Insight into the sorption and desorption pattern of pyrrolizidine alkaloids and their N-oxides in acidic tea (Camellia sinensis) plantation soils.

Pyrrolizidine alkaloids (PAs) and their N-oxides (PANOs) are phytotoxins produced by various plant species and have been emerged as environmental pollutants. The sorption/desorption behaviors of PAs/PANOs in soil are crucial due to the horizontal transfer of these natural products from PA-producing plants to soil and subsequently absorbed by plant roots. This study firstly investigated the sorption/desorption behaviors of PAs/PANOs in tea plantation soils with distinct characteristics. Sorption amounts for seneciphylline (Sp) and seneciphylline-N-oxide (SpNO) in three acidic soils ranged from 2.9 to 5.9 µg/g and 1.7 to 2.8 µg/g, respectively. Desorption percentages for Sp and SpNO were from 22.2% to 30.5% and 36.1% to 43.9%. In the mixed PAs/PANOs systems, stronger sorption of PAs over PANOs was occurred in tested soils. Additionally, the Freundlich models more precisely described the sorption/desorption isotherms. Cation exchange capacity, sand content and total nitrogen were identified as major influencing factors by linear regression models. Overall, the soils exhibiting higher sorption capacities for compounds with greater hydrophobicity. PANOs were more likely to migrate within soils and be absorbed by tea plants. It contributes to the understanding of environmental fate of PAs/PANOs in tea plantations and provides basic data and clues for the development of PAs/PANOs reduction technology.

Effects of dietary digestible lysine levels in breeding Japanese quails on productive and reproductive performance, egg quality, blood metabolites and immune responses.

BACKGROUND: The vegetable-based diet alone does not provide the lysine (Lys) needed to maximize poultry productive performance. OBJECTIVES: This experiment aimed to evaluate the effects of dietary digestible Lys (dLys) level on productive and reproductive performance, egg quality, blood metabolites and immune responses in breeding Japanese quails (Coturnix japonica). METHODS: The experiment was conducted in a completely randomized design with 6 treatments, 5 replicates and 15 (12 females and 3 meals) 10-week-old breeding Japanese quails each. A basal diet was formulated to meet nutritional requirements of breeding quails except dLys. The basal diet was supplemented with graded (+0.82 g/kg) levels of l-Lys-HCl, corresponding to dietary dLys levels of 0.690%, 0.755%, 0.820%, 0.885%, 0.950% and 1.015%. The experiment lasted for 12 weeks, which was divided into 3-4-week periods. RESULTS: Significant differences were observed for egg production (EP), egg mass (EM) and feed efficiency (FE) in response to increasing dietary dLys concentration with quadratic trends. The highest traits were observed in the birds fed with a diet containing 0.885% dLys. However, feed intake, egg quality, reproductive performance, blood metabolites and immune responses against sheep red blood cell inoculation were not significantly affected by increasing dietary dLys concentrations. The dLys requirements during 11-14, 15-18, 19-22 and 11-22 (overall) weeks of age for optimal EP, EM and FE, based on the quadratic broken-line regression analysis, were estimated 272, 265, 250 and 266; 293, 285, 264 and 279; and 303, 294, 281 and 293 mg/bird/day, respectively. CONCLUSIONS: The dLys requirements vary depending on the EP phase and the trait being optimized. The estimated dLys requirement for FE was higher than those for EP and EM. During the peak stage of the first laying cycle, the dietary dLys level of 0.932% and a daily intake of 303 mg dLys/bird are sufficient for optimal performance.

Examining the Relationship Between Obesity and Income Distribution Using Body Mass Index (BMI) and Body Shape and Size Index (BSSI): A Case Study of Pakistan.

Introduction: Obesity and income (wage) distribution have emerged as one of the most serious public health concerns but in this research, the study is continued under body mass index (BMI) and body shape and size index (BSSI) among children and adults in Pakistan. Methods: This cross-sectional study investigated the health-related behaviors and outcomes of 2,223 children and adults aged 2 to 19 years from Multan, Pakistan, including both males and females, providing a comprehensive understanding of the health status in this population. Data about gender, weight, height, age, family income and other demographic measures are recorded. For the measurement of BMI and BSSI, the latest formulas and methods were used. Different variables were applied through statistical description understudy. To check out the wage distribution in BSSI and BMI, the comparative approach was used and performed a role in making charts for BSSI and BMI against family income, age group and gender. Results: The mean BMI and BSSI for complete data are 18.00 and 0.23 for the age group of 2 to 5 years of children with family income less than 10,000. Similarly, these figures are 20.59 and 0.29 for the family income greater than 50,000. Conclusion: Most important things have been observed by this study, that income greatly affected the rate of obesity. BMI and BSSI increased by increasing the family income of children and adults in Pakistan. BMI and BSSI show high figures for female respondents as compared to male ones, observed by this research.

