Tag: pubmed

Sci Rep. 2025 Sep 30;15(1):34077. doi: 10.1038/s41598-025-14375-4.

ABSTRACT

Sustainable construction materials are one of the major solutions to combat climate change and reduce its impact on the global economy. This research study aimed to optimise the quaternary blend of cement, fly ash, pumice and rice husk ash using factorial experiments and elastic modulus tests, followed by their empirical modelling. Keeping the quantities of fly ash and rice husk ash per unit volume of concrete constant, it was observed that the compressive strength decreased with the increase in quantity of pumice per unit volume of concrete due to its lower specific surface area. Similarly, the highest value of elastic modulus was observed for the sample containing 10% fly ash, 15% rice husk ash, and 5% pumice, as it was approximately 14.2% and 13.9% higher than the control group at 28 days and 120 days, respectively. Novel equations for estimating elastic modulus and flexural strength as a function of compressive strength were developed and found to be statistically reliable. Lastly, in comparison to Random Forest model, the Extreme Gradient Boosting model successfully predicted the compressive strength of quaternary blended concrete as evident from its higher R² values of 0.999 and 0.921 and lower RMSE values of 0.419 and 4.96 during the training and testing phases, respectively, and the results were confirmed using the paired t-test.

PMID:41028118 | DOI:10.1038/s41598-025-14375-4

Sci Rep. 2025 Sep 30;15(1):33972. doi: 10.1038/s41598-025-11076-w.

ABSTRACT

This study investigated the effectiveness of a resistance and aerobic training model among 71 middle-aged participants aged 30-60 (mean age 44.27 ± 8.67 years; mean BMI 27.94 ± 3.92 kg/m²) with obesity, comprising 36 males and 35 females (male/female ratio ≈ 1.03:1). Participants were categorized into four groups based on their self-reported training regimens: dietary-only (Group C), aerobic fat oxidation (Group F), high-intensity interval training (Group H), and resistance training (Group R). Subjects followed their specialized routines through online and offline sources for at least 12 weeks. Groups F, H, and R demonstrated statistically lower body weight as well as waist-to-hip ratio and body fat percent levels, when assessed against Group C (P < 0.01). The combination of resistance training with specific benefits produced larger reductions in waist-to-hip ratio, together with android fat mass, primarily observed among male participants (P < 0.01). The participants in Group H demonstrated the greatest decrease in body fat percentage among female subjects (P < 0.01), even though Group R participants achieved beneficial results, although their adherence level was less than ideal. Participants from all experimental groups maintained similar levels of muscle mass. The hybrid online and offline approach effectively enhanced adherence and engagement, demonstrating its scalability and potential for managing obesity.

PMID:41028117 | DOI:10.1038/s41598-025-11076-w

Sci Rep. 2025 Sep 30;15(1):33923. doi: 10.1038/s41598-025-08813-6.

ABSTRACT

The current manuscript deals with the analytical study of the generalized Benjamin-Ono (BO) equation. The underlying model has numerous applications in scientific fields like wave propagation effect and study of the plasma dynamics and complicated modeling of physical systems. The Jacobi elliptic function (JEF) expansion method is employed for employed for the solitary wave and soliton solutions for the underlying model. This technique provides dark, bright and dark periodic solitary wave solutions. Different solutions are chosen to draw their physical behavior. 2D, 3D and their corresponding contours are drawn and their physical behavior is explained in the context of real-life application. The stability analysis, chaotic behavior, sensitivity and bifurcation analysis is derived to analyze its various dynamics and simulations are plotted for various choices of the parameters. These results will create an impact in the existing literature.

PMID:41028092 | DOI:10.1038/s41598-025-08813-6

Sci Rep. 2025 Sep 30;15(1):34027. doi: 10.1038/s41598-025-12847-1.

ABSTRACT

The COVID-19 pandemic has caused over 7 million deaths worldwide, with age, underlying conditions, and immunosuppression increasing the incidence of severe outcomes. Despite vaccination, immunocompromised (IC) individuals show lower vaccine response, probably leading to more breakthrough infections. The objective of our study was to evaluate the overall occurrence of intensive care admission and/or death during hospitalisation, stratified by COVID-19 severity and immunological status (IC vs. non-IC individuals). Our study used a nationwide database to compare COVID-19 hospitalisations and outcomes in IC versus non-immunocompromised individuals (non-IC). This is a longitudinal cohort study analysed de-identified COVID-19 data from Brazil’s DATASUS system (02 March 2020-31 December 2023). The study included 361,898 subjects, identifying 7484 (2.07%) IC individuals. IC individuals showed higher rates of chronic liver, neurological, and lung diseases, while non-IC individuals had higher obesity rates. Intensive care unit (ICU) admissions (42.6% vs. 38.5%) and mortality (51.1% vs. 35.9%) were greater in IC compared to non-IC individuals. Therefore, IC individuals consistently experienced more ICU admissions and higher mortality across the COVID-19 pandemic years (odds ratios rising from 1.68 in 2020 to 2.39 in 2023), influenced by the prevalence of SARS-Cov-2 variants. Our study shows higher morbidity and mortality in IC individuals during the COVID-19 pandemic, underscoring the need for targeted strategies like early interventions, reinforcing the need for sustained surveillance, targeted vaccination strategies, and prioritised care.

