Unveiling the nonlinear dynamics of player performance in China's super league as a function of age.

To explore the dynamics in physical and technical performance of professional football players and changes across age groups. Match statistics were collected from 1900 games across ten seasons (2012-2021) in the Chinese Super League. Generalized additive models visualized age-related trends in 12 key performance indicators including technical and physical variables. Revealed nonlinear trajectories characterized by rapid early declines, stable peak periods and accelerated late decreases. Physical indicators decreased progressively from the early 20 s before stabilizing briefly then declining further after 30. Conversely, technical metrics gradually improved into the late 20 s and early 30 s prior to decreasing again. This study provides novel evidence that football performance changes nonlinearly across age. Targeted training and development strategies should be tailored to the specific needs of different career stages.

Temperature modification of air pollutants and their synergistic effects on respiratory diseases in a semi-arid city in Northwest China.

Air pollutants and temperature are significant threats to public health, and the complex linkages between the environmental factors and their interactions harm respiratory diseases. This study is aimed to analyze the impact of air pollutants and meteorological factors on respiratory diseases and their synergistic effects in Dingxi, a city in northwestern China, from 2018 to 2020 using a generalized additive model (GAM). Relative risk (RR) was employed to quantitatively evaluate the temperature modification on the short-term effects of PM2.5 and O3 and the synergistic effects of air pollutants (PM2.5 and O3) and meteorological elements (temperature and relative humidity) on respiratory diseases. The results indicated that the RRs per inter-quatile range (IQR) rise in PM2.5 and O3 concentrations were (1.066, 95% CI: 1.009-1.127, lag2) and (1.037, 95% CI: 0.975-1.102, lag4) for respiratory diseases, respectively. Temperature stratification suggests that the influence of PM2.5 on respiratory diseases was significantly enhanced at low and moderate temperatures, and the risk of respiratory diseases caused by O3 was significantly increased at high temperatures. The synergy analysis demonstrated significant a synergistic effect of PM2.5 with low temperature and high relative humidity and an antagonistic effect of high relative humidity and O3 on respiratory diseases. The findings would provide a scientific basis for the impact of pollutants on respiratory diseases in Northwest China.

Independent association between prostate-specific antigen nadir and PSA progression-free survival in first-line abiraterone acetate treatment in castration-resistant prostate cancer patients: a pilot study.

Background: There is limited evidence regarding the correlation between prostate-specific antigen (PSA) kinetics and clinical outcomes. Therefore, after regulating other covariates, we studied patients with castration-resistant prostate cancer who received abiraterone acetate as the first-line treatment. In this study, we investigated whether time to PSA nadir was independently associated with PSA progression-free survival (PFS). Methods: As a retrospective cohort study, this study contained a total of 77 castration-resistant prostate cancer patients who received abiraterone acetate from October 2015 to April 2021 in a Chinese hospital. The dependent variable was PSA-PFS. The objective independent variable was time to PSA nadir (TTPN). Covariates involved in this study included age, duration of androgen deprivation therapy (ADT), PSA level at baseline, time of 50% PSA decline, time of PSA decline to nadir, Gleason score, bone metastasis, previous treatment, PSA decline <50% in 3 months, PSA to nadir in 3 months, PSA decline <90%, PSA decline <0.2 ng/mL, and PSA flare. Results: For the 77 subjects, their mean age was 72.70 ± 8.08 years. Fully calibrated linear regression findings indicated that PSA decline and kinetics were positively associated with PFS (months) after adjusting confounders (ß = 0.77, 95% CI: 0.11-1.44). A non-linear relationship was not detected between PSA decline or PSA kinetics and progression-free survival. Conclusion: According to the data of this study, there was a correlation between early PSA changes and patients treated with abiraterone acetate.

Analysis of the Distribution Characteristics of Jellyfish and Environmental Factors in the Seawater Intake Area of the Haiyang Nuclear Power Plant in China.

