Category: Statistics

Eur J Clin Nutr. 2026 May 11. doi: 10.1038/s41430-026-01752-z. Online ahead of print.

ABSTRACT

BACKGROUND: Cirrhosis and sarcopenia frequently coexist and are associated with poor clinical outcomes; however, their shared genetic basis remains incompletely understood.

METHODS: We applied conditional and conjunctional false discovery rate (cFDR/ccFDR) analyses to genome-wide association study (GWAS) summary statistics for cirrhosis and sarcopenia-related traits, including appendicular lean mass (ALM) and usual walking pace (UWP). In parallel, weighted gene co-expression network analysis (WGCNA) and three machine learning algorithms (LASSO, random forest, and support vector machine-recursive feature elimination) were applied to liver and skeletal muscle transcriptomes. External validation was performed using independent transcriptomic cohorts. Two-sample Mendelian randomization (MR) was conducted to explore causal directions.

RESULTS: GWAS-based pleiotropic analysis identified seven shared genetic loci for both conditions cirrhosis and sarcopenia. Transcriptomic and machine learning analyses prioritized eight shared candidate genes across liver and skeletal muscle tissues, among which PTPRR emerged as a convergent candidate identified by multiple analytical layers. Functional enrichment revealed pleiotropic loci were primarily associated with lipid metabolism and inflammatory pathways, whereas machine learning-derived genes were enriched in intracellular signaling and transcriptional regulation. MR analyses further suggested that genetically predicted higher ALM and faster UWP were associated with a lower risk of cirrhosis (inverse-variance weighted [IVW] P = 0.0127 and 0.0211, respectively).

CONCLUSIONS: By jointly reporting pleiotropic genetic loci and shared candidate genes, this study provides a multi-layered view of the genetic architecture underlying cirrhosis-sarcopenia comorbidity and supports the robustness of the identified gene signature across independent transcriptomic datasets, highlighting candidate molecular targets for future mechanistic investigation.

PMID:42115738 | DOI:10.1038/s41430-026-01752-z

Int J Obes (Lond). 2026 May 12. doi: 10.1038/s41366-026-02087-2. Online ahead of print.

ABSTRACT

BACKGROUND: High pre-pregnancy body mass index (BMI), accompanied by chronic low-grade inflammation may predispose offspring to adverse health outcomes by interfering with fetal development. However, the association between maternal pre-pregnancy obesity and elevated inflammatory biomarkers in the mother or fetus remains controversial. This study analyzed the association between pre-pregnancy BMI and biomarkers of inflammation in maternal serum and cord blood at birth in two large birth cohorts.

METHODS: Pre-pregnancy weight and height were used to calculate pre-pregnancy BMI (underweight [<18.5 kg/m²]; normal [18.5-24.9 kg/m²]; overweight [25.0-29.9 kg/m²]; obese [≥30 kg/m²]). Biomarkers of inflammation (interleukin [IL]-1β, IL-6, IL-10, tumor necrosis factor α [TNF-α]) were measured from maternal serum collected at birth in ELFE (n = 1046) and cord blood collected in both cohorts (EDEN [n = 856 for cytokines]; ELFE [n = 1016]) C-reactive protein (CRP) was additionally measured in the cord blood of both cohorts (EDEN: n = 820; ELFE: n = 1012]). Linear regression models were used to determine the association between BMI categories with biomarker levels, adjusting for confounders.

RESULTS: In ELFE, pre-pregnancy obesity was strongly and positively associated with cord blood CRP (adjusted β 0.52 [95% CI 0.32, 0.72]), while in EDEN, maternal overweight was associated with higher levels of cord blood CRP (0.32 [0.12, 0.54]). In ELFE, maternal underweight was also associated with higher levels of cord blood IL-10 in cord blood (0.20 [0.04, 0.35]). Pre-pregnancy BMI was not associated with any of the maternal serum biomarkers in ELFE in the overall analyses.

CONCLUSIONS: High pre-pregnancy BMI was associated with elevated CRP levels in cord blood, reflecting higher inflammatory marker levels in the perinatal environment. These findings should be replicated in other large cohort studies. The potential implications of elevated prenatal inflammation on offspring outcomes warrant further investigation.

PMID:42115734 | DOI:10.1038/s41366-026-02087-2

Sci Rep. 2026 May 11. doi: 10.1038/s41598-026-52283-3. Online ahead of print.

