Tag: pubmed

Sci Rep. 2026 Jun 1. doi: 10.1038/s41598-026-55920-z. Online ahead of print.

ABSTRACT

Conventional root canal treatment eliminates infection in necrotic permanent teeth but does not restore the dentin-pulp complex. The clinical predictability of regenerative endodontic treatment in mature teeth remains uncertain. This study evaluated the clinical and radiographic outcomes of an injectable platelet-rich fibrin (i-PRF)-supported regenerative protocol in mature necrotic teeth. This retrospective two-center cohort study included mature permanent teeth presenting with pulp necrosis, closed apices, and periapical lesions with a periapical index (PAI) score ≥ 3 treated between 2022 and 2025 using a standardized i-PRF-supported regenerative endodontic treatment protocol. Periapical healing was assessed using PAI scores, with cone-beam computed tomography (CBCT) images serving as supplementary three-dimensional illustrations. Postoperative pain was recorded using the Visual Analog Scale (VAS). Statistical analysis was performed using the Wilcoxon signed-rank test, Friedman test with Dunn post hoc analysis, logistic regression, and receiver operating characteristic (ROC) curve analysis. A total of 27 teeth were included, with a mean follow-up of 22.5 months. Clinical success was achieved in 24 teeth (88.9%). Mean PAI scores decreased significantly from 4.3 ± 0.78 preoperatively to 1.07 ± 1.57 at follow-up (p < 0.001). CBCT images showed radiographic evidence of three-dimensional periapical healing. Postoperative pain was minimal, with median scores reaching zero within 72 h and no need for analgesic medication. No significant predictors of treatment failure were identified. Within the limitations of this two-center retrospective study, i-PRF-supported regenerative endodontic treatment in mature necrotic teeth demonstrated favorable clinical outcomes, periapical healing, and low postoperative pain, suggesting its potential as a biologically based management approach.

PMID:42225930 | DOI:10.1038/s41598-026-55920-z

Acta Neurochir (Wien). 2026 Jun 1. doi: 10.1007/s00701-026-06930-3. Online ahead of print.

ABSTRACT

PURPOSE: Accurate segmentation of the subthalamic nucleus (STN) is paramount for optimising outcomes under deep brain stimulation (DBS) in Parkinson’s disease (PD). Clinically available tools like Brainlab Elements (BL-E) enable automated segmentations for surgical planning, yet their spatial relationship with postoperative volumes of tissue activated (VTAs) remains insufficiently characterised. Using multi-atlas segmentation (MAS) as an external anatomical reference, we compared the spatial correspondence of STN segmentations derived from BL-E with effective VTAs following monopolar contact review.

METHODS: We analysed imaging data from 40 PD patients with chronic STN-DBS. Segmentations were obtained using BL-E based on T1w and T2w scans and MAS derived from a library of 20 manually segmented midbrain nuclei atlases. Spatial correspondence was assessed using Dice Coefficients, Jaccard Indices, and Euclidean centroid distances. Distances between VTA centroids and clinically established settings for STN-DBS were calculated to evaluate targeting consistency. Statistical differences between metrics were assessed using Wilcoxon signed-rank tests.

RESULTS: BL-E segmentations demonstrated superior spatial correspondence with VTAs compared to MAS, with smaller Euclidean distances between centroids (p < 0.001). Dice Coefficients and Jaccard Indices showed no significant differences (p = 0.18). VTA centroid distances to the most efficient stimulation location were consistent across hemispheres (left: 2.54 mm [1.92-3.25]; right: 2.87 mm [1.85-3.82]) MAS targets were positioned more inferiorly and anteriorly compared to BL-E targets.

CONCLUSION: Clinically applied VTAs showed good spatial correspondence with planning segmentations, suggesting within-workflow reproducibility but not superior correspondence to anatomical ground truth per se. Future studies should incorporate connectomic information to more accurately reflect the functional relevance of stimulation and its therapeutic effects.

PMID:42225900 | DOI:10.1007/s00701-026-06930-3

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