We examined PM2.5 and Hazard Mapping System smoke plume satellite data at ∼600 United States (US) air monitoring stations to identify surface smoke on 14.0% of all May-September days for 2018-2023, with large influences in 2020 and 2021, due to California fires, and 2023, due to Canadian fires. Days with smoke have an average of 11 μg m-3 more PM2.5 and 8 ppb higher maximum daily 8 h average (MDA8) O3 concentrations than nonsmoke days, and they also account for 94% of all days that exceed the daily PM2.5 health standard (35 μg m-3) and 36% of all days that exceed the O3 health standard (70 ppb). To estimate the smoke contributions to the O3 MDA8, Generalized Additive Models (GAMs) were built for each site using the nonsmoke day data and up to 8 predictors. The mean and standard deviation of the residuals from the GAMs were 0 ± 6.1 ppb for the nonsmoke day data and 4.3 ± 7.9 ppb for the smoke day data, indicating a significant enhancement in the MDA8 O3 on smoke days. We found positive residuals on 72% of the smoke days and for these days, we calculate an average smoke contribution to the O3 MDA8 of 7.8 ± 6.0 ppb. Over the 6 year period, the percentage of exceedance days due to smoke in the continental US was 25% of all exceedance days, and the highest was in 2023 (38%). In 2023, the Central US experienced an unusually high number of exceedance days, 1522, with 52% of these impacted by smoke, while the Eastern US had fewer exceedance days, 288, with 78% of these impacted by smoke. Our results demonstrate the importance of wildland fires as contributors to exceedances of the health-based national air quality standards for PM2.5 and O3.

Since steroids are crucial for diagnosing endocrine disorders, the lack of research on factors that affect hormone levels makes interpreting the results difficult. Our study aims to assess the stability of the pre-analytical procedure and the impact of hormonal physiological fluctuations using real-world data. The datasets were created using 12,418 records from individuals whose steroid hormone measurements were taken in our laboratory between September 2019 and March 2024. 22 steroid hormones in plasma by a well-validated liquid chromatography tandem mass spectrometry method were measured. After normalization transformation, outlier removal, and z-score normalization, generalized additive models were constructed to evaluate preanalytic stability and age, sex, and sample time-dependent hormonal fluctuations. Most hormones exhibit significant variability with age, particularly steroid hormone precursors, sex hormones, and certain corticosteroids such as aldosterone. 18-hydroxycortisol, 18-oxocortisol. Sex hormones varied between males and females. Levels of certain hormones, including cortisol, cortisone, 11-deoxycortisol, 18-hydroxycortisol, 18-oxocortisol, corticosterone, aldosterone, estrone, testosterone, dihydrotestosterone, dehydroepiandrosterone sulfate, 11-ketotestosterone, and 11-hydroxytestosterone, fluctuated with sampling time. Moreover, levels of pregnenolone and progesterone decreased within 1 hour of sampling, with pregnenolone becoming unstable with storage time at 4 degrees after centrifugation, while other hormone levels remained relatively stable for a short period of time without or after centrifugation of the sample. This is the first instance real-world data has been used to assess the pre-analytic stability of plasma hormones and to evaluate the impact of physiological factors on steroid hormones.

BACKGROUND: The inconsistent relationship between chemical exposure and estimated glomerular filtration rate (eGFR) has been examined in only a few studies. We investigated the association between paraben exposure and indicators of renal function in a total of 361 individuals recruiting from a representative study.
METHODS: The levels of urinary parabens, including methylparaben (MeP), ethylparaben (EtP), propylparaben (PrP), and butylparaben (BuP), were measured using UPLC-MS/MS. The association between paraben exposure and indices of renal function was assessed using multiple logistic regression and Bayesian Kernel Machine Regression (BKMR).
RESULTS: The median levels of urinary parabens in the adult group were significantly higher than those in the minor group, that is, 397 vs. 148 ng/mL for MeP, 38.8 vs. 13.6 ng/mL for EtP, 117 vs. 57.7 ng/mL for PrP, and 6.61 vs. 2.79 ng/mL for BuP (all P < 0.001). In the adult group, multivariate regression models confirmed a positive association between the albumin-to-creatinine ratio and urinary MeP (β = 0.580) and a positive association of BUN (β = 0.061) and a negative association of eGFR (β = -0.051) with urinary EtP (all P < 0.001). In the adult group, compared with the lowest tertile group, the adjusted odds ratio in the third tertile (T3) of urinary EtP levels indicated a 3.08 times increased risk of eGFR abnormalities, followed by the second tertile (T2) with a 2.63 times increased risk. The generalized additive model (GAM) and BKMR models showed a non-linear correlation between urinary EtP levels and early CKD, as well as reduced eGFR. We observed a significant positive cumulative effect of urinary paraben on eGFR, and a significant positive single exposure effect of urinary EtP with eGFR abnormality.
CONCLUSIONS: We found a significant association between exposure to EtP and an increased risk of high BUN levels and decreased eGFR.