In recent years, there have been frequent jellyfish outbreaks in Chinese coastal waters, significantly impacting the structure, functionality, safety, and economy of nuclear power plant cooling water intake and nearby ecosystems. Therefore, this study focuses on jellyfish outbreaks in Chinese coastal waters, particularly near the Shandong Peninsula. By analyzing jellyfish abundance data, a Generalized Additive Model integrating environmental factors reveals that temperature and salinity greatly influence jellyfish density. The results show variations in jellyfish density among years, with higher densities in coastal areas. The model explains 42.2% of the variance, highlighting the positive correlation between temperature (20-26 °C) and jellyfish density, as well as the impact of salinity (27.5-29‱). Additionally, ocean currents play a significant role in nearshore jellyfish aggregation, with a correlation between ocean currents and site coordinates. This study aims to investigate the relationship between jellyfish blooms and environmental factors. The results obtained from the study provide data support for the prevention and control of blockages in nuclear power plant cooling systems, and provide a data basis for the implementation of monitoring measures in nuclear power plants.

A robust regression model for bounded count health data.

Bounded count response data arise naturally in health applications. In general, the well-known beta-binomial regression model form the basis for analyzing this data, specially when we have overdispersed data. Little attention, however, has been given to the literature on the possibility of having extreme observations and overdispersed data. We propose in this work an extension of the beta-binomial regression model, named the beta-2-binomial regression model, which provides a rather flexible approach for fitting a regression model with a wide spectrum of bounded count response data sets under the presence of overdispersion, outliers, or excess of extreme observations. This distribution possesses more skewness and kurtosis than the beta-binomial model but preserves the same mean and variance form of the beta-binomial model. Additional properties of the beta-2-binomial distribution are derived including its behavior on the limits of its parametric space. A penalized maximum likelihood approach is considered to estimate parameters of this model and a residual analysis is included to assess departures from model assumptions as well as to detect outlier observations. Simulation studies, considering the robustness to outliers, are presented confirming that the beta-2-binomial regression model is a better robust alternative, in comparison with the binomial and beta-binomial regression models. We also found that the beta-2-binomial regression model outperformed the binomial and beta-binomial regression models in our applications of predicting liver cancer development in mice and the number of inappropriate days a patient spent in a hospital.

Association between serum chloride and in-hospital mortality in congestive heart failure with diabetes: Data from the MIMIC-IV database.

Background: Congestive heart failure (CHF) demonstrates a heightened prevalence in individuals with diabetes mellitus within Intensive Care Units. The occurrence of abnormal chloride levels is frequently observed in critically ill patients, yet its clinical significance remains subject to debate. This study endeavors to explore the relationship between serum chloride levels and in-hospital mortality among patients affected by both congestive heart failure and diabetes. Methods: A retrospective cohort study was conducted, utilizing data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database, focusing on adult patients in the United States. The impact of serum chloride levels upon ICU admission on in-hospital mortality was analyzed using multivariable logistic regression models, generalized additive models and subgroup analysis. Results: The study encompassed 7,063 patients with coexisting diabetes and congestive heart failure. The fully adjusted model revealed an inverse association between serum chloride levels and in-hospital mortality. As a tertile variable (Q3 vs Q1), the odds ratio (OR) was 0.73 with a 95% confidence interval (CI) of 0.54-0.98 (p = 0.039). As a continuous variable, per 1 mmol/L increment, the OR (95% CI) was 0.97 (0.96-0.99, p = 0.01). The relationship between serum chloride and in-hospital mortality demonstrated linearity (non-linear p = 0.958). Stratified analyses further validated the robustness of this correlation. Conclusions: Serum chloride levels exhibited a negative association with in-hospital mortality in patients with both congestive heart failure and diabetes. Nevertheless, prospective, randomized, controlled studies are warranted to corroborate and validate the findings presented in this investigation.

Unraveling the dynamic transcriptomic changes during the dimorphic transition of Talaromyces marneffei through time-course analysis.

