Category: Statistics

Mol Psychiatry. 2025 Apr 4. doi: 10.1038/s41380-025-02954-w. Online ahead of print.

ABSTRACT

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discovered 22 novel gene-sleep duration interaction loci for blood pressure, mapped to 23 genes. Investigating these genes’ functional implications shed light on neurological, thyroidal, bone metabolism, and hematopoietic pathways that necessitate future investigation for blood pressure management that caters to sleep health lifestyle. Non-overlap between short sleep (12) and long sleep (10) interactions underscores the plausible nature of distinct influences of both sleep duration extremes in cardiovascular health. Several of our loci are specific towards a particular population background or sex, emphasizing the importance of addressing heterogeneity entangled in gene-environment interactions, when considering precision medicine design approaches for blood pressure management.

PMID:40181193 | DOI:10.1038/s41380-025-02954-w

Sci Rep. 2025 Apr 3;15(1):11493. doi: 10.1038/s41598-025-96343-6.

ABSTRACT

This research investigates the compressive strength behavior of basalt fiber-reinforced concrete (BFRC) using machine learning models to optimize predictions and enhance its practical applications. The study incorporates various modeling techniques, including Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, and Random Forest (RF), to evaluate their predictive capabilities. Basalt Fiber Reinforced Concrete (BFRC) is a composite material that incorporates basalt fibers into traditional concrete to enhance its mechanical and durability properties. The use of basalt fibers, derived from natural volcanic rocks, aligns with sustainability goals due to their eco-friendliness, cost-effectiveness, and high performance. BFRC combines structural excellence with sustainability, making it an ideal material for modern construction practices. Its ability to enhance performance, reduce environmental impact, and ensure long-term durability positions it as a pivotal solution for sustainable infrastructure development. The developed models were used to predict compressive strength of basalt fiber concrete (Cs_bf) using the concrete mixture contents, age, and fiber dimensions. All the developed models were created using “Orange Data Mining” software version 3.36. A total of three hundred and nine (309) records were collected from literature for compressive strength for different mixing ratios of basalt fiber concrete with concrete at different ages. Each record contains the following data: C-Cement content (Kg/m³), FA-Fly ash content (Kg/m³), W-Water content (Kg/m³), SP-Super-plasticizer content (Kg/m³), CAg-Coarse aggregates content (Kg/m³), FAg-Fine aggregates content (Kg/m³), Age-The concrete age at testing (days), L_b-length of basalt fibers (mm), d_bf-Diameter of basalt fibers (µm), V_bf-Volume content of basalt fibers (%) and Cs_bf-Compressive strength of basalt fibre concrete (MPa). The collected records were divided into training set (249 records≈80%) and validation set (60 records≈ 20%). At the end of the process, it can be shown that the present research work outclassed other ML techniques applied in the previous research paper, which reported the utilization of the same size of data entries and basalt reinforced concrete constituents. Taylor chart for measured compressive strength of basalt fiber reinforced concrete predicted with ANN, KNN, SVM, Tree and RF is presented for comparing the performance of predictive models by illustrating three key statistical measures simultaneously: the correlation coefficient (R), the normalized standard deviation (σ), and the root-mean-square error (RMSE). Finally, it can be deduced that after considering the performance indices of the selected ensemble and classification models utilized in this present research paper, all the developed modes have almost the same excellent level of accuracy 95%, but ANN, KNN, and SVR produced R2 of 0.98 each with KNN producing MAE of 1.4 MPa, and MSE of 2.5 MPa to outperform ANN and SVR which produced MAE of 1.55 MPa/MSE of 4.1 MPa and MAE of 1.6 MPa/MSE of 3.85 MPa, respectively. Three techniques were used to estimate the impact of each input on the compressive strength, namely correlation matrix, sensitivity analysis and relative importance chart.

PMID:40181176 | DOI:10.1038/s41598-025-96343-6

Sci Rep. 2025 Apr 3;15(1):11458. doi: 10.1038/s41598-025-96288-w.