PMID:41028085 | DOI:10.1038/s41598-025-12847-1

Sci Rep. 2025 Sep 30;15(1):33928. doi: 10.1038/s41598-025-09801-6.

ABSTRACT

Cycling safety at intersections is a growing concern as both cycling activity and motor vehicle traffic continue to rise. Intersections pose heightened risks for cyclists due to complex traffic patterns, ambiguous right-of-way rules, and insufficient signaling, often leading to collisions. This study investigates how intersection control types and operational characteristics influence driver failure-to-yield behavior toward cyclists. Using ten years of Michigan crash data involving single motor vehicle-cyclist collisions, we apply a Mixed Logit Model to account for unobserved heterogeneity in driver behavior. The analysis focuses on three types of intersection control: traffic signals, stop/yield signs, and uncontrolled intersections, examining their impact on various driver-cyclist interaction scenarios. Key findings indicate that driver age, day of the week, vehicle type, and speed limit consistently affect yielding behavior across all control types. Impairment due to alcohol or drugs significantly increases the likelihood of hazardous driver actions. Drivers are more prone to fail to yield in straight-ahead scenarios, though they are less likely to be deemed at fault in non-yield crashes. Intersection control effectiveness also varies by maneuver type; signalized intersections reduce failure rates in straight-travel scenarios, while stop/yield signs are more effective during left turns. This research addresses a critical gap by linking infrastructure features with driver yielding performance, offering evidence-based insights for improving intersection safety. The findings support targeted interventions in roadway design, driver education, and the integration of advanced technologies such as cyclist detection systems and vehicle-to-vehicle communication to enhance cyclist protection.

PMID:41028078 | DOI:10.1038/s41598-025-09801-6

Sci Rep. 2025 Sep 30;15(1):34101. doi: 10.1038/s41598-025-19539-w.

ABSTRACT

The prediction and management of water quality are critical to ensure Sustainable water resources, particularly in regions like Odisha, where rivers face increasing pollution from industrialization, agriculture, and urban expansion. The Brahmani River, located in the Odisha State, is the 2nd largest watershed in the province, by which its water quality is affected by natural and anthropogenic changes. In this research, water samples were gathered throughout the monsoon season for four years (2020-2024) from previously selected 7 sampling stations. Geographical Information System (GIS) techniques were used to find out the distribution of surface water quality on land use pattern. A particular focus is given to hybrid models that integrate multiple approaches to improve predictive accuracy and robustness. Therefore, the study was undertaken by incorporating Weighted Arithmetic (WA) Water Quality Index (WQI), Synthetic Pollution Index (SPI), Nemerow Pollution Index (NPI), Overall Index of Pollution (OIP), multivariate statistical method, namely Factor Analysis (FA) or Principal Component Analysis (PCA), and Multi-Criteria Decision-Making (MCDM) approaches like Evaluation based on Distance from Average Solution (EDAS). The goal of this investigation is to evaluate the water’s purity and whether it is Suitable for consumption. Fifteen physicochemical parameters were tested from 7 observation stations. Referring to the present research, the obtained order of anionic abundance was SO₄^2- > Cl^– > NO₃^– >F^– > PO₄^3–. However, the order of cationic abundance was Ca²⁺ > Mg²⁺ > Na⁺ > K⁺. The calculated WA-WQI values ranged between 49 and 72. Toxic heavy metals, nutrients, and microorganisms were the major pollutants influencing water quality, as stated by WA-WQI. In addition, the data was interpreted using pollution indices such as SPI (0.31-0.68), NPI (6-29.91), and OIP (0.45-4.40). By results, it is concluded that mainly 4 sites are unsuitable for drinking and irrigation purposes, due to long-term use of waste water, anthropogenic activities, over-extraction of Surface water and changes in land use pattern. Using the multivariate technique, the PCA method was useful to identify two latent pollution sources, that correctly assign 89% of the total variance in the dataset. During the first component, the major loadings on parameters: TDS, EC, alkalinity, Na⁺, Ca²⁺, Mg²⁺, K⁺, F^–, Cl^–, NO₃^–, and SO₄^2–. It indicates that locations were primarily Harmed by oxygen-consuming organic and Hazardous contamination. Furthermore, the EDAS score fluctuated between 0.01 and 0.97. The results revealed that Y-(1) mentioned high polluted water, followed by Y-(2) and Y-(7). This signifies the existence of dissolving biological material; nitrogen was the major pollutant, originating primarily from anthropogenic local contamination. Later on, the outcomes of water quality parameters on the different indexing methods were evaluated, and the obtained outcomes indicate that the highest mean effective weight value belongs to the TDS, EC, Cl^–, SO₄^2- and PO₄^3–, respectively. Notably, effective control of point source pollution and upper river ecological restoration should be done to improve the water quality and protect the reservoir. This research identifies key research gaps and proposes future directions for developing transparent, adaptive, and accurate models. The findings can also guide researchers and policymakers towards the development of smart water quality management systems that enhance decision-making and ecological sustainability.

