Tag: pubmed

Sci Rep. 2026 Jun 1. doi: 10.1038/s41598-026-55394-z. Online ahead of print.

ABSTRACT

The Muller maneuver (MM) collapses the upper airway and mimics apneic events during sleep. This study aimed to assess the effect of MM on the upper airway (UA) and surrounding structures of patients with OSA using cone-beam computed tomography (CBCT). This prospective study of 18 moderate-to-severe OSA patients included two CBCT scans, one during gentle breathing and another while performing MM, with standardized head and neck positioning. UA, soft tissue, and hyoid bone were analyzed using linear, area, and volumetric measurements with OnDemand 3D software version 10.0.1 (1008 measurements). Paired t-tests, Wilcoxon signed-rank tests, Marginal Homogeneity tests, and two-way repeated-measures ANOVA were performed using SPSS version 27 software. Effect sizes were calculated using Cohen’s d. MM statistically significantly decreased the following airway parameters: minimum anterior-posterior (mAP) of nasopharynx (6.41% (P = 0.048)), mAP-oropharynx (38.81% (P = 0.006)), minimum transverse(mT) of oropharynx (38.88% (P = 0.006)), minimum cross-sectional area(mCSA) of oropharynx (42.02%; P = 0.011), volume(V) of oropharynx (27.41%; P = 0.002), mAP-hypopharynx (19.77%;P = 0.039) and mCSA-hypopharynx (11.77%;P = 0.048), V-UA (11.76%;P = 0.048) and minimum axial area (39.01%; P = 0.007). MM also resulted in significant vertical hyoid bone changes and soft tissue length (P = 0.001, P = 0.016, respectively). Effect size analysis demonstrated predominantly moderate-to-large effects across variables, particularly for hyoid bone displacement and oropharyngeal airway narrowing, indicating that the observed changes were not only statistically significant but also clinically meaningful. This noninvasive, low-cost approach, provides comprehensive evaluation of the UA and surrounding structures. It also offers functional insight by capturing airway configuration under negative pressure conditions, enabling a more dynamic assessment of airway behavior and collapsibility.

PMID:42225889 | DOI:10.1038/s41598-026-55394-z

J Imaging Inform Med. 2026 Jun 1. doi: 10.1007/s10278-026-02010-1. Online ahead of print.

ABSTRACT

Conventional pixel-wise loss functions fail to enforce topological consistency in coronary vessel segmentation, producing fragmented vascular trees despite high pixel-level accuracy. We present ARIADNE, a two-stage framework coupling preference-aligned perception with RL-based diagnostic reasoning for topologically consistent stenosis detection through an explicit Perception-Reasoning Synergy in which topology-aware segmentation serves as the structural prerequisite for reliable downstream diagnosis. The perception module employs DPO to fine-tune the Sa2VA vision-language foundation model using Betti number constraints as preference signals, aligning the policy toward topologically consistent vessel structures rather than pixel-wise overlap metrics. The reasoning module formulates stenosis localization as a Markov Decision Process with an explicit rejection mechanism that autonomously defers ambiguous anatomical candidates such as bifurcations and vessel crossings, shifting from coverage maximization to reliability optimization and thereby mitigating the clinical alert fatigue that has historically constrained automated decision support. Validated through a conservative patient-level statistical design (n = 35), ARIADNE achieves state-of-the-art Dice of 0.8034 and centerline Dice (clDice) of 0.8378, significantly outperforming generic foundation models including MedSAM3, while attaining a True Positive Rate of 0.867 and reducing False Positives Per Image to 0.85 in stenosis detection. External validation on the public XCAD benchmark confirms generalization across acquisition protocols. This represents the first application of DPO for topological alignment in medical imaging, demonstrating that preference-based learning over structural constraints mitigates topological violations while maintaining diagnostic sensitivity in interventional cardiology workflows. The code is available at https://github.com/qimingfan10/ARIADNE .

PMID:42225888 | DOI:10.1007/s10278-026-02010-1

J Gen Intern Med. 2026 Jun 1. doi: 10.1007/s11606-026-10528-x. Online ahead of print.