ABSTRACT

Relying on passive, traditional methods in anatomy education hinders deep understanding, whereas adopting active and creative teaching strategies fosters engagement and improves outcomes. Achieving deep immersion and sustained engagement in learning anatomy through learner-centered and active teaching strategies allows learners to understand complex spatial and functional relationships better. Therefore, this study aimed to examine the effect of drawing-based instruction on learning outcomes and the academic flow of medical students in the cardiovascular and respiratory system anatomy course. In this quasi- experimental study, 233 (Response rate 99.15%) medical students who were selected through the census method participated and were divided into two groups: control (n = 117) and intervention (n = 116). The control group received traditional lectures, while the intervention group was taught using drawing-based instruction. To implement this method, students were grouped and, as a team, completed drawing worksheets related to the topics of cardiovascular and respiratory system anatomy. At the end of the course, a station-based practical exam was administered to both groups. The Flow scale, developed by Martin and Jackson, was used to assess the flow level. The Persian version of this scale has confirmed validity and reliability, and its internal consistency has been assessed at a desirable level (Cronbach’s alpha = 0.85). Statistical analysis was conducted using SPSS software version 26. 0. Data analysis showed that the mean scores of students in the intervention group were significantly higher than those in the control group in respiratory system (3.11 ± 0.88 vs. 2.52 ± 0.80, d = -0.71, t = -5.262, P < 0.01) and cardiovascular (3.21 ± 0.88 vs. 2.58 ± 0.83, d = -0.74, t = -5.642, P < 0.01) courses. Additionally, the flow level in students trained with the drawing method was significantly higher than in the lecture group (24.31 ± 5.30 vs. 20.15 ± 4.73, d = – 0.83, t = -6. 315, P < 0. 01). The findings of this study indicate that incorporating learner-centered and active teaching methods, such as drawing, into anatomy education significantly improves learning outcomes and enhances the academic flow of medical students. Drawing acts as an active learning tool in anatomy education, ultimately leading to a better understanding of the material and a more engaging experience for students.

PMID:42115730 | DOI:10.1038/s41598-026-52283-3

Sci Rep. 2026 May 11;16(1):14728. doi: 10.1038/s41598-026-51510-1.

ABSTRACT

The airway epithelium is the first site of injury from cigarette smoke (CS), a major risk factor for chronic lung disease including idiopathic pulmonary fibrosis (IPF). Here, we report the first intracellular proteomic analysis of CS exposure in fully differentiated primary human bronchial epithelial cells (phBECs). Following pathway enrichment analysis, we identified nuclear protein 1 (NUPR1) as a candidate regulator of epithelial stress responses. In contrast to the prediction by pathway enrichment analysis, NUPR1 activity was not altered by CS in vitro. Nevertheless, inhibition of its nuclear translocation using ZZW-115 revealed a cytoprotective and anti-apoptotic role in phBECs, as demonstrated by increased apoptosis and impaired epithelial integrity. NUPR1 expression was markedly reduced in IPF whole lung tissue and bronchial epithelium. IPF-derived basal cells differentiated into an epithelium exhibiting fewer ciliated and more secretory cells which exhibited significantly higher sensitivity to NUPR1 inhibition. Our findings underscore cell type- and tissue-specific variation in NUPR1-dependent pathways. Collectively, this study positions NUPR1 as a context-dependent epithelial stress regulator whose loss may contribute to epithelial vulnerability in IPF.

PMID:42115729 | DOI:10.1038/s41598-026-51510-1

Sci Rep. 2026 May 12. doi: 10.1038/s41598-026-52888-8. Online ahead of print.