PMID:41028073 | DOI:10.1038/s41598-025-19539-w

Sci Rep. 2025 Sep 30;15(1):33896. doi: 10.1038/s41598-025-08809-2.

ABSTRACT

This study investigates the efficacy of Astaxanthin and Quercetin as potential therapeutic agents for mitigating chloramphenicol-induced liver toxicity. Despite chloramphenicol’s broad-spectrum antibiotic properties, its clinical utility is hampered by hepatotoxic side effects. This research assesses the impact of chloramphenicol-induced mitochondrial toxicity, reactive oxygen species (ROS) production, and gene expression alterations in HepG2 liver cells. To enhance mitochondrial sensitivity, cells were cultured in galactose-containing media and exposed to chloramphenicol (up to 3000 µmol/L) for 48 h, with or without Astaxanthin (5-15 µM) or Quercetin (10-30 µM). Untreated and DMSO vehicle controls were included. Mitochondrial toxicity was evaluated using ATP content, ROS levels (ROS-Glo™ assay), and gene expression profiling. Expression of five mitochondrial-related genes SOD2, UCP2, NRF1, SURF1, and TFAM were analyzed due to their roles in oxidative stress, membrane potential regulation, biogenesis, and respiratory complex assembly. Antioxidant treatments resulted in significant reductions in ROS levels (p < 0.005) and restoration of mitochondrial gene expression patterns (p < 0.05, n = 3), alongside improved ATP retention. IC50 values and statistical comparisons were derived using GraphPad Prism with one-way ANOVA and appropriate post hoc tests. These findings suggest that Astaxanthin and Quercetin confer mitochondrial protection through modulation of oxidative stress and gene expression.

PMID:41028070 | DOI:10.1038/s41598-025-08809-2

JMIR Public Health Surveill. 2025 Sep 30;11:e72591. doi: 10.2196/72591.

ABSTRACT

BACKGROUND: Scalable and accurate screening tools are critical for public mental health strategies, especially in low- and middle-income countries (LMICs). While the Generalized Anxiety Disorder Scale (GAD-7) and Patient Health Questionnaire (PHQ-9) are widely used, their full application in large-scale programs can pose feasibility challenges. By contrast, shorter versions like GAD-2 and PHQ-2 reduce burdens but fail to capture symptom diversity.

OBJECTIVE: This study aimed to optimize screening for anxiety and depression severity using classification and regression tree (CART) models, identifying concise and high-performing decision rules based on the GAD-7 and PHQ-9 items, and to test their reproducibility in 5 independent datasets.

METHODS: A cross-sectional, nonprobabilistic study was conducted with 20,585 Brazilian adults from all 27 states and more than 3,000 cities, collected using digital outreach. Anxiety and depression symptoms were assessed using the GAD-7 and PHQ-9. CART models were trained and tested on bootstrapped samples (70% training, 30% testing), totaling 45,000 trees per scale. Each model used combinations of scale items and sociodemographic predictors. Robustness was evaluated via 10-fold cross-validation and evaluation across 3 hyperparameter configurations (minsplit and minbucket=500, 1000, 2000). Performance metrics included accuracy, sensitivity, specificity, precision, F1-score, and area under the curve (AUC).

RESULTS: The CART models produced concise, high-performing decision rules-using only 2 items for the GAD-7 and 3 for the PHQ-9. No sociodemographic variable appeared in the final classification paths. For GAD-7, the models achieved an accuracy of 86.1% for minimal or mild severity and 85.1% for severe cases, with both categories showing AUC values above 0.900. By contrast, the moderate severity class had lower performance, with accuracy around 51% and an AUC of 0.728. For PHQ-9, the models achieved 81.7% accuracy for minimal or mild cases and 78.8% for severe cases, with AUCs again exceeding 0.900 for the extreme classes; the moderate or moderately severe class showed 66.9% accuracy and an AUC of 0.776. The most frequently repeated rules included the following: “GAD2<2 and GAD4<2” for identifying minimal or mild anxiety and “GAD2≥2 and GAD4=3” for severe anxiety; for depression, “PHQ2<2and PHQ4<2” for minimal or mild cases and “PHQ2≥2 and PHQ8≥2” for severe cases. These rule-based models demonstrated stable performance across thousands of bootstrapped replications and showed reproducibility in 5 independent datasets through external validation.

