Tag: pubmed

Allergol Immunopathol (Madr). 2026 May 1;54(3):17-23. doi: 10.15586/aei.v54i3.1564. eCollection 2026.

ABSTRACT

PURPOSE: House dust mite (HDM) allergy is a common cause of allergic rhinitis (AR) and allergic asthma (AA). Interleukin-27 (IL-27) is known to suppress Th2-mediated inflammation, a key driver of these diseases. This study aimed to assess regional sensitization to Dermatophagoides subspecies and to investigate the association between HDM-specific IgE responses and serum IL-27 levels.

METHODS: Fifty-eight children with HDM allergy were evaluated, of whom 53 were sensitized to D. Pteronyssinus. Serum Der p 1/Der p 2 specific IgE (sIgE) (FEIA) and IL-27, IL-5, and IL-13 levels (ELISA) were measured. Twenty-five healthy children served as controls.

RESULTS: Among patients (43% AR, 57% AA), Der p 1 and Der p 2 sensitization rates were 49% and 55%, respectively. Both Der p 1/Der p 2 sIgE levels were significantly elevated compared to controls (p < 0.001). Although IL-27 levels were lower in patients, the difference was not statistically significant (p = 0.98). However, IL-27 showed positive correlations with IL-5, IL-13, and Der p 1 sIgE (all p < 0.05). IL-27 levels were unexpectedly higher in Der p 1-sensitized patients (p = 0.006), particularly in AR (p = 0.02; r = 0.43), but not in AA.

CONCLUSIONS: This is the first clinical study to investigate the relationship between IL-27 and HDM-sIgE in children and to demonstrate a phenotype-specific interaction. IL-27 may act as a context-dependent immunomodulator rather than a simple Th2 suppressor. The positive correlation between IL-27 and Der p 1 sIgE in AR patients may indicate a compensatory feedback mechanism triggered by allergen-specific inflammation.

PMID:42115790 | DOI:10.15586/aei.v54i3.1564

Diabetes Obes Metab. 2026 May 11. doi: 10.1111/dom.70868. Online ahead of print.

ABSTRACT

AIMS: To evaluate the impact of long-term glycemic exposure and control status on cognitive function in older adults.

MATERIALS AND METHODS: Using repeated measurements of fasting blood glucose (FBG) from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China project, we calculated 10-year time-weighted cumulative fasting blood glucose (cumFBG) and glycemic variability, and identified FBG trajectories using group-based trajectory modelling among 26 108 participants. In addition, diabetic patients were categorized as consistently controlled or not according to long-term FBG levels. Mini-Mental State Examination was used to evaluate global and domain-specific cognition, and cognitive impairment was defined according to education-specific cutoffs. Linear and logistic regression models were applied to assess associations between FBG-related indicators and cognition.

RESULTS: CumFBG was nonlinearly associated with global cognition and cognitive impairment (both P_{for nonlinearity} < 0.05). Compared with participants with cumFBG < 900 mg/dL × year, those with cumFBG ≥ 1260 mg/dL × year had a 0.091-point lower z-standardized global cognitive score (95% CI: -0.142, -0.040) and a 3% increased cognitive impairment risk (OR: 1.030; 95% CI: 1.010, 1.050). Among specific domains, attention appeared more susceptible, with declines emerging when cumFBG ≥ 900 mg/dL × year. Notably, among individuals with cumFBG ≥ 1260 mg/dL × year, increasing trajectory had a 21.2% increased cognitive impairment risk (OR: 1.212; 95% CI: 1.122, 1.310). Higher variability was associated with worse cognition. Furthermore, only diabetic participants with consistent control exhibited cognition comparable to those without diabetes.

CONCLUSION: Elevated cumFBG, especially in an increasing pattern, influenced cognition in older adults and sustained glycemic control appeared to mitigate these adverse impacts.

PMID:42115763 | DOI:10.1111/dom.70868

NPJ Precis Oncol. 2026 May 11. doi: 10.1038/s41698-026-01472-4. Online ahead of print.

ABSTRACT

Accurate prediction of recurrence risk is essential to devise effective and personalized treatment strategies for patients with soft tissue sarcoma (STS). This study aimed to develop and validate a multimodal deep learning framework that integrates clinical features, preoperative MR images, and hematoxylin and eosin-stained whole slide images (WSIs) to predict recurrence in patients with STS. A total of 323 patients with STS were retrospectively enrolled from two hospitals, serving as development and validation sets, respectively. The ShuffleNetV2 network was utilized to develop patch-level and WSI-level signatures. A convolutional neural network fusing the channel and spatial attention mechanisms was used to develop a radiology signature. The combined model was built by integrating clinical features, radiology signature score, and WSI-level signature score with Cox regression analysis. The combined model demonstrated superior performance in the validation set, achieving a C-index of 0.857 and a time-dependent area under the curve of 0.959. Class activation maps facilitated the monitoring of suspected regions to inform recurrence decisions. The recurrence-free survival times of the low- and high-risk cohorts were statistically different (p < 0.05). The proposed multimodal framework offers satisfactory accuracy for predicting recurrence risk in patients with STS and could guide the choice of treatment modality.

PMID:42115754 | DOI:10.1038/s41698-026-01472-4

Sci Rep. 2026 May 11. doi: 10.1038/s41598-026-52492-w. Online ahead of print.

ABSTRACT

This paper presents a statistical framework for early warning change-point detection in electrical grid frequency time series. Frequency deviations outside the tolerance band of 49.85-50.15 Hz are treated as error events. A high-volatility (HV) measure is computed using a rolling-window approach and compared against a Hoeffding-bound threshold to identify significant transitions that may precede hazardous excursions. A dataset of 1250 error-event sequences collected over six months is divided into training (34%), validation (33%), and testing (33%) subsets. To improve efficiency, k-means clustering and dynamic time warping (DTW) are used to select representative training sequences, and a mapping-with-regression procedure is applied to generate warning signals. Experimental results show that the proposed method achieves 98.04% accuracy and an F1-score of 98.06%, while maintaining a false-negative rate of 1.1%. Lead-time evaluation confirms consistent early detection, and baseline comparison against deep learning approaches, demonstrates competitive performance with low computational cost.

PMID:42115751 | DOI:10.1038/s41598-026-52492-w

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