Introduction: Systemic dimorphic fungi pose a significant public health challenge, causing over one million new infections annually. The dimorphic transition between saprophytic mycelia and pathogenic yeasts is strongly associated with the pathogenesis of dimorphic fungi. However, despite the dynamic nature of dimorphic transition, the current omics studies focused on dimorphic transition primarily employ static strategies, partly due to the lack of suitable dynamic analytical methods. Methods: We conducted time-course transcriptional profiling during the dimorphic transition of Talaromyces marneffei, a model organism for thermally dimorphic fungi. To capture non-uniform and nonlinear transcriptional changes, we developed DyGAM-NS (dynamic optimized generalized additive model with natural cubic smoothing). The performance of DyGAM-NS was evaluated by comparison with seven other commonly used time-course analysis methods. Based on dimorphic transition induced genes (DTIGs) identified by DyGAM-NS, cluster analysis was utilized to discern distinct gene expression patterns throughout dimorphic transitions of T. marneffei. Simultaneously, a gene expression regulatory network was constructed to probe pivotal regulatory elements governing the dimorphic transitions. Results: By using DyGAM-NS, model, we identified 5,223 DTIGs of T. marneffei. Notably, the DyGAM-NS model showcases performance on par with or superior to other commonly used models, achieving the highest F1 score in our assessment. Moreover, the DyGAM-NS model also demonstrates potential in predicting gene expression levels throughout temporal processes. The cluster analysis of DTIGs suggests divergent gene expression patterns between mycelium-to-yeast and yeast-to-mycelium transitions, indicating the asymmetrical nature of two transition directions. Additionally, leveraging the identified DTIGs, we constructed a regulatory network for the dimorphic transition and identified two zinc finger-containing transcription factors that potentially regulate dimorphic transition in T. marneffei. Discussion: Our study elucidates the dynamic transcriptional profile changes during the dimorphic transition of T. marneffei. Furthermore, it offers a novel perspective for unraveling the underlying mechanisms of fungal dimorphism, emphasizing the importance of dynamic analytical methods in understanding complex biological processes.

Relationship between triglyceride-glucose index and blood eosinophils among asthmatic individuals in the USA.

BACKGROUND: Presently, the majority of investigations primarily evaluate the correlation between triglyceride-glucose index (TyGI) with lung diseases, such as asthma. However, they did not delve into the correlation between TyGI and inflammatory responses related to the disease. Few studies have explored the association between TyGI and blood eosinophil count (BEOC). Thus, National Health and Nutrition Examination Survey (NHANES) data were used in this study to evaluate the correlation between TyGI and BEOC in individuals with asthma. METHODS: This study investigated 3902 individuals with asthma. Linear regression analysis was performed to investigate the association between TyGI and BEOC in patients with asthma. Subsequently, the GAM and threshold effect models were used to validate the presence of either a nonlinear or linear association between TyGI and BEOC. Finally, stratified analyses were conducted to ascertain the correlations between different subgroups. RESULTS: Four linear regression models confirmed a positive linear correlation between TyGI and BEOC in patients with asthma. In Model D, which controlled for all covariates, BEOC increased by 12.44 cells/uL for every extra unit of TyGI. The GAM and threshold effect models further verified the positive linear correlation between TyGI and BEOC. The XGBoost model indicated that the six most significant variables influencing BEOC, in order of relative importance, were age, cholesterol level, body mass index (BMI), poverty-to-income ratio (PIR), BNEUC, and TyGI. CONCLUSIONS: In patients with asthma, the study discovered a linear positive correlation between TyGI and BEOC. This indicates a potential connection between TyGI and alterations in the immune status of individuals with asthma, which may help detect abnormalities in a timely manner and provide a reference for clinical decision-making. This study offers fresh insights for the future exploration of the management and treatment of asthma.

Quantifying the impact of anthropogenic emissions and aquatic environmental impacts on sedimentary mercury variations in a typical urban river.