ABSTRACT

The genetic variability of Plasmodium falciparum serves as an important marker of the parasite’s ability to adapt and develop resistance to antimalarial treatments. This study sought to evaluate the diversity of the P. falciparum merozoite surface protein 2 (msp2) gene among patients receiving care in four health centers in the Mbouda Health District, Cameroon. Blood samples were obtained from 481 individuals who came for diagnostic testing with symptoms suggestive of malaria. Rapid diagnostic tests and thick blood smears were performed to confirm P. falciparum infection and determine parasite density, respectively. Positive samples were spotted onto Whatman filter paper for molecular testing. DNA was extracted using the Chelex-100 technique, and msp2 fragments were amplified via nested PCR. Amplicons were separated on 1.3% agarose gels and visualized under UV light. Phylogenetic analysis was performed in R, and statistical analyses were conducted using SPSS version 23. Out of the 481 samples analyzed, 137 (28.48%) tested positive for P. falciparum, with a mean parasite density of 2196.77 ± 1344.36 parasites/µL. Female participants showed a weakly significant association with malaria infection, while children aged 0-5 years, despite having an odds ratio above 1, did not show a statistically significant association. The msp2 gene was successfully amplified in 64% of positive samples, revealing 15 distinct alleles. The overall genetic diversity was 14.15%, with a mean multiplicity of infection (MOI) of 1.20. The proportions of mono-, double-, and triple-genotype infections were 81.68%, 18.18%, and 1.33%, respectively. Phylogenetic analysis identified 13 distinct clades, indicating genetic relatedness among circulating P. falciparum strains. A considerable level of genetic diversity and multiple infections was detected among P. falciparum isolates in the Mbouda Health District, suggesting high transmission intensity. Further studies incorporating additional molecular markers such as msp1 and GLURP are recommended to provide a more comprehensive picture of P. falciparum genetic variation in the region.

PMID:42115727 | DOI:10.1038/s41598-026-52888-8

Sci Rep. 2026 May 11. doi: 10.1038/s41598-026-51942-9. Online ahead of print.

ABSTRACT

A brain stroke is a critical cerebrovascular disorder that disrupts blood flow to specific brain regions, leading to irreversible neuronal damage if not diagnosed promptly. Accurate and early classification of stroke subtype plays a vital role in reducing mortality and improving recovery outcomes. Traditional diagnostic methods such as MRI and CT imaging rely heavily on manual interpretation, which can be time-consuming and prone to subjective variability. This research presents a Quantum support vector machine (QSVM) framework for automated brain stroke classification. The proposed system integrates a unified classical feature extraction pipeline comprising textural, morphological, frequency-domain, and statistical descriptors, followed by quantum state encoding within a six-qubit circuit using a ZZFeatureMap-based quantum kernel. The quantum kernel maps classical features into a higher-dimensional Hilbert space to enhance nonlinear separability. Experimental evaluation on a publicly available Kaggle MRI dataset using stratified 5-fold cross-validation demonstrates that the QSVM achieves 96.8% classification accuracy, 96.2% precision, 97.1% recall, F1-score of 96.6%, and an AUC-ROC of 0.982, outperforming optimized classical baselines including Random Forest, K-Nearest Neighbors, and traditional SVM variants on identical feature sets. All experiments were conducted using a classical quantum simulator; therefore, the reported improvements represent simulator-based performance gains rather than hardware-level quantum advantage. These findings suggest that quantum-inspired kernel methods can improve classification performance under controlled experimental conditions, warranting further validation on larger multicenter datasets and real quantum hardware.

PMID:42115706 | DOI:10.1038/s41598-026-51942-9

Sci Data. 2026 May 11. doi: 10.1038/s41597-026-07370-5. Online ahead of print.

ABSTRACT

Accurate, continuous and high-resolution mapping of multiple crop types, including both grain and cash crops, is vital for supporting sustainable agricultural development. While substantial progress has been made in mapping major grain crops, China still lacks a long-term, high-resolution dataset that simultaneously captures winter wheat, maize, rice and sugarcane. In this study, we used machine learning method to produce the China Crop Dataset (CCD), a 30 m resolution multi-crop dataset spanning 2001-2024, by integrating Landsat imagery and fused product with high spatial resolution. Validation based on field surveys and visually interpreted Google Earth samples confirmed the high accuracy of the CCD, with producer’s accuracy, user’s accuracy and overall accuracy reaching 88.45%, 87.2% and 91.01%, respectively. Furthermore, the CCD exhibited strong spatial consistency with existing datasets. The classified areas of winter wheat, maize, single-season rice, double-season rice and sugarcane showed good agreement with statistical area, with correlation coefficients (R²) exceeding 0.6 in most provinces. This dataset provides a robust and long-term resource for supporting agricultural planning, and facilitating research on land use and food security in China.

PMID:42115702 | DOI:10.1038/s41597-026-07370-5

Sci Rep. 2026 May 11. doi: 10.1038/s41598-026-52977-8. Online ahead of print.