ABSTRACT

BACKGROUND: High-quality patient-centered communication (PCC) is associated with improved health outcomes. However, individuals from underrepresented racial/ethnic communities in the U.S. often experience poor PCC and disproportionately worse health outcomes compared to White individuals. Racial-ethnic-cultural (REC) belonging, defined as a sense of connection to one’s REC group that fosters feelings of value, acceptance, and security, represents an understudied aspect of community-based social support. Unlike related constructs like patient-provider racial concordance, REC belonging emphasizes individuals’ lived experiences of inclusion and may play an important role in moderating PCC, which functions as clinical social support.

OBJECTIVE: To examine potential associations between PCC and REC belonging and explore how REC belonging varies across sociodemographic factors.

DESIGN: Cross-sectional analysis of self-reported data from the National Cancer Institute’s Health Information National Trends Survey 7 (HINTS 7), a nationally representative survey of U.S. adults. Descriptive statistics identified sociodemographic patterns in REC belonging. Logistic regressions further explored differences in REC belonging across race/ethnicity. Linear regressions examined associations between REC belonging and PCC.

PARTICIPANTS: Respondents to HINTS 7 who reported visiting a healthcare clinician within the 12 months prior to survey completion (n = 5023).

MAIN MEASURES: PCC was assessed using the 7-item Patient-Centered Communication Scale (PCCS). REC belonging was assessed through agreement with a statement regarding a strong sense of belonging to one’s ethnic, racial, or cultural group, with responses categorized as “belonging” or “non-belonging.”

KEY RESULTS: Greater REC belonging was observed among non-White Hispanic (p < 0.001), heterosexual (p = 0.004), older (75+) (p = 0.006), non-liberal (p < 0.001), and non-married (p = 0.04) individuals. REC belonging was also significantly associated with higher PCC overall (β, 95% CI 4.97, 2.63-7.31).

CONCLUSIONS: Results showed an association between higher PCC and REC belonging. Understanding sociodemographic differences in REC belonging may guide community-based strategies to enhance communication, strengthen social support, and improve health outcomes in underrepresented communities.

PMID:42225874 | DOI:10.1007/s11606-026-10528-x

Nat Genet. 2026 Jun 1. doi: 10.1038/s41588-026-02643-6. Online ahead of print.

NO ABSTRACT

PMID:42225866 | DOI:10.1038/s41588-026-02643-6

NPJ Digit Med. 2026 Jun 1;9(1):412. doi: 10.1038/s41746-026-02819-8.

ABSTRACT

Artificial intelligence (AI) has shown strong performance in cardiology, but most approaches rely on sensing modalities whose physical limitations constrain available information. Magnetocardiography (MCG) records the cardiac magnetic field with less tissue distortion than surface potentials and may preserve higher-dimensional spatiotemporal electrophysiological structure. Here, we investigated whether combining MCG with self-supervised learning enables physiologically meaningful cardiac representations. We developed MCG2Vec, a contrastive encoder trained directly on raw 64-channel MCG recordings. Using recordings from 1732 consecutive patients, learned embeddings were evaluated with task-specific probes for multivessel coronary artery disease, reduced left ventricular ejection fraction, and paroxysmal atrial fibrillation risk from sinus-rhythm recordings. The representations enabled discrimination of multivessel coronary artery disease (area under the receiver operating characteristic curve (AUC) 0.89), reduced left ventricular ejection fraction (AUC 0.81), and atrial fibrillation risk (AUC 0.77). Attribution analyses revealed probe-specific temporal and spatial patterns corresponding to ventricular depolarization, repolarization, atrial activation dynamics, and coronary territories, supporting physiological interpretability. These findings suggest that higher-fidelity sensing combined with self-supervised representation learning can yield structured and explainable embeddings from non-invasive cardiac magnetic field recordings. More broadly, the study highlights measurement physics as an important determinant of what medical AI systems can learn.

PMID:42225842 | DOI:10.1038/s41746-026-02819-8

Sci Rep. 2026 Jun 1. doi: 10.1038/s41598-026-54196-7. Online ahead of print.