In urban and industrial regions, sedimentary mercury (Hg) serves as the crucial indicator for Hg pollution, posing potential risks to ecology and human health. The physicochemical processes of Hg in aquatic environments are influenced by various factors such as anthropogenic emissions and aquatic environmental impacts, making it challenging to quantify the drivers of total mercury (THg) variations. Here, we analyzed the spatiotemporal variations, quantified driving factors, and assessed accumulation risks of sedimentary THg from the mainstream of a typical urban river (Haihe River). THg in the urban region (37-3237 ng g-1) was significantly higher (t-test, p < 0.01) than in suburban (71-2317 ng g-1) and developing regions (156-916 ng g-1). The sedimentary THg in suburban and developing regions increased from 2003 to 2018, indicating the elevated atmospheric deposition of Hg. Together with the temperature, grain size of sediments, total organic carbon (TOC), the pH and salinity of water, 40 components of parent and substituted polycyclic aromatic hydrocarbons (PAHs) were first introduced to quantify the driver of sedimentary THg based on generalized additive model. Results showed that anthropogenic emissions, including three PAHs components (31%) and TOC (63%), accounted for 94% of sedimentary THg variations. The aquatic environmental impacts accounted for 5% of sedimentary THg variations. The geo-accumulation index of THg indicated moderate to heavy accumulation in the urban region. This study demonstrates that homologous pollutants such as PAHs can be used to trace sources and variations of Hg pollution, supporting their co-regulation as international conventions regulate pollutants.

Association between the triglyceride glucose index and length of hospital stay in patients with heart failure and type 2 diabetes in the intensive care unit: a retrospective cohort study.

Background: The coexistence of heart failure and diabetes is prevalent, particularly in Intensive Care Units (ICU). However, the relationship between the triglyceride-glucose (TyG) index, heart failure, diabetes, and the length of hospital stay (LHS) in patients with cerebrovascular disease in the ICU remains uncertain. This study aims to investigate the association between the TyG index and LHS in patients with heart failure and diabetes. Methods: This retrospective study utilized the Medical Information Mart for Intensive Care (MIMIC)-IV database to analyze patients with diabetes and heart failure. Participants were categorized into quartiles based on the TyG index, and the primary outcome was LHS. The association between the TyG index at ICU admission and LHS was examined through multivariable logistic regression models, restricted cubic spline regression, and subgroup analysis. Results: The study included 635 patients with concurrent diabetes and heart failure. The fully adjusted model demonstrated a positive association between the TyG index and LHS. As a tertile variable (Q2 and Q3 vs Q1), the beta (ß) values were 0.88 and 2.04, with a 95% confidence interval (95%CI) of -0.68 to 2.44 and 0.33 to 3.74, respectively. As a continuous variable, per 1 unit increment, the ß (95% CI) was 1.13 (0.18 to 2.08). The TyG index's relationship with LHS showed linearity (non-linear p = 0.751). Stratified analyses further confirmed the robustness of this correlation. Conclusion: The TyG index exhibited a linearly positive association with the LHS in patients with both heart failure and diabetes. Nevertheless, prospective, randomized, controlled studies are imperative to substantiate and validate the findings presented in this investigation.

Nonlinear time effects of vegetation response to climate change: Evidence from Qilian Mountain National Park in China.

Vegetation responses to climate change are typically nonlinear with varied time effects, yet current research lacks comprehensiveness and precise definitions, hindering a deeper understanding of the underlying mechanisms. This study focuses on the mountain-type Qilian Mountain National Park (QMNP), investigating the characteristics and patterns of these nonlinear time effects using a generalized additive model (GAM) based on MODIS-NDVI, growing season temperature, and precipitation data. The results show that 1) The time effects of climate change on vegetation exhibit significant spatial variations, differing across vegetation types and topographic conditions. Accounting for optimal time effects can increase the explanatory power of climate on vegetation change by 6.8 %. Precipitation responses are mainly characterized by time-lag and time-accumulation effects, notably in meadows and steppes, while temperature responses are largely cumulative, especially in steppes. The altitude and slope significantly influence the pattern of vegetation response to climate, particularly in areas with high altitudes and steep slopes. 2) There is a significant nonlinear relationship between vegetation growth and both precipitation and temperature, with the nonlinear relationship between precipitation and vegetation being stronger than that with temperature, particularly in the western and central regions of the park. Different vegetation types exhibit significant variations in their response to climate change, with deserts and steppes being more sensitive to precipitation. 3) Precipitation is the primary driver of vegetation change in the QMNP, particularly for high-elevation vegetation and herbaceous vegetation. The complex temporal patterns of vegetation response to climate change in the QMNP not only deepen the understanding of the intricate relationship between regional vegetation and climate variability but also provide a methodological reference for global studies on vegetation responses to climate change.