A Mixed-Effect Kernel Machine Regression Model for Integrative Analysis of Alpha Diversity in Microbiome Studies.

Increasing evidence suggests that human microbiota plays a crucial role in many diseases. Alpha diversity, a commonly used summary statistic that captures the richness and/or evenness of the microbial community, has been associated with many clinical conditions. However, individual studies that assess the association between alpha diversity and clinical conditions often provide inconsistent results due to insufficient sample size, heterogeneous study populations and technical variability. In practice, meta-analysis tools have been applied to integrate data from multiple studies. However, these methods do not consider the heterogeneity caused by sequencing protocols, and the contribution of each study to the final model depends mainly on its sample size (or variance estimate). To combine studies with distinct sequencing protocols, a robust statistical framework for integrative analysis of microbiome datasets is needed. Here, we propose a mixed-effect kernel machine regression model to assess the association of alpha diversity with a phenotype of interest. Our approach readily incorporates the study-specific characteristics (including sequencing protocols) to allow for flexible modeling of microbiome effect via a kernel similarity matrix. Within the proposed framework, we provide three hypothesis testing approaches to answer different questions that are of interest to researchers. We evaluate the model performance through extensive simulations based on two distinct data generation mechanisms. We also apply our framework to data from HIV reanalysis consortium to investigate gut dysbiosis in HIV infection.

Assessment and simulation of eco-environmental quality changes in rapid rural urbanization: Xiong'an New Area, China.

Xiong'an New Area was established as a state-level new area in 2017 and serves as a typical representative area for studying the ecological evolution of rural areas under rapid urbanization in China. Remote sensing-based ecological index (RSEI) is a regional eco-environmental quality (EEQ) assessment index. Many studies have employed RSEI to achieve rapid, objective, and effective quantitative assessment of the spatio-temporal changes of regional EEQ. However, research that combines RSEI with machine learning algorithms to conduct multi-scenario simulation of EEQ is still relatively scarce. Therefore, this study assessed and simulated EEQ changes in Xiong'an and revealed that: (1) The large-scale construction has led to an overall decline in EEQ, with the RSEI decreasing from 0.648 in 2014 to 0.599 in 2021. (2) Through the multi-scenario simulation, the non-unidirectional evolution of RSEI during the process of urban-rural construction has been revealed, specifically characterized by a significant decline followed by a slight recovery. (3) The marginal effects of urban-rural construction features for simulated RSEI demonstrate an inverted "U-shaped" curve in the relationship between urbanization and EEQ. This indicates that urbanization and EEQ may not be absolute zero-sum. These findings can provide scientific insights for maintaining and improving the regional EEQ in urban-rural construction.

Exploring key factors influencing depressive symptoms among middle-aged and elderly adult population: A machine learning-based method.

OBJECTIVE: This paper aims to investigate the key factors, including demographics, socioeconomics, physical well-being, lifestyle, daily activities and loneliness that can impact depressive symptoms in the middle-aged and elderly population using machine learning techniques. By identifying the most important predictors of depressive symptoms through the analysis, the findings can have important implications for early depression detection and intervention. PARTICIPANTS: For our cross-sectional study, we recruited a total of 976 volunteers, with a specific focus on individuals aged 50 and above. Each participant was requested to provide their demographic, socioeconomic information and undergo several physical health tests. Additionally, they were asked to respond to questionnaires that assessed their mental well-being. Furthermore, participants were requested to maintain an activity log for a continuous 14-day period, starting from the day after they signed up. They had the option to use either a provided mobile application or paper to record their activities. METHODS: We evaluated multiple machine learning models to find the best-performing one. Subsequently, we conducted post-hoc analysis to extract the variable significance from the selected model to gain deeper insights into the factors influencing depression. RESULTS: Logistic Regression was chosen as it exhibited superior performance across other models, with AUC of 0.807 ± 0.038, accuracy of 0.798 ± 0.048, specificity of 0.795 ± 0.061, sensitivity of 0.819 ± 0.097, NPV of 0.972 ± 0.013 and PPV of 0.359 ± 0.064. The top influential predictors identified in the model included loneliness, health indicator (i.e. frailty, eyesight, functional mobility), time spent on activities (i.e. staying home, doing exercises and visiting friends) and perceived income adequacy. CONCLUSION: These findings have the potential to identify individuals at risk of depression and prioritize interventions based on the influential factors. The amount of time dedicated to daily activities emerges as a significant indicator of depression risk among middle-aged and elderly individuals, along with loneliness, physical health indicators and perceived income adequacy.