ABSTRACT

One of the purposes of tissue engineering is to offer therapeutic alternatives to treat various esophagus-related diseases. To develop viable esophageal replacements that are both mechanically and biologically compatible and to assess the impact of pharmacological treatments on esophageal tissue at the macro- and micro-structural levels, it is crucial to understand the biomechanical properties of the esophagus. In this study, we analyzed esophageal tissue samples from nine newborn lambs. Subjects were randomly separated into a control group (n = 5) and a melatonin-treated group (n = 4). The passive mechanical response of the esophagus was studied by performing in-vitro uniaxial tensile tests along longitudinal and circumferential directions. Samples were classified into three types: internal tissue (mucosa and submucosa layers), external tissue (external muscular layer), and integrated tissue (comprising all layers). Uniaxial stress versus stretch curves of each classification were used to determine mechanical properties that were statistically analyzed. Moreover, average experimental results were used to calibrate an anisotropic hyperelastic model. Stress-stretch curves from uniaxial tests showed a highly anisotropic behavior, with a higher stiffness along the longitudinal direction and internal tissue exhibiting the highest stiffness. To contrast the results obtained from mechanical testing, histological analysis of esophagus samples was carried out. Microstructural components were quantified and morphological measurements of the main zones were performed. No significant differences were found at the macro- and microstructural levels of the tissue, indicating that the supply of low doses of melatonin does not alter the biomechanical properties of the esophagus.

PMID:40181158 | DOI:10.1038/s41598-025-96288-w

Sci Rep. 2025 Apr 3;15(1):11456. doi: 10.1038/s41598-025-96682-4.

ABSTRACT

Preterm birth is a global health issue associated with neonatal death and morbidity. However, current methods of predicting preterm birth are insufficient to accurately screen for risk. This study aimed to assess the potential of site-specific N-glycosylation of cervicovaginal fluid (CVF) proteins as predictive biomarkers of preterm birth in a case-control study. Statistical analysis used Student’s t-tests, ROC curve and logistic regression adjusted age and BMI. Using N-glycoproteomic analysis of the CVF, we identified 862 N-glycoproteins in CVF samples form 20 pregnancies and 6595 N-linked glycopeptides used a false discovery rate of less than 1%. Of 173 upregulated glycan in preterm group, we found low levels of fucosylation and high levels of sialylation in preterm birth (p < 0.05). Then we found that three poly-sialylated glycans had a high predictive value (AUC = 0.802, p < 0.017), which were expressed in all samples. In addition, the glycan model with clinical markers performed better. The results indicate that poly-sialylated glycans in CVF have potential value as novel clinical markers for predicting preterm birth during pregnancy. This study suggests strategies for developing new predictive biomarkers using cervicovaginal glycans to detect preterm birth in advance.

PMID:40181148 | DOI:10.1038/s41598-025-96682-4

Sci Rep. 2025 Apr 3;15(1):11517. doi: 10.1038/s41598-025-96592-5.

ABSTRACT

This study evaluated the prognostic value of the pan-immune-inflammation value (PIV) combined with the albumin-to-globulin ratio (AGR) for postoperative survival in colorectal cancer (CRC) patients and developed a nomogram for survival prediction. A total of 650 CRC patients who underwent radical surgery were included, with data from one institution used as the training set. The optimal cut-off values for PIV (426.8) and AGR (1.4) were determined using maximally selected rank statistics. Kaplan-Meier analysis showed that patients in the low-PIV group had significantly better 5-year overall survival (OS) compared to the high-PIV group, while those in the high-AGR group had better 5-year OS than those in the low-AGR group. Multivariate analysis identified age, N stage, degree of differentiation, PIV, and AGR as independent prognostic factors for OS. A nomogram for OS was developed and validated, demonstrating robust predictive performance. This study highlights the value of PIV and AGR as reliable indicators for predicting OS in CRC patients, with high PIV and low AGR associated with worse prognosis. Timely interventions may improve patient outcomes.

PMID:40181140 | DOI:10.1038/s41598-025-96592-5

Sci Rep. 2025 Apr 3;15(1):11503. doi: 10.1038/s41598-025-96394-9.