ABSTRACT

Peritoneal adhesions are common following abdominal surgery and lead to significant morbidity. Both pharmacological agents and barrier methods have been investigated for prevention, but with limited success. In this study, the efficacy of intraperitoneal pitavastatin was evaluated and compared with Seprafilm. Thirty-two female Wistar albino rats were randomly divided into four groups (n = 8): control, saline, pitavastatin (30 mg/kg intraperitoneal), and Seprafilm (30 × 20 mm). Macroscopic adhesions were graded using the Majuzi classification, and microscopic adhesions were assessed using the Zühlke scoring system. Plasma SCUBE1, malondialdehyde, and tissue-type plasminogen activator levels were analyzed. Macroscopic evaluation showed that adhesions were predominantly grade 2 in the control group (62.5%) and the saline group (75%). In contrast, a partial reduction in adhesion severity was observed in both the pitavastatin and Seprafilm groups; 25% of patients in both groups showed no adhesions (grade 0). Lower-grade adhesions (grades 1-2) were observed more frequently in these treatment groups. Microscopically, all rats in the control and Seprafilm groups were classified as stage 2 according to the Zühlke grading system. While grade 1 adhesions were predominantly observed in the saline group (87.5%), a broader distribution was observed in the pitavastatin group, including grade 1 (25%), grade 2 (50%), and grade 3 (25%) adhesions. Biochemical analysis revealed no significant differences among groups in plasma SCUBE1 (p = 0.294) and malondialdehyde levels (p = 0.051). However, plasma tissue-type plasminogen activator levels were significantly higher in the control group compared with both the pitavastatin (p = 0.012) and Seprafilm groups (p = 0.027). Intraperitoneal pitavastatin has demonstrated efficacy comparable to that of Seprafilm; however, neither treatment provided a statistically significant protective effect against postoperative peritoneal adhesions.

PMID:42225809 | DOI:10.1038/s41598-026-54196-7

Sci Rep. 2026 Jun 1. doi: 10.1038/s41598-026-54322-5. Online ahead of print.

ABSTRACT

This study aimed to evaluate the performance of different rootstock-scion combinations on yield and quality improvement in ber (Ziziphus mauritiana Lamk.) under semiarid conditions was conducted at the Regional Research Station, Bawal, Haryana. For statistical analysis, data collected during the stabilized bearing phase (2020-2024) were utilized. The experiment was conducted in a two-factor randomized block design (RBD) comprising three rootstocks (Ziziphus rotundifolia, Ziziphus mauritiana cv. Tikadi and Ziziphus mauritiana cv. Sukhavani) and two scion varieties (Gola and Umran). The pooled data revealed significant variation among rootstocks, scions and their combinations in terms of plant height, trunk girth, leaf area index, chlorophyll content, canopy footprint, fruit physicochemical attributes and yield. Z. mauritiana Tikadi consistently imparted greater vigor, greater trunk cross-sectional area (45.1 cm²), increased canopy footprint (25.8 m²) and greater yield (53.60 kg/plant) in combination with Umran. In contrast, Z. mauritiana Sukhavani presented a comparatively reduced tree size and improved fruit quality traits, such as greater total soluble solids (19.25°B) and ascorbic acid (95.41 mg/100 g), especially with Gola. Fruit size and pulp content are largely governed by the scion genotype, with Umran producing heavier fruits. The scion/stock ratio remained close to unity across the treatments, confirming good graft compatibility. The results highlight the importance of rootstock selection in regulating tree performance, suggesting a practical strategy for improving orchard productivity without the need for the genetic replacement of cultivars.

PMID:42225796 | DOI:10.1038/s41598-026-54322-5

Sci Rep. 2026 Jun 1. doi: 10.1038/s41598-026-54803-7. Online ahead of print.

ABSTRACT

Asthma and chronic obstructive pulmonary disease (COPD) are significant global health burdens with conventional diagnosis relying on resource-intensive spirometry. This paper presents a reproducible multimodal respiratory sound screening model combining complementary acoustic and clinical representations. The proposed method fuses handcrafted spectral-temporal features (MFCCs, chroma, spectral contrast, tonnetz, mel-spectrogram, tempogram) with precomputed cough and vowel embeddings and structured clinical metadata, processed via a class-weighted CatBoost ensemble on the standardized AIRS Kaggle benchmark dataset. The model achieves an overall accuracy of 90.3% with class-wise F1-scores of 0.945 (Healthy), 0.915 (Asthma), and 0.842 (COPD). Systematic ablation experiments confirm the importance of multimodal fusion (-7.8% accuracy without full feature fusion), the attention mechanism (-4.9%), and data augmentation (-6.7%). Additional metrics such as, Matthews Correlation Coefficient (MCC = 0.856) and Cohen’s Kappa (κ = 0.849) – confirm robust classification under class imbalance. Structured multimodal feature fusion with gradient boosting enables scalable, reproducible respiratory disease screening applicable to telemedicine. Future work should address prospective validation on diverse, multi-institutional clinical cohorts.

