),smoking(OR=12.25,95%CI:2.53-59.37),history of TB contact(OR=6.56,95%CI:1.23-35.03)and poor glycemic control(OR=3.37,95%CI:1.11-10.25)was associated with an increased risk of developing PTB in patients with T2DM,While being overweight(OR=0.23,95%CI:0.08-0.72)and obesity(OR=0.11,95%CI:0.02-0.72)was associated with a reduced risk of developing PTB in patients with T2DM.CONCLUSION T2DM-PTB patients are prone to worse glycemic control,higher infection frequency,and a higher proportion of people smoking,drinking alcohol,and lack of exercise.Lymphopenia,smoking,history of TB exposure,poor glycemic control were independent risk factors for T2DM-PTB,and overweight and obesity were associated with reduced risk of concurrent PTB in patients with T2DM.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Incidence and risk factors of depression in patients with metabolic syndrome

BACKGROUND Many studies have explored the relationship between depression and metabolic syndrome(MetS),especially in older people.China has entered an aging society.However,there are still few studies on the elderly in Chinese communities.AIM To investigate the incidence and risk factors of depression in MetS patients in China's Mainland and to construct a predictive model.METHODS Data from four waves of the China Health and Retirement Longitudinal Study were selected,and middle-aged and elderly patients with MetS(n=2533)were included based on the first wave.According to the center for epidemiological survey-depression scale(CESD),participants with MetS were divided into depression(n=938)and non-depression groups(n=1595),and factors related to depression were screened out.Subsequently,the 2-,4-,and 7-year follow-up data were analyzed,and a prediction model for depression in MetS patients was constructed.RESULTS The prevalence of depression in middle-aged and elderly patients with MetS was 37.02%.The prevalence of depression at the 2-,4-,and 7-year follow-up was 29.55%,34.53%,and 38.15%,respectively.The prediction model,constructed using baseline CESD and Physical Self-Maintenance Scale scores,average sleep duration,number of chronic diseases,age,and weight had a good predictive effect on the risk of depression in MetS patients at the 2-year follow-up(area under the curve=0.775,95%confidence interval:0.750-0.800,P<0.001),with a sensitivity of 68%and a specificity of 74%.CONCLUSION The prevalence of depression in middle-aged and elderly patients with MetS has increased over time.The early identification of and intervention for depressive symptoms requires greater attention in MetS patients.

Interplay between microglia and environmental risk factors in Alzheimer's disease

Alzheimer s disease,among the most common neurodegenerative disorders,is chara cterized by progressive cognitive impairment.At present,the Alzheimer’s disease main risk remains genetic ris ks,but major environmental fa ctors are increasingly shown to impact Alzheimer’s disease development and progression.Microglia,the most important brain immune cells,play a central role in Alzheimer’s disease pathogenesis and are considered environmental and lifestyle"sensors."Factors like environmental pollution and modern lifestyles(e.g.,chronic stress,poor dietary habits,sleep,and circadian rhythm disorde rs)can cause neuroinflammato ry responses that lead to cognitive impairment via microglial functioning and phenotypic regulation.However,the specific mechanisms underlying interactions among these facto rs and microglia in Alzheimer’s disease are unclear.Herein,we:discuss the biological effects of air pollution,chronic stress,gut micro biota,sleep patterns,physical exercise,cigarette smoking,and caffeine consumption on microglia;consider how unhealthy lifestyle factors influence individual susceptibility to Alzheimer’s disease;and present the neuroprotective effects of a healthy lifestyle.Toward intervening and controlling these environmental risk fa ctors at an early Alzheimer’s disease stage,understanding the role of microglia in Alzheimer’s disease development,and to rgeting strategies to to rget microglia,co uld be essential to future Alzheimer’s disease treatments.

Adolescent suicide risk factors and the integration of socialemotional skills in school-based prevention programs

Adolescents are considered one of the most vulnerable groups affected by suicide.Rapid changes in adolescents’physical and mental states,as well as in their lives,significantly and undeniably increase the risk of suicide.Psychological,social,family,individual,and environmental factors are important risk factors for suicidal behavior among teenagers and may contribute to suicide risk through various direct,indirect,or combined pathways.Social-emotional learning is considered a powerful intervention measure for addressing the crisis of adolescent suicide.When deliberately cultivated,fostered,and enhanced,selfawareness,self-management,social awareness,interpersonal skills,and responsible decision-making,as the five core competencies of social-emotional learning,can be used to effectively target various risk factors for adolescent suicide and provide necessary mental and interpersonal support.Among numerous suicide intervention methods,school-based interventions based on social-emotional competence have shown great potential in preventing and addressing suicide risk factors in adolescents.The characteristics of school-based interventions based on social-emotional competence,including their appropriateness,necessity,cost-effectiveness,comprehensiveness,and effectiveness,make these interventions an important means of addressing the crisis of adolescent suicide.To further determine the potential of school-based interventions based on social-emotional competence and better address the issue of adolescent suicide,additional financial support should be provided,the combination of socialemotional learning and other suicide prevention programs within schools should be fully leveraged,and cooperation between schools and families,society,and other environments should be maximized.These efforts should be considered future research directions.