OBJECTIVE: Previous research shows conflicting views on the relationship between obesity and osteoporosis, partly due to variations in obesity classification and the nonlinear nature of these relationships. This study investigated the association between adiposity indices and osteoporosis, diagnosed using dual-energy X-ray absorptiometry (DXA), employing nonlinear models and offering optimal thresholds to prevent further bone mineral density decline.
METHODS: In 2019, a prospective study enrolled males over 50 years and postmenopausal women. Anthropometric measurements, blood biochemistry, and osteoporosis measured by DXA were collected. Associations between adiposity indices and osteoporosis were analyzed using a generalized additive model and segmented regression model.
RESULTS: The study included 872 women and 1321 men. Indices such as abdominal volume index (AVI), visceral adiposity index (VAI), waist circumference (WC), hip circumference, body mass index (BMI), waist-to-hip ratio, and waist-to-height ratio (WHtR) were inversely associated with osteoporosis. In women, the relationship between the risk of osteoporosis and the adiposity indices was U-shaped, with thresholds of WC = 94 cm, AVI = 17.67 cm2, BMI = 25.74 kg/m2, VAI = 4.29, and WHtR = 0.61, considering changes in bone mineral density. Conversely, men exhibited a linear patterns for the inverse association.
CONCLUSIONS: The impact of obesity and adiposity on osteoporosis varies significantly between women and men. In postmenopausal women, the relationship is nonlinear (U-shaped), with both very low and very high adiposity linked to higher osteoporosis risk. In men over 50, the relationship is linear, with higher adiposity associated with lower osteoporosis risk. The study suggests that maintaining specific levels of adiposity could help prevent osteoporosis in postmenopausal women.

UNASSIGNED: Research exploring the link between dietary riboflavin intake and cognitive decline in this demographic is limited. Our aim was to examine the association between riboflavin intake levels and cognitive decline.
UNASSIGNED: The National Health and Nutrition Examination Survey (NHANES) data from 2011 to 2014 were utilized in this cross-sectional analysis. The Consortium to Establish a Registry for Alzheimer\'s Disease test Word Learning delayed recall trial (DR), Digit Symbol Substitution Test (DSST), Animal Fluency Test(AFT) and Z test were used to evaluate cognitive performance. Multivariate logistic regression, restricted cubic spline and subgroup analysis were performed to evaluate the associations between riboflavin intake and cognitive decline.
UNASSIGNED: The study included a total of 2255 patients, with 47.9% being male. The incidence of cognitive decline was 23.8%. After adjusting for all selected covariates, we found that high riboflavin intake was associated with a lower risk of cognitive impairment in adults in the United States. When riboflavin intake was used as a Categorical variable, compared to those with the lowest intake, the odds ratio (OR) of individuals with the highest riboflavin intake for DR test, AFT test, DSST test and Z test were 0.73 (95% CI: 0.53~1), 0.68(95% CI: 0.49-0.96),0.53(95% CI: 0.37-0.77) and 0.56(95% CI: 0.39-0.8). The study also found an L-shaped association between riboflavin intake and cognitive decline, with an inflection point at approximately 2.984 mg/d.
UNASSIGNED: Our cross-sectional study in a nationwide sample of American old adults suggests that dietary riboflavin intake was negative associated with cognitive decline.

Plant phenology is an important indicator of the impact of climate change on ecosystems. We have continuously monitored vegetation phenology using near-surface remote sensing, i.e., the PhenoCam in a gully region of the Loess Plateau of China from March 2020 to November 2022. In each image, three regions of interest (ROIs) were selected to represent different types of vegetation (scrub, arbor, and grassland), and five vegetation indexes were calculated within each ROI. The results showed that the green chromatic coordinate (GCC), excess green index (ExG), and vegetation contrast index (VCI) all well-captured seasonal changes in vegetation greenness. The PhenoCam captured seasonal trajectories of different vegetation that reflect differences in vegetation growth. Such differences may be influenced by external abiotic environmental factors. We analyzed the nonlinear response of the GCC series to environmental variables with the generalized additive model (GAM). Our results suggested that soil temperature was an important driver affecting plant phenology in the Loess gully region, especially the scrub showed a significant nonlinear response to soil temperature change. Since in situ phenology monitoring experiments of the small-scale on the Loess Plateau are still relatively rare, our work provides a reference for further understanding of vegetation phenological variations and ecosystem functions on the Loess Plateau.

UNASSIGNED: According to study on the under-estimation of COVID-19 cases in African countries, the average daily case reporting rate was only 5.37% in the initial phase of the outbreak when there was little or no control measures. In this work, we aimed to identify the determinants of the case reporting and classify the African countries using the case reporting rates and the significant determinants.
UNASSIGNED: We used the COVID-19 daily case reporting rate estimated in the previous paper for 54 African countries as the response variable and 34 variables from demographics, socioeconomic, religion, education, and public health categories as the predictors. We adopted a generalized additive model with cubic spline for continuous predictors and linear relationship for categorical predictors to identify the significant covariates. In addition, we performed Hierarchical Clustering on Principal Components (HCPC) analysis on the reporting rates and significant continuous covariates of all countries.
UNASSIGNED: 21 covariates were identified as significantly associated with COVID-19 case detection: total population, urban population, median age, life expectancy, GDP, democracy index, corruption, voice accountability, social media, internet filtering, air transport, human development index, literacy, Islam population, number of physicians, number of nurses, global health security, malaria incidence, diabetes incidence, lower respiratory and cardiovascular diseases prevalence. HCPC resulted in three major clusters for the 54 African countries: northern, southern and central essentially, with the northern having the best early case detection, followed by the southern and the central.
UNASSIGNED: Overall, northern and southern Africa had better early COVID-19 case identification compared to the central. There are a number of demographics, socioeconomic, public health factors that exhibited significant association with the early case detection.

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.

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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.