ABSTRACT

Analyzing the trend and identifying the factors influencing the utilization of modern contraceptives is essential for designing effective measures to improve reproductive health and economic development to ensure universal access to family planning services. This study aimed to investigate the trends in modern contraceptive use among women of reproductive age in Ethiopia and the factors influencing these trends. Data from the 2014 to 2019 Performance Monitoring and Accountability/Action Survey datasets were analyzed, with 4422 women in 2014, 5113 in 2015, 5071 in 2016, 4927 in 2017, 4981 in 2018, and 6117 in 2019 included. Data analysis was conducted using Stata version 16.0 statistical software. Given the sample disproportionality, survey design considerations were taken into account by applying probability weights. The sample weight was utilized with weighting factors provided in the PMA data to address the complex survey design. The ‘svy’ Stata command was applied to consider the clustering effect before descriptive statistical analysis. The trend was examined based on selected characteristics, with the primary statistical parameter being the trend of modern contraceptive utilization. The trends of modern contraceptive utilization for each year from 2014 to 2019 were calculated. Then, logit-based decomposition analysis was used to identify the factors driving changes in modern contraceptive utilization. Statistical significance was determined at a P value of less than 0.05, and results were presented as decomposition coefficients and percentages. Additionally, we conducted data smoothing to predict the future trend of modern contraceptive utilization and did not visually observe a distinct trend. We explored different time-series models such as linear, quadratic, exponential, and exponential smoothing. The results showed that the exponential smoothing trend model provided the most accurate fit, with the lowest standard error of estimate, sum square error, and highest adjusted R² value. The modern contraceptive utilization trend increased from 32.5% in 2014 to 37% in 2019, with 5.94% and 94.06% attributed to changes in composition and behavior, respectively. Changes in composition were influenced by factors such as women’s age, marital status, education level, community lifestyle, and number of children. Meanwhile, changes in behavior among educated women, those aged 35-49, with a certain number of children, and living an agrarian lifestyle contributed to the change in modern contraceptive use. The most significant increase in modern contraceptive methods mix during this timeframe was noted in the adoption of implants. The future trend in modern contraceptive use can be described by the formula: modern contraceptive utilization = 32.5 + 1.3 (coded year) – 0.099 (squared coded year), with an adjusted R² value of 0.99 and a P value of 0.027. It suggests that the exponential smoothing trend can explain nearly 99% of the variation in modern contraceptive utilization. Over the past 6 years, population composition and behavior changes have driven a noticeable increase in modern contraceptive utilization. In Ethiopia, it is essential to target interventions toward advancing-age women, those women with no children, and women belonging to pastoralist communities to enhance contraceptive utilization rates further. Further, focusing on behavioral interventions and education to increase modern contraceptive use rather than solely targeting demographic changes is imperative.

PMID:40181136 | DOI:10.1038/s41598-025-96394-9

Sci Rep. 2025 Apr 3;15(1):11430. doi: 10.1038/s41598-025-94783-8.

ABSTRACT

This study aims to investigate the incidence of new-onset atrial fibrillation (AF) in individuals with type 2 diabetes mellitus (T2DM) across different categories of steatotic liver disease (SLD). Using a health examination database between 2009 and 2012, this study included 2,480,880 patients. Participants were categorized into five groups based on hepatic steatosis (fatty liver index ≥ 60), cardiometabolic risk factors, and alcohol consumption. Cox regression analyses were performed. The metabolic dysfunction-associated steatotic liver disease (MASLD) group showed an increased risk of new-onset AF (adjusted hazard ratio (aHR), 1.10; 95% confidence interval (CI), 1.08-1.11). The MASLD with other combined group demonstrated increased AF development (aHR, 1.22; 95% CI, 1.18-1.26). In metabolic dysfunction and alcohol-related steatotic liver disease (MetALD) and alcohol-related liver disease (ALD) with metabolic groups, heavy to excessive alcohol consumption increased the risk of AF incidence, with the highest aHR associated with greater alcohol intake (aHR, 1.26; 95% CI, 1.22-1.29, 1.48; 95% CI, 1.41-1.55). MASLD increased the risk of AF in patients with T2DM, with a higher risk observed when accompanied by other liver diseases. Alcohol consumption was associated with proportional increase in the risk of AF, with excessive alcohol consumption associated with the highest risk of AF.