Factors affecting the survival of prediabetic patients: comparison of Cox proportional hazards model and random survival forest method.

BACKGROUND: The worldwide prevalence of type 2 diabetes mellitus in adults is experiencing a rapid increase. This study aimed to identify the factors affecting the survival of prediabetic patients using a comparison of the Cox proportional hazards model (CPH) and the Random survival forest (RSF). METHOD: This prospective cohort study was performed on 746 prediabetics in southwest Iran. The demographic, lifestyle, and clinical data of the participants were recorded. The CPH and RSF models were used to determine the patients' survival. Furthermore, the concordance index (C-index) and time-dependent receiver operating characteristic (ROC) curve were employed to compare the performance of the Cox proportional hazards (CPH) model and the random survival forest (RSF) model. RESULTS: The 5-year cumulative T2DM incidence was 12.73%. Based on the results of the CPH model, NAFLD (HR = 1.74, 95% CI: 1.06, 2.85), FBS (HR = 1.008, 95% CI: 1.005, 1.012) and increased abdominal fat (HR = 1.02, 95% CI: 1.01, 1.04) were directly associated with diabetes occurrence in prediabetic patients. The RSF model suggests that factors including FBS, waist circumference, depression, NAFLD, afternoon sleep, and female gender are the most important variables that predict diabetes. The C-index indicated that the RSF model has a higher percentage of agreement than the CPH model, and in the weighted Brier Score index, the RSF model had less error than the Kaplan-Meier and CPH model. CONCLUSION: Our findings show that the incidence of diabetes was alarmingly high in Iran. The results suggested that several demographic and clinical factors are associated with diabetes occurrence in prediabetic patients. The high-risk population needs special measures for screening and care programs.

A Linear Regression Approach for Best Scanline Determination in the Object to Image Space Transformation Using Pushbroom Images.

The use of linear array pushbroom images presents a new challenge in photogrammetric applications when it comes to transforming object coordinates to image coordinates. To address this issue, the Best Scanline Search/Determination (BSS/BSD) field focuses on obtaining the Exterior Orientation Parameters (EOPs) of each individual scanline. Current solutions are often impractical for real-time tasks due to their high time requirements and complexities. This is because they are based on the Collinearity Equation (CE) in an iterative procedure for each ground point. This study aims to develop a novel BSD framework that does not need repetitive usage of the CE with a lower computational complexity. The Linear Regression Model (LRM) forms the basis of the proposed BSD approach and uses Simulated Control Points (SCOPs) and Simulated Check Points (SCPs). The proposed method is comprised of two main steps: the training phase and the test phase. The SCOPs are used to calculate the unknown parameters of the LR model during the training phase. Then, the SCPs are used to evaluate the accuracy and execution time of the method through the test phase. The evaluation of the proposed method was conducted using ten various pushbroom images, 5 million SCPs, and a limited number of SCOPs. The Root Mean Square Error (RMSE) was found to be in the order of ten to the power of negative nine (pixel), indicating very high accuracy. Furthermore, the proposed approach is more robust than the previous well-known BSS/BSD methods when handling various pushbroom images, making it suitable for practical and real-time applications due to its high speed, which only requires 2-3 s of time.

Application of the Length of Index and Ring Fingers to Estimate the Stature.