Interactions Between Serotonin Transporter Gene and Adverse Childhood Experience in a Generalized Additive Model: A Pilot Study.

BACKGROUND: While most people experience potentially traumatic events (PTEs), including Adverse Childhood Experiences (ACEs), the stress reactions to PTEs on mental health outcomes are highly heterogeneous. Resilience is influenced by a complex biopsychosocial ecological system, including gene serotonin transporter-linked promoter region or 5-HTTLPR /rs25531 by ACEs interactions. AIMS: This pilot study investigated the gene-by-environment interactions on mental health outcomes in adults enrolled in a health care profession program using a generalized additive model (GAM). METHODS: Seventy health care college students (mean age = 27.4 years, 67.1% women) participated in this cross-sectional study. Saliva samples were collected from students to analyze 5-HTTLPR/rs25531. Participants completed the ACE Questionnaire and the Mental Health Inventory. GAMs with different interaction terms were built adjusting for age, gender, and race. The value of the effective degree of freedom (EDF) quantifies the curvature of the relationship. RESULTS: Among participants with the long allele of 5-HTTLPR/rs25531, a linear pattern was found between the total ACE score and mental health outcomes (EDF = 1). Conversely, among participants with the short allele, EDF was approximately 2, indicating a curved association suggesting that mental health worsens in individuals exposed to up to four types of ACEs. CONCLUSIONS: The impact of up to four ACEs on mental health was stronger among individuals with the short allele of 5-HTTLPR/rs25531 than those with the long allele. Although this study does not claim to provide a definite approach to analyzing gene-by-environment interactions, we offer a different perspective to explore the relationship.

Effects of ambient PM2.5 on non-accidental death: a time-series study in Shenzhen, China during 2014-2019.

This study aims to investigate the impact of PM2.5 on non-accidental death of residents. The single-pollutant model revealed that the influence of PM2.5 on non-accidental death was significant at lag0 and lag4-6, and was greatest at lag5. A 10 µg/m3 increase in PM2.5 was related with a 1.31% increase in non-accidental deaths. The connection between PM2.5 and non-accidental death was stronger in femalesthan males, in people aged ≥ 65 years than people aged < 65 years, and in people below high school education than people with high school education or above. Two-pollutant model revealed that the influence of PM2.5 on non-accidental death was essentially unchanged when CO, SO2, and O3 were included and reduced when NO2 was included. The multiple-pollutant model showed that the effect of ambient PM2.5 on non-accidental death was reduced. An increase in PM2.5 concentrations may cause an increase in non-accidental death.

[Impact of Air Pollutants on Outpatient Visits for Pediatric Atopic Dermatitis in Lanzhou].