PMID:40181094 | DOI:10.1038/s41598-025-94783-8

Sci Rep. 2025 Apr 3;15(1):11420. doi: 10.1038/s41598-025-96182-5.

ABSTRACT

How do we interact with our environment and make decisions about the world around us? Empirical research using psychophysical tasks has demonstrated that our perceptual decisions are influenced by past choices, a phenomenon known as the “choice history bias” effect. This decision-making process suggests that the brain adapts to environmental uncertainties based on history. However, single-subject experiment task design is prevalent across the work on choice history bias, thus limiting the implications of the empirical evidence to individual decisions. Here, we explore the choice history bias effect using a dual-participant approach, where dyads perform a shared perceptual decision-making task. We first propose two competing hypotheses: the participants equally weigh their own and their partner’s decision history, or the participants do not weigh equally their own and their partner’s decision history. We then use a statistical modeling approach to fit generalized linear models to the choice data in a series of steps and arrive at a model that best fits the observed data. Our results indicated that the own and partner’s trial history cannot be treated independently. The findings suggest an interaction of actor and decision at 1-back, leading to a choice alternation bias after a partner’s decision in contrast to a choice repetition bias after an own decision. A similar effect is observed at 2-back, in addition to an additive choice repetition bias of similar size. The effects of actor and decision at 2-back do not depend on the properties of the 1-back trial. Together, these findings support the idea that the participants do not ignore their partner’s decisions but treat these qualitatively differently from their own.

PMID:40181067 | DOI:10.1038/s41598-025-96182-5

Sci Rep. 2025 Apr 3;15(1):11516. doi: 10.1038/s41598-025-96043-1.

ABSTRACT

Accurate estimation techniques are crucial in statistical modeling and reliability analysis, which have significant applications across various industries. The three-parameter Weibull distribution is a widely used tool in this context, but traditional estimation methods often struggle with outliers, resulting in unreliable parameter estimates. To address this issue, our study introduces a robust estimation technique for the three-parameter Weibull distribution, leveraging the probability integral transform and specifically employing the Weibull survival function for the transformation, with a focus on complete data. This method is designed to enhance robustness while maintaining computational simplicity, making it easy to implement. Through extensive simulation studies, we demonstrate the effectiveness and resilience of our proposed estimator in the presence of outliers. The findings indicate that this new technique significantly improves the accuracy of Weibull parameter estimates, thereby expanding the toolkit available to researchers and practitioners in reliability data analysis. Furthermore, we apply the proposed method to real-world reliability datasets, confirming its practical utility and effectiveness in overcoming the limitations of existing estimation methodologies in the presence of outliers.

PMID:40181061 | DOI:10.1038/s41598-025-96043-1

Sci Rep. 2025 Apr 3;15(1):11413. doi: 10.1038/s41598-025-95640-4.

ABSTRACT

The potential of the sequence in Coronary Angiography (CA) frames for diagnosing coronary artery disease (CAD) has been largely overlooked. Our study aims to reveal the “Sequence Value” embedded within these frames and to explore methods for its application in diagnostics. We conduct a survey via Amazon Mturk (Mechanical Turk) to evaluate the effectiveness of Sequence Restoration Capability in indicating CAD. Furthermore, we develop a self-supervised deep learning model to automatically assess this capability. Additionally, we ensure the robustness of our results by differently selecting coronary angiographies/modules for statistical analysis. Our self-supervised deep learning model achieves an average AUC of 80.1% across five-fold validation, demonstrating robustness against static data noise and efficiency, with calculations completed within 30 s. This study uncovers significant insights into CAD diagnosis through the sequence value in coronary angiography. We successfully illustrate methodologies for harnessing this potential, contributing valuable knowledge to the field.

PMID:40181050 | DOI:10.1038/s41598-025-95640-4