INTRODUCTION: Stature contributes as a crucial element of an individual's physical appearance and can be instrumental in establishing their identity. In cases where the body is extensively mutilated, decomposed, or reduced to skeletal remains, stature becomes an essential component in identifying the unknown by means of measuring the skeletal remains. Its estimation relies on the principle that an individual's height has a definite and linear relationship with specific body parts and long bones. This process, together with assessing age, sex, and race constitutes the essential components of the anthropological protocol. Stature estimation can be accomplished through both anatomical and mathematical approaches. The present study clearly defines regression models for height estimation from finger lengths. The formula derived can prove particularly valuable in Medico-legal scenarios, as it can be applied effectively even when only a portion of the body is accessible. AIM: The purpose of the present study is to estimate the stature of individuals by measuring the length of the index and ring fingers. MATERIALS AND METHOD: The current study acquired three measurements, such as stature, right/left index finger length (RIFL/LIFL), and ring finger length (RFL), from 220 samples, including 110 males and 110 females, respectively, between the age groups of 20 and 60 years. RESULT: The application of the length of the index and ring finger in forensic investigations holds significance due to their potential as reliable predictors of an individual's height. According to the findings of the study, males showed significantly higher stature than females. A statistically significant correlation was also observed (p-value = 0) between stature and finger lengths (IFL, RFL) in both hands. The highest correlation coefficients were found for the left RFL (r = 0.688) in females and the LIFL (r = 0.552) in males. Additionally, males showed significantly longer index and RFL than females. Linear regression models for the estimation of stature from ring and index finger length were also derived successfully. CONCLUSION:  The results obtained from the present study exhibit potential use to evaluate the utility of measuring index and RFLs for determining stature and predicting the precision of regression models by employing those parameters. The models derived from this study can serve as corroborative evidence for identifying mutilated body parts or unknown remains.

Trends and multi-model prediction of hepatitis B incidence in Xiamen.

Background: This study aims to analyze the trend of Hepatitis B incidence in Xiamen City from 2004 to 2022, and to select the best-performing model for predicting the number of Hepatitis B cases from 2023 to 2027. Methods: Data were obtained from the China Information System for Disease Control and Prevention (CISDCP). The Joinpoint Regression Model analyzed temporal trends, while the Age-Period-Cohort (APC) model assessed the effects of age, period, and cohort on hepatitis B incidence rates. We also compared the predictive performance of the Neural Network Autoregressive (NNAR) Model, Bayesian Structural Time Series (BSTS) Model, Prophet, Exponential Smoothing (ETS) Model, Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, and Hybrid Model, selecting the model with the highest performance to forecast the number of hepatitis B cases for the next five years. Results: Hepatitis B incidence rates in Xiamen from 2004 to 2022 showed an overall declining trend, with rates higher in men than in women. Higher incidence rates were observed in adults, particularly in the 30-39 age group. Moreover, the period and cohort effects on incidence showed a declining trend. Furthermore, in the best-performing NNAR(10, 1, 6)[12] model, the number of new cases is predicted to be 4271 in 2023, increasing to 5314 by 2027. Conclusions: Hepatitis B remains a significant issue in Xiamen, necessitating further optimization of hepatitis B prevention and control measures. Moreover, targeted interventions are essential for adults with higher incidence rates.

Analysis of mortality in Parkinson disease in China: Exploration of recent and future trends.

Objectives: Parkinson disease (PD) is the third leading cause of mortality among middle-aged and older individuals in China. This study aimed to explore the trends and distribution features of PD mortality in China from 2013 to 2021 and make predictions for the next few decades. Methods: Relevant data were obtained from the Chinese Center for Disease Control and Prevention Disease Surveillance Point system. The joinpoint regression model was used to evaluate trends. The R software was used to predict future trends. Results: Age-standardized mortality rate (ASMR) of PD increased from 0.59 per 100,000 individuals to 1.22 per 100,000 individuals from 2013 to 2021, with an average annual percent change (AAPC) of 9.50 (95% CI: 8.24-10.78). The all-age ASMR of PD were higher in male individuals than in female individuals, and ASMR increased with age. The number of deaths and ASMR increased gradually from west to east and from rural to urban areas. Furthermore, ASMR is expected to increase to 2.66 per 100,000 individuals by 2040. Conclusions: The heightened focus on the ASMR of PD among male individuals, urban areas, eastern China, and individuals aged ≥85 years has become a key determinant in further decreasing mortality, thereby exhibiting novel challenges to effective strategies for disease prevention and control.