CONCLUSIONS: CART models enabled simplified, symptom-specific pathways for stratifying anxiety and depression severity with high precision and minimal item burden. These rule-based shortcuts offer an efficient alternative to fixed short forms (eg, GAD-2, PHQ-2) by preserving symptom diversity and severity discrimination. The findings support and lay the groundwork for adaptive, cost-effective screening and intervention models, especially in resource-limited settings and LMICs.

PMID:41027019 | DOI:10.2196/72591

JMIR Mhealth Uhealth. 2025 Sep 30;13:e68824. doi: 10.2196/68824.

ABSTRACT

BACKGROUND: Engagement with digital mental health interventions is often measured as a summary-level variable and remains underresearched despite its importance for meaningful symptom change. This study deepens understanding of engagement in a digital eating disorder intervention, recovery record, by measuring engagement with unique components of the app, on 2 different devices (phone and watch), and at a summary level.

OBJECTIVE: This study described and modeled how individuals engaged with the app across a variety of measures of engagement and identified baseline predictors of engagement.

METHODS: Participants with current binge-eating behavior were recruited as part of the Binge Eating Genetics Initiative study to use a digital eating disorder intervention for 4 weeks. Demographic and severity of illness variables were captured in the baseline survey at enrollment, and engagement data were captured through both an iPhone and Apple Watch version of the intervention. Engagement was characterized by log type (urge, behavior, mood, or meal), device type (logs on phone or watch), and overall usage (total logs) and averaged each week for 4 weeks. Descriptives were tabulated for demographic and engagement variables, and multilevel growth models were conducted for each measure of engagement with baseline characteristics and time as predictors.

RESULTS: Participants (N=893) self-reported as primarily White (743/871, 85%), non-Hispanic (801/893, 90%), females (772/893, 87%) with a mean age of 29.6 (SD 7.4) years and mean current BMI of 32.5 (SD 9.8) kg/m2 and used the app for a mean of 24 days. Most logs were captured on phones (217,143/225,927; 96%), and mood logs were the most used app component (174,818/282,136; 62% of logs). All measures of engagement declined over time, as illustrated by the visualizations, but each measure of engagement illustrated unique participant trajectories over time. Time was a significant negative predictor in every multilevel model. Sex and ethnicity were also significant predictors across several measures of engagement, with female and Hispanic participants demonstrating greater engagement than male and non-Hispanic counterparts. Other baseline characteristics (age, current BMI, and binge episodes in the past 28 days) were significant predictors of 1 measure of engagement each.

CONCLUSIONS: This study highlighted that engagement is far more complex and nuanced than is typically described in research, and that specific components and mode of delivery may have unique engagement profiles and predictors. Future work would benefit from developing early engagement models informed by baseline characteristics to predict intervention outcomes, thereby tailoring digital eating disorder interventions at the individual level.

PMID:41027004 | DOI:10.2196/68824

J Holist Nurs. 2025 Sep 30:8980101251380316. doi: 10.1177/08980101251380316. Online ahead of print.

ABSTRACT

A trauma cluster occurs when two or more natural disasters are experienced simultaneously or consecutively within a short timeframe. With the predicted increase in the frequency and severity of such events, the risk of experiencing trauma clusters is expected to rise, posing significant threats to public health and safety. While the focus of disaster research traditionally centers on negative psychological outcomes, such experiences can also lead to positive effects such as post-traumatic growth and ease. As such, this study aimed to assess the levels of post-traumatic growth and ease, and their relationship, among Bahamians who experienced the trauma cluster of Hurricane Dorian and the coronavirus disease pandemic. Nearly 4 years after the trauma cluster event began, 208 participants completed an online survey including a sociodemographic form, the Post-traumatic Growth Inventory, and Ease Measure. Data were analyzed using descriptive statistics and Pearson’s correlation. Results revealed a broad range of individual post-traumatic growth and ease scores, with a significant positive correlation between overall post-traumatic growth and ease levels. The findings suggest that individuals can respond to adversity in positive ways across various aspects of life. The knowledge gained can inform holistic nursing interventions to support disaster survivors.

PMID:41026997 | DOI:10.1177/08980101251380316