Objective To analyze the correlation between air pollutants and pediatric atopic dermatitis outpatient visits in Lanzhou,and provide scientific insights for the life guidance of the affected children and disease prevention by relevant departments.Methods A generalized additive model was employed to analyze the effects and lagged effects of air pollutants on pediatric atopic dermatitis outpatient visits in Lanzhou while controlling for confounding factors such as long-term trends,holiday effects,day of the week effects and meteorological factors.Results The effects of NO2,PM2.5,PM10,and SO2 on pediatric atopic dermatitis outpatient visits were most significant on the current day(Lag0),but were not statistically significant (all P>0.05);CO also had the most significant effect on Lag0,and for every 10 µg/m3 increase in its concentration,the excess risk (ER) for pediatric atopic dermatitis outpatient visits was 0.05% (95%CI=0-0.10%,P=0.049);and O3 exhibited the most significant effect on day 7 of the cumulative lag (Lag07),with a statistically significant increase in the ER for each 10 µg/m3 increase in its concentration of 7.40% (95%CI=5.31%-9.53%,P<0.001) for pediatric atopic dermatitis outpatient visits.Age stratification showed that children aged 0-3 years with atopic dermatitis were the most sensitive to CO,with an increased ER of 0.09% (95%CI=0.04%-0.15%,P<0.001) for every 10 µg/m3 increase in concentration,and children aged 7-14 years with atopic dermatitis were the most sensitive to O3,with an increased ER of 8.26% (95%CI=4.99%-11.64%,P<0.001) for every 10 µg/m3 increase in concentration.Seasonal stratification showed that CO exerted a stronger effect on pediatric atopic dermatitis outpatient visits in summer and fall,with ER values of 0.45% and 0.16% (both P<0.001),respectively,while O3 had a significant effect on outpatient visits in winter,with an ER value of 20.48% (P<0.001).Conclusion Elevated daily average concentrations of air pollutants CO and O3 in Lanzhou were positively correlated with the number of outpatient visits for atopic dermatitis in children,with significant seasonal effects and age-stratified sensitivities.

Relationship between glycated hemoglobin levels and three-month outcomes in acute ischemic stroke patients with or without diabetes: a prospective Korean cohort study.

OBJECTIVE: In patients experiencing acute ischemic stroke, there is ongoing debate surrounding the connection between chronic hyperglycemic status and their initial clinical outcomes. Our objective was to examine the connection between glycated hemoglobin (HbA1c) levels and adverse clinical outcomes at both 3-months adverse clinical outcomes in individuals with acute ischemic stroke (AIS) with and without diabetes. METHODS: The present prospective cohort study involved 896 AIS patients without diabetes and 628 with diabetes treated at a South Korean hospital from January 2010 to December 2016. The target independent variable is HbA1c. The outcome variable is a modified Rankin scale score ≥ 3. A binary logistic regression model was applied to assess the connection between HbA1c levels and 3-month poor clinical outcomes in AIS patients with and without diabetes. Additionally, a generalized additive model and smoothed curve fitting were utilized to explore potential nonlinear associations between HbA1c levels and 3-month adverse clinical outcomes in AIS patients with and without diabetes. RESULTS: The binary logistic regression model could not identify any statistically significant connection between HbA1c and 3-month adverse clinical outcomes in AIS patients, both those with and without diabetes, after correcting for various factors. However, a nonlinear relationship emerged between HbA1c and 3-month adverse clinical outcomes in AIS patients with diabetes. The inflection point for HbA1c was determined to be 6.1%. For HbA1c values ≤ 6.1%, an inverse association was observed between HbA1c and 3-month adverse clinical outcomes in diabetic AIS patients, and each 1% increase in HbA1c in AIS patients with DM was associated with an 87% reduction in 3-month adverse clinical outcomes (OR = 0.13, 95% CI: 0.02-0.81). Conversely, when HbA1c exceeded 6.1%, a positive association between HbA1c and 3-month adverse clinical outcomes became apparent in diabetic AIS patients, and each 1% increase in HbA1c in AIS patients with DM was associated with a 23% increase in 3-month adverse clinical outcomes (OR = 1.23, 95%CI: 1.03-1.47). However, it's important to note that no significant linear or nonlinear relationships were observed between HbA1c levels and 3-month adverse clinical outcomes in AIS patients without diabetes. CONCLUSION: Our findings suggest a nonlinear connection and threshold effect between HbA1c and 3-month adverse clinical outcomes in AIS patients with diabetes. AIS patients with diabetes had a lower risk of 3-month adverse clinical outcomes when their HbA1c control was close to 6.1%. Our findings may aid treatment decision-making and potentially guide interventions to optimize glycemic control in AIS patients.

U-shaped association between serum calcium and in-hospital mortality in patients with congestive heart failure.