Using the Electronic Health Record to Develop a Gastric Cancer Risk Prediction Model.

Background and Aims: Gastric cancer (GC) is a leading cause of cancer incidence and mortality globally. Population screening is limited by the low incidence and prevalence of GC in the United States. A risk prediction algorithm to identify high-risk patients allows for targeted GC screening. We aimed to determine the feasibility and performance of a logistic regression model based on electronic health records to identify individuals at high risk for noncardia gastric cancer (NCGC). Methods: We included 614 patients who had a diagnosis of NCGC between ages 40 and 80 years and who were seen at our large tertiary medical center in multiple states between 2010 and 2021. Controls without a diagnosis of NCGC were randomly selected in a 1:10 ratio of cases to controls. Multiple imputation by chained equations for missing data followed by logistic regression on imputed datasets was used to estimate the probability of NCGC. Area under the curve and the 0.632 estimator was used as the estimate for discrimination. Results: The 0.632 estimator value was 0.731, indicating robust model performance. Probability of NCGC was higher with increasing age (odds ratio [OR] = 1.16, 95% confidence interval [CI]: 1.04-1.3), male sex (OR = 1.97; 95% CI: 1.64-2.36), Black (OR = 3.07; 95% CI: 2.46-3.83) or Asian race (OR = 4.39; 95% CI: 2.60-7.42), tobacco use (OR = 1.61; 95% CI: 1.34-1.94), anemia (OR = 1.35; 95% CI: 1.09-1.68), and pernicious anemia (OR = 6.12, 95% CI: 3.42-10.95). Conclusion: We demonstrate the feasibility and good performance of an electronic health record-based logistic regression model for estimating the probability of NCGC. Future studies will refine and validate this model, ultimately identifying a high-risk cohort who could be eligible for NCGC screening.

A hybrid approach for modeling bicycle crash frequencies: Integrating random forest based SHAP model with random parameter negative binomial regression model.

To effectively capture and explain complex, nonlinear relationships within bicycle crash frequency data and account for unobserved heterogeneity simultaneously, this study proposes a new hybrid framework that combines the Random Forest-based SHapley Additive exPlanations (RF-SHAP) method with a random parameter negative binomial regression model (RPNB). First, four machine learning algorithms, including random forest (RF), support vector machine (SVM), gradient boosting machine (GBM), and Extreme Gradient Boosting (XGBoost), were compared for variable importance calculation. The RF algorithm, demonstrating the best performance, was selected and integrated into an interpretable machine learning-based method (i.e., RF-SHAP) to provide an interpretable measure of each variable's impact, which is critical for understanding the model's predictions results. Finally, the RF-SHAP method was combined with the RPNB model to explore individual-specific variations that influence crash frequency predictions. Using 288 traffic analysis zones (TAZs) in Greater London and various regional risk factors for bicycle crash frequency, the proposed framework was validated. The results indicate that the proposed framework demonstrates improved prediction accuracy and better factor interpretation in analyzing bicycle crash frequency. The model exhibits consistent Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, indicating its reliable explanatory power. Furthermore, there is a significant improvement in the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This suggests that the proposed model effectively combines the explanatory power of statistical models with the forecasting powers of data-driven models. The interpretability of SHAP values, coupled with the causal insights from RPNB, provides policymakers with actionable information to develop targeted interventions.

Acarbose might be associated with reduced risk of gastric cancer in patients with diabetes mellitus: A nationwide population-based cohort study.

BACKGROUND: Several epidemiologic studies have revealed a higher risk of cancer in patients with diabetes mellitus (DM) relative to the general population. To investigate whether the use of acarbose was associated with higher/lower risk of new-onset cancers. METHOD: We conducted a retrospective cohort study, using a population-based National Health Insurance Research Database of Taiwan. Both inpatients and outpatients with newly onset DM diagnosed between 2000 and 2012 were collected. The Adapted Diabetes Complications Severity Index (aDCSI) was used to adjust the severity of DM. The Cox proportional hazards regression model was used to estimate the hazard ratio (HR) of disease. RESULTS: A total of 22 502 patients with newly diagnosed DM were enrolled. The Cox proportional hazards regression model indicating acarbose was neutral for risk for gastroenterological malignancies, when compared to the acarbose non-acarbose users group. However, when gastric cancer was focused, acarbose-user group had significantly lowered HR than non-acarbose users group (p = 0.003). After adjusted for age, sex, cancer-related comorbidity, severity of DM, and co-administered drugs, the HR of gastric cancer risk was 0.43 (95% CI = 0.25-0.74) for acarbose-user patients. CONCLUSION: This long-term population-based study demonstrated that acarbose might be associated with lowered risk of new-onset gastric cancer in diabetic patients after adjusting the severity of DM.