ABSTRACT

Pediatric nurses constitute a critical workforce in Ghana due to the continuous and high-intensity demands of child care. However, their occupational health outcomes remain underexplored, particularly in northern Ghana. This study assessed the effect of night shift work on daytime sleepiness and physiological health among pediatric nurses in the Tamale Metropolis, Northern Ghana. A cross-sectional survey was conducted among 175 pediatric nurses. A stratified sampling technique was used to ensure proportional representation of pediatric nurses from each health facility, followed by simple random sampling for participant selection. Data were collected using a structured questionnaire incorporating the Epworth Sleepiness Scale (ESS) and Night Shift Physiological Health Assessment Scale (NSPHAS). Appropriate inferential statistics were used, with statistical significance set at p < 0.05. The mean daytime sleepiness score following night shift work was 10.12 (SD = 5.55), with 20.6% of participants exhibiting severe excessive daytime sleepiness. The highest chance of dozing was reported when lying down to rest in the afternoon (31.4%). Sleep disturbances recorded the highest mean physiological health score (2.87 ± 0.98), followed by gastrointestinal disturbances and eating habit disruptions (2.29 ± 0.91), and cardiovascular and physical strain (2.28 ± 0.91). Significant differences in physiological health outcomes were also observed across sociodemographic and work-related characteristics (p < 0.05). Night shift work is associated with increased daytime sleepiness and physiological health disturbances among pediatric nurses. Context specific interventions, including improved shift scheduling and targeted sleep health education, may help reduce these adverse outcomes.

PMID:42115689 | DOI:10.1038/s41598-026-52977-8

Sci Rep. 2026 May 11. doi: 10.1038/s41598-026-50659-z. Online ahead of print.

ABSTRACT

Early diagnosis of postmenopausal osteoporosis provides an opportunity to detect and prevent fractures. This study uses machine learning (ML) techniques to enhance the predictive ability for low bone mass (LBM) risk. A retrospective cross-sectional study was performed, including 3,738 menopausal women from a hospital (the internal validation dataset) and 1,008 menopausal women from the community (the external validation dataset) between December 2014 and February 2022. The least absolute shrinkage and selection operation (LASSO) and elastic net methods are employed to screen the variables. ML algorithms and logistic regression are applied using clinical risk factors to develop a prediction model, and its effectiveness is subsequently evaluated. The optimal model is selected, and the concordance statistic is established for discrimination, comprising 11 variables. In predicting LBM, the model achieves an AUC of 0.918 in the internal validation dataset and 0.910 in the external validation dataset, with the XGboost model particularly noteworthy. This prediction model assists older women at elevated risk of osteoporosis, guiding decision-making for primary care providers to identify those needing preventive treatment.

PMID:42115688 | DOI:10.1038/s41598-026-50659-z

Sci Rep. 2026 May 11;16(1):14706. doi: 10.1038/s41598-026-50503-4.

ABSTRACT

High-purity α-Al2O3 nanoparticles were synthesised using a modified Pechini sol-gel method and calcinated at 1100 °C. Their structural, optical, and dielectric properties were thoroughly examined. Structure research utilizing X-ray diffraction and advanced Rietveld refinement showed that adding the axial divergence asymmetry pseudo-Voigt function improves refinement quality and produces more precise crystallographic parameters for the R-3c corundum structure. A stress-free crystalline framework was confirmed by a size-strain plot showing a volume-weighted crystallite size of ~ 24.4 nm and low lattice strain. The HR-TEM revealed spherical polycrystalline aggregates with an average particle size of ~ 100 nm, while FTIR confirmed phase purity and complete organic precursor elimination. UV-Vis diffuse reflectance spectroscopy determined the refractive index, extinction coefficient, absorption coefficient, and complex dielectric function. The Kubelka-Munk formalism estimated the optical band gap at ~ 4.29 eV, indicating wide-band-gap insulating behaviour. In the visible-near-infrared region, the real part of the dielectric constant showed substantial photon energy dispersion, but the imaginary part remained extremely low (≤ 0.03), indicating minimal optical losses. The dielectric loss tangent was extremely low (~ 10-4-10-6), indicating strong electronic polarization and minimal dissipation. The lattice dielectric constant (εₗ = 7.97), low plasma frequency, and minimal free-carrier contribution support the intrinsic insulation of α-Al2O3. Researchers found a strong correlation between structural perfection and low-loss optical response, making α-Al2O3 nanoparticles promising for high-transparency, dielectric-stable applications such as optical coatings, ultraviolet optoelectronic devices, and high-frequency photonics.

PMID:42115682 | DOI:10.1038/s41598-026-50503-4