AIMS: Serum calcium level is widely used for evaluating disease severity, but its impact on clinical outcomes in patients with congestive heart failure (CHF) remains poorly understood. The aim of this study is to investigate the relationship between serum calcium levels and in-hospital mortality in CHF patients. METHODS AND RESULTS: We conducted a retrospective analysis utilizing clinical data from the Medical Information Mart for Intensive Care database, encompassing a cohort of 15 983 CHF patients. This cohort was stratified based on their serum calcium levels, with the primary objective being the determination of in-hospital mortality. To assess the impact of admission serum calcium levels on in-hospital mortality, we employed various statistical methodologies, including multivariable logistic regression models, a generalized additive model, a two-piecewise linear regression model, and subgroup analysis. Comparative analysis of the reference group (Q3) revealed increased in-hospital mortality in the first quintile (Q1, the group with the lowest blood calcium level) and the fifth quintile (Q5, the group with the highest blood calcium level), with fully adjusted odds ratios of 1.38 [95% confidence interval (CI): 1.13-1.68, P = 0.002] and 1.23 (95% CI: 1.01-1.5, P = 0.038), respectively. A U-shaped relationship was observed between serum calcium levels and in-hospital mortality, with the lowest risk occurring at a threshold of 8.35 mg/dL. The effect sizes and corresponding CIs below and above this threshold were 0.782 (95% CI: 0.667-0.915, P = 0.0023) and 1.147 (95% CI: 1.034-1.273, P = 0.0094), respectively. Stratified analyses confirmed the robustness of this correlation. CONCLUSIONS: Our study identifies a U-shaped association between serum calcium levels and in-hospital mortality in CHF patients, with a notable inflection point at 8.35 mg/dL. Further investigation through prospective, randomized, and controlled studies is warranted to validate the findings presented in this study.

Mapping the spatial disparities of HIV prevalence in Ethiopian zones using the generalized additive model.

HIV is a worldwide social and health pandemic that poses a significant problem. This study contributes to the 2030 global agenda of reducing HIV prevalence. The study analyzed HIV prevalence using the 2016 Ethiopian Demographic and Health Survey data. The study included men aged 15-54 years and women aged 15-49 years who responded to questions about HIV tests. A generalized geo-additive model (GAM) was fitted to HIV data using nonparametric smooth terms for geolocations. Two smoothing techniques were used in GAMs to evaluate spatial disparities and the probable effects of variables on HIV risk. There were certain areas in Ethiopia that were identified as hot spot zones for HIV, including Nuer and Agnuak in Gambella, West Wollega and Illubabor in Oromia, Benchi Maji and Shaka in SNNPR, Awsi, Fantana, Kilbet, and Gabi in the Afar region, Shinilie of the Somalia region, North and South Wollo, Oromia special zones of the Amhara region, Central Ethiopia, and Addis Ababa city. On the other hand, the eastern parts of Ethiopia, particularly most zones in the Somalia region, were identified as cold spot zones with the lowest HIV odds ratio. The odds of HIV+ were higher for those who reside in rural areas than in urban areas. Furthermore, people who have STIs, who used contraceptive methods, and who learned at the secondary level of education were more likely to be infected with HIV. After adjusting for confounding variables, the results indicated that there are substantially significant spatial variations in HIV prevalence across Ethiopian zones. These results provide essential information to strategically target geographic areas to allocate resources and policy interventions at zonal level administrations.

U-Shaped Association between Serum Chloride Levels and In-Hospital Mortality in Patients with Congestive Heart Failure in Intensive Care Units.