Health risk assessment of municipal solid waste incineration emissions based on regression analysis.

This study examined the potential health risks posed by the operation of 96 waste-to-energy (WtE) plants in 30 cities in the Bohai Rim of China. Utilizing a sophisticated simulation approach, the Weather Research and Forecasting (WRF) model coupled with the California Puff (CALPUFF) model, we obtained the spatial distribution of pollutants emitted by WtE plants in the atmosphere. Hazard indices (HI) and cancer risks (CR) were calculated for each plant using the United States Environmental Protection Agency's recommended methodologies. The results indicated that both HIs and CRs were generally low, with values below the accepted threshold of 1.0 and 1.0 × 10-6, respectively. Specifically, the average HI and CR values for the entire study area were 2.95 × 10-3 and 3.43 × 10-7, respectively. However, some variability in these values was observed depending on the location and type of WtE plant. A thorough analysis of various parameters, such as waste composition, moisture content, and operating conditions, was conducted to identify the factors that influence the health risks associated with incineration. The findings suggest that proper waste sorting and categorization, increased cost of construction, and elevated height of chimneys are effective strategies for reducing the health risks associated with incineration. Overall, this study provides valuable insights into the potential health risks associated with WtE plants in the Bohai Rim region of China. The findings can serve as useful guidelines for law enforcement wings and industry professionals seeking to minimize the risks associated with municipal solid waste (MSW) management and promote sustainable development.

A new two-parameter over-dispersed discrete distribution with mathematical properties, estimation, regression model and applications.

This paper focuses on the derivation of a new two-parameter discrete probability distribution. The new model is derived by mixing Poisson and Loai distributions and is named "Poisson Loai Distribution". The paper explores various mathematical properties of the new model, introducing a count-regression model based on this distribution. The parameters of the model are estimated using the maximum likelihood estimation method. A comprehensive simulation study is utilized to assess the behavior of derived estimators. The importance of the proposed distribution is confirmed through the analysis of three real datasets. It is found that the suggested distribution has the greatest match when compared to all rival distributions, and it may be a viable alternative for assessing dispersed count data.

Impact of urban-rural development and its industrial elements on regional economic growth: An analysis based on provincial panel data in China.

Urban-rural development is an important driving force for regional economic growth. The existing researches have studied this issue from various perspectives, but they ignore the impact of big data on the economy. In the post pandemic era, big data, as an emerging production factor, has a significant indicative effect in promoting urban-rural economic recovery and fostering new business forms. Therefore, fully considering the factor of big data can help reveal its impact mechanism on urban-rural economic growth in the post-epidemic period. Based on the data of 30 provinces and cities in China, this paper introduced big data on the basis of traditional models and constructed a multi-dimensional factor indicator system. At the same time, the panel regression model was established by using unit root test, Hausman test and precision test. Through benchmark regression and heterogeneity analysis, the impact of urban-rural development factors on economic growth was discussed. The results showed that the panel model passed all tests, and its regression error was stable below 5 %. Transportation, technology, and the three major industries can all promote positive economic growth, with a significance of 1 %. The three industries' contribution to economic growth ranks the third, second and first industries in order. In addition, the good ecological environment contributes to the benign economic growth during the study period. A 1 % increase in forest cover would drive economic growth by 0.215 %. But the impact of public's attention on the overall economy was an indirect effect manifested through its physical industries.The regional heterogeneity indicated that each element had different effects on economic development in eastern, central and western regions. Based on its results, this paper proposed suggestions for each region. In addition, this study found that the Internet attention reflected by big data did not directly drive economic growth, but affected economic growth through indirect channels such as information flow and resource allocation of real industries. This study provided data support for the existing theoretical review, and provided policy reference for the rational planning and industrial layout of China's regional economy.

Exploration of Geographical Environmental Factors Influencing Regional Population Mortality Patterns in China.