PMID:42225794 | DOI:10.1038/s41598-026-54803-7

Sci Rep. 2026 Jun 1. doi: 10.1038/s41598-026-54553-6. Online ahead of print.

ABSTRACT

The paper focuses on detecting faults in tapered roller bearings to prevent unplanned shutdowns, accidents, and financial losses. Tapered roller bearings are an indispensable component used in mechanical applications, where handling of combined loads is required in rotating machinery. This study proposes a novel framework ConvECA-Net for advanced fault diagnosis of tapered roller bearings. This innovative architecture combines Efficient Channel Attention (ECA), adaptive kernel size strategy, and Leaky ReLU activation. This fault classification network is designed to classify five fault conditions in the tapered roller bearings. This proposed architecture is compared with deep learning algorithms such as ANN and ResNet50 and traditional machine learning algorithms such as SVM and RF. The proposed ConvECA-Net achieves a better classification accuracy of 95.07% and requires only 563 K trainable parameters and a model size of 2.2 MB. A systematic component-wise ablation study is conducted to validate that each component of this architectural design, namely ECA attention, adaptive kernel sizing, and Leaky-ReLU activation, individually and collectively contribute to the overall performance of this diagnostic model, as it achieves superior performance compared to its baseline variant by 5.45%. Cross-condition robustness is further established by evaluating this diagnostic model on three different rotational speeds and various load levels. Evaluation of noise robustness on this diagnostic model for various levels of Gaussian white noise and pink noise, further establishes its robustness of the proposed model. Further, Statistical validation of this diagnostic model is conducted by running this experiment ten times, using paired t-tests, Shapiro-Wilk normality tests, and stratified 5-fold cross-validation (94.82% ± 0.38%). Finally, analysis of computational efficiency of this diagnostic model reveals that it achieves 192 MFLOPs and an inference latency of 0.82 ms/sample, making it suitable for industrial condition monitoring systems of rotating machinery to prevent the plant shutdown.

PMID:42225790 | DOI:10.1038/s41598-026-54553-6

Sci Rep. 2026 Jun 1. doi: 10.1038/s41598-026-54775-8. Online ahead of print.

ABSTRACT

As demonstrated by numerous environmental disasters worldwide, many sites have suffered from seismically induced liquefaction, resulting in substantial economic losses. Consequently, there is an urgent need for reliable prediction methods to assess vulnerability to liquefaction. In this study, the liquefaction potential (LP) of gravelly soil sites is predicted using available seismological parameters (M_w, R, t, PGA), soil parameters (G, F, D₅₀), and site profile parameters (N’₁₂₀, σ’_v, Dw, H_n, D_n) through AI-based symbolic regression techniques, namely response surface methodology (RSM), genetic programming (GP), evolutionary polynomial regression (EPR), and the group method of data handling neural network (GMDH-NN). A total of 234 data records were compiled from earthquake case histories reported in the literature and divided into 80% for training and 20% for validation. RSM was employed to model the database, whereas GP, EPR, and GMDH-NN were used for liquefaction potential classification in the investigated area. Comparative evaluation of model performance indicates that the RSM yielded a statistically significant parametric LP equation, operating with a degree of variation of 0.61 and a p-value of 0.0001. For the classification models, GP, EPR, and GMDH indicate that liquefaction is expected when the predicted LP ≥ 0.5, while liquefaction is not triggered when LP < 0.5. The total misclassification cases were categorized into positive errors, where liquefaction occurred but was not predicted, and negative errors, where liquefaction was predicted but not observed, with the latter being more conservative. In practice, negative errors are not entirely definitive, as liquefaction may locally occur beneath surface layers without being visibly manifested. Although all four predictive models achieved comparable accuracy levels ranging from 88% to 90%, further analysis revealed that the GP model produced 12% positive errors, whereas both EPR and GMDH models resulted in only 6% positive errors, indicating that they are more conservative and safer than the GP model. In addition, the GMDH model is considerably more complex than the EPR model, providing EPR with a notable practical advantage. Correlation analysis further demonstrated that the vertical effective overburden stress (σ’_v) and the dynamic penetration test blow count (N’₁₂₀) are the most influential parameters, each exhibiting a correlation coefficient greater than 0.3g.

PMID:42225787 | DOI:10.1038/s41598-026-54775-8