Serum chloride level has clinical significance in the prognosis of heart failure. Little is known regarding the association between serum chloride levels and in-hospital mortality in patients with heart failure.This retrospective study used clinical data obtained from the Medical Information Mart for Intensive Care Database. The study cohort comprised patients who were categorized on the basis of their serum chloride levels, and the primary endpoint was in-hospital mortality. To assess the impact of serum chloride levels at the time of intensive care unit admission on in-hospital mortality, we used various statistical approaches, including multivariable logistic regression models, a generalized additive model, and a two-piecewise linear regression model. In addition, subgroup analysis was conducted to examine the robustness of the main findings.This study comprised 15,983 participants. When compared with the reference group (Q5), the groups with the highest (Q7) and lowest (Q1) blood chloride levels exhibited increased in-hospital mortality, with fully adjusted odds ratios (ORs) of 1.36 [95% confidence interval (CI): 1.08-1.71] and 1.25 (95% CI: 1-1.56), respectively. A U-shaped relationship was observed between blood chloride levels and in-hospital mortality, with the lowest risk observed at a threshold of 105.017 mmol/L. The effect sizes and corresponding CIs below and above the threshold were 0.969 (95% CI: 0.957-0.982) and 1.039 (95% CI: 1.002-1.076), respectively. Stratified analyses demonstrated the robustness of this correlation.The relationship between serum chloride levels and in-hospital mortality in patients with heart failure was U-shaped, with an inflection point of 105.017 mmol/L.

Estuarine environmental flow assessment based on the flow-ecological health index relation model: a case study in Yangtze River Estuary, China.

Environmental flow (e-flow) is the water demand of one given ecosystem, which can become the flow regulation target for protection and restoration of river or estuarine ecosystems. In this study, an e-flow assessment based on the flow-ecological health index (EHI) relation model was conducted to improve ecosystem health of the Yangtze River Estuary (YRE). Monitoring data of hydrology, biology, and water environment in the last decades were used for the model establishment. For the description of the YRE ecosystem, an EHI system was developed by cumulative frequency distribution curves and adaption of national standards. After preprocessing original flow values into proportional flow values, the generalized additive model and Monte Carlo random sampling were used for the establishment of the flow-EHI relation model. From the model calculation, the e-flow assessment results were that, in proportional flow values, the suitable flow range was 1.05-1.35, and the optimum flow range was 1.15-1.25 (flows in Yangtze River Datong Station). For flow regulation in two crucial periods, flows of 42,630-65,545 m3/s or over 14,675 m3/s are needed for the suitable flow of YRE in summer (June-August) or January, respectively. An adaptive management framework of ecological health-based estuarine e-flow assessment for YRE was contrived due to the limitation of current established model when facing the extreme drought in summer, 2022. The methodology and framework in this study are expected to provide valuable management and data support for the sustainable development of estuarine ecosystems and to bring inspiration for further studies at even continental or global levels.

Development of a community severance index for urban areas in the United States: A case study in New York City.

BACKGROUND AND AIMS: Traffic-related exposures, such as air pollution and noise, have a detrimental impact on human health, especially in urban areas. However, there remains a critical research and knowledge gap in understanding the impact of community severance, a measure of the physical separation imposed by road infrastructure and motorized road traffic, limiting access to goods, services, or social connections, breaking down the social fabric and potentially also adversely impacting health. We aimed to robustly quantify a community severance metric in urban settings exemplified by its characterization in New York City (NYC). METHODS: We used geospatial location data and dimensionality reduction techniques to capture NYC community severance variation. We employed principal component pursuit, a pattern recognition algorithm, combined with factor analysis as a novel method to estimate the Community Severance Index. We used public data for the year 2019 at census block group (CBG) level on road infrastructure, road traffic activity, and pedestrian infrastructure. As a demonstrative application of the Community Severance Index, we investigated the association between community severance and traffic collisions, as a proxy for road safety, in 2019 in NYC at CBG level. RESULTS: Our data revealed one multidimensional factor related to community severance explaining 74% of the data variation. In adjusted analyses, traffic collisions in general, and specifically those involving pedestrians or cyclists, were nonlinearly associated with an increasing level of Community Severance Index in NYC. CONCLUSION: We developed a high spatial-resolution Community Severance Index for NYC using data available nationwide, making it feasible for replication in other cities across the United States. Our findings suggest that increases in the Community Severance Index across CBG may be linked to increases in traffic collisions in NYC. The Community Severance Index, which provides a novel traffic-related exposure, may be used to inform equitable urban policies that mitigate health risks and enhance well-being.