OBJECTIVES: The regional population mortality patterns in China exhibit substantial geographical distribution characteristics. This paper aims to explore the impact and mechanisms of geographical environmental factors on regional population mortality patterns. METHODS: This study first utilized the data from China's Seventh Population Census to obtain mortality patterns for the 31 provincial-level administrative regions. Subsequently, a functional regression method was employed to explore the geographical environmental driving factors of regional mortality patterns. RESULTS: The study provides a detailed explanation of the mechanisms and marginal contributions of key geographical environmental factors at different age groups. CONCLUSIONS: (1) The impact of geographical environmental factors on mortality patterns shows distinct phased characteristics. Mortality patterns before the age of 40 years are hardly influenced by geographical environmental factors, with a noticeable impact beginning at ages 40-69 years and reaching the maximum influence after the age of 70 years. (2) In mortality patterns at ages 40-69 years, average altitude have the most substantial impact, followed by extreme low-temperature days and PM2.5 concentration. In mortality patterns at ages 70-94 years, high-temperature days have the greatest influence, followed by the impact of SO2 concentration. (3) In comparisons based on gender, socioeconomic factors, and geographical environmental factors, gender and urban-rural differences have the most significant impact on regional population mortality patterns, followed by the influence of other socioeconomic factors, with geographical environmental factors having a relatively smaller impact.

Isomer of per- and polyfluoroalkyl substances and red blood cell indices in adults: The Isomers of C8 Health Project in China.

This study aimed to explore the isomer-specific, sex-specific, and joint associations of PFAS and red blood cell indices. We used data of 1,238 adults from the Isomers of C8 Health Project in China. Associations of PFAS isomers and red blood cell indices were explored using multiple linear regression models, Bayesian Kernel Machine Regression models and subgroup analysis across sex. We found that serum concentration of linear (n-) and branched (Br-) isomers of perfluorooctane sulfonate (PFOS) and perfluorohexanesulfonic acid (PFHxS) were significantly associated with red blood cell indices in single-pollutant models, with stronger associations observed for n-PFHxS than Br-PFHxS, in women than in men. For instance, the estimated percentage change in hemoglobin concentration for n-PFHxS (3.65%; 95% CI: 2.95%, 4.34%) was larger than that for Br-PFHxS (0.96%; 95% CI: 0.52%, 1.40%). The estimated percentage change in red blood cell count for n-PFHxS in women (2.55%; 95% CI: 1.81%, 3.28%) was significantly higher than that in men (0.12%; 95% CI: -1.04%, 1.29%) (Pinter < 0.001). Similarly, sex-specific positive association of PFAS mixture and outcomes was observed. Therefore, the structure, susceptive population, and joint effect of PFAS isomers should be taken into consideration when evaluating the health risk of chemicals.

Association between changes in high-density lipoprotein cholesterol and risk of cardiovascular disease.

BACKGROUND: The present study was performed to determine the association between changes in the HDL-C concentration and incident CVD. METHODS: Time-dependent Cox regression models were used to evaluate the association between changes in the HDL-C concentration and the risk of incident CVD. Participants were followed up from 2015 to 2021. RESULTS: In total, 24,123 participants with a median follow-up of 4.26 years were analyzed, and the mean age of the cohort was 56.24 years, 57.8 % were female, 24.3 % were current smokers, and 12.8 % had a history of alcohol use. Low, normal, and high HDL-C was defined as <40, 40-80, and >80 mg/dL, respectively. The average time for the two HDL-C measurements was 2.8 years,compared with participants whose HDL-C was maintained at a normal level, the risk of CVD was higher in those whose HDL-C changed to a low level, remained unchanged at a low level(HR, 1.24; 95 % CI, 1.01-1.40,P < 0.001), similarly, the risk of CVD was higher in those whose HDL-C changed from very high level to normal level(HR, 0.81; 95 % CI, 0.67-0.99,P = 0.039). Also compared with participants whose HDL-C was maintained at a normal level, the risk of CVD was lower in those whose HDL-C increased from low to normal and high(HR, 0.80; 95 % CI, 0.66-0.98,P = 0.029). CONCLUSIONS: Participants whose HDL-C changed to a low level and whose low HDL-C level was maintained had a higher risk of CVD, whereas participants whose HDL-C changed from low to high had a lower risk of CVD.