Tag: pubmed

Commun Biol. 2026 Jun 16. doi: 10.1038/s42003-026-10451-1. Online ahead of print.

ABSTRACT

Cell-cell communication (CCC) contributes to bacterial survival and adaptability. Gram-positive bacteria employ secreted peptides to coordinate CCC. While the molecular pathways activated by these peptides are well studied, little is known about how individual cells contribute to initiating the signaling response. To address this question, we used microdroplet arrays to examine the major human pathogen Streptococcus pneumoniae and its TprA/PhrA regulator/peptide CCC system, which promotes colonization and virulence. We measured phrA promoter activity in wild-type (WT) cells and in a phrA deletion mutant, using populations seeded before signaling began. As signaling emerged, we observed heterogeneity in S. pneumoniae signaling within and across microdroplets. Addition of exogenous PhrA increased both the magnitude of signal and the percentage of signaling cells, yet it did not reduce the heterogeneity of signal. When examining whether PhrA peptide produced from WT cells was shared with ΔphrA cells, we found a preference for self-signaling over signaling to neighboring cells. Overall, we developed a platform to quantify cell-cell signaling at the single-cell level and determined that at early stages TprA/PhrA signaling is highly heterogeneous and primarily targets producing cells. We propose that this heterogeneity and its amplification through autoinduction may confer a fitness advantage to the population.

PMID:42304065 | DOI:10.1038/s42003-026-10451-1

Sci Rep. 2026 Jun 16. doi: 10.1038/s41598-026-58469-z. Online ahead of print.

ABSTRACT

To identify psychosocial factors associated with medication adherence among elderly patients with chronic diseases, guided by the Integrated Theory of Health Behavior Change (ITHBC). A cross-sectional survey was conducted among 1,138 community-dwelling adults aged sixty years or older who were receiving long-term treatment for chronic diseases. A structured questionnaire was administered that incorporated validated instruments including the Adherence to Refills and Medications Scale (ARMS), the Medication Literacy Knowledge-Attitudes-Practices (KAP) Scale, the Beliefs about Medicines Questionnaire (BMQ), the Self-efficacy for Appropriate Medication Use Scale (SEAMS), and the Social Support Rating Scale (SSRS). Descriptive statistics, group comparisons, and binary logistic regression were used to examine the psychosocial factors associated with medication adherence. A total of 1,102 valid responses were obtained, yielding a response rate of 96.84%. Among the participants, 46.64% demonstrated good medication adherence. Guided by the ITHBC framework, psychosocial variables were categorized into intention-, capability-, and context-related domains. Logistic regression analysis showed that higher self-efficacy (OR = 0.874, p < 0.001) and higher medication literacy (OR = 0.980, p = 0.020) were associated with lower odds of poor medication adherence, whereas stronger medication concern beliefs (OR = 1.128, p = 0.008) and a history of adverse drug reactions (OR = 0.596, p = 0.035) were associated with poorer adherence. Compared with older adults living with a spouse, those living alone (OR = 3.848, p = 0.003) and those living with children (OR = 3.404, p = 0.004) showed significantly higher odds of poor medication adherence. Medication adherence among older adults with chronic diseases was associated with medication literacy, self-efficacy, social support, and medication beliefs. The ITHBC provided a useful framework for understanding these psychosocial determinants. The findings highlight the importance of multidimensional adherence-support interventions integrating medication education, psychosocial support, and self-management enhancement for older adults with chronic diseases.

PMID:42304063 | DOI:10.1038/s41598-026-58469-z

Sci Rep. 2026 Jun 16. doi: 10.1038/s41598-026-57900-9. Online ahead of print.

ABSTRACT

The domain shift problem induced by variable working conditions severely constrains the cross-domain generalization capability of data-driven fault diagnosis models. Existing methods lack collaborative modeling of the multi-scale time-frequency characteristics of vibration signals at the feature extraction level. They also neglect the constraint guidance of fault physical mechanisms on the feature alignment process at the domain adaptation level. To address these deficiencies, this paper proposes a physics-guided cross-domain adaptation framework-a Hierarchical Hybrid Transformer network with Contrastive Learning (PgHHT-CL). The framework comprises three key designs. At the feature encoding level, a hierarchical hybrid Transformer encoder is constructed. It achieves collaborative extraction of transient impulse components and periodic modulation components in vibration signals through gated interactive fusion of local convolutional branches and global self-attention branches across multiple abstraction levels. At the domain adaptation level, a physics-guided cross-domain contrastive learning strategy leverages the order-invariance relationship between fault characteristic frequencies and rotational frequency from bearing dynamics prior knowledge to constrain the construction of cross-domain positive and negative sample pairs. The feature alignment process is thereby required to satisfy physical consistency beyond statistical distribution matching. At the training optimization level, a joint optimization objective integrates classification loss, cross-domain contrastive loss, and physical consistency loss, with a progressive weight adjustment strategy to ensure stable convergence of multi-task learning. Extensive cross-condition transfer experiments on two public bearing datasets from Case Western Reserve University and Paderborn University show that PgHHT-CL achieves average diagnostic accuracies of 94.94 ± 0.32% and 90.26 ± 0.50%, respectively, attaining the highest mean accuracy across all 18 transfer tasks among the representative state-of-the-art baselines selected for comparison. The framework also exhibits notable robustness under large domain shift and strong noise conditions. Ablation experiments and feature visualization analyses further validate the effectiveness and physical interpretability of the physics-guided strategy and hierarchical hybrid architecture.

PMID:42304057 | DOI:10.1038/s41598-026-57900-9

Sci Rep. 2026 Jun 16. doi: 10.1038/s41598-026-58191-w. Online ahead of print.

ABSTRACT

Heart failure (HF) is a prevalent, progressive syndrome, and a leading cause of hospitalization, mortality, and healthcare expenditure worldwide, especially among older adults. This study aimed to assess the long-term burden of mortality, rehospitalizations and their composite among HF patients and to identify independent predictors of these outcomes. A population-based retrospective cohort study was conducted across all hospitals in the province of L’Aquila, Italy, including all residents discharged alive after an index HF hospitalization between 1 January 2014 and 31 December 2022, with follow-up through 31 December 2023. Kaplan-Meier analysis estimated event rates at 30 days, 90 days, 1 year, 5 years and 9 years. Predictors of the 5-year composite outcome (death or rehospitalization) and rehospitalization frequency were analyzed using multivariable Cox regression and negative binomial models. A total of 5,883 patients were included. By 5 years, 69.4% had experienced the composite outcome, increasing to 85.4% by 9 years. Nearly half of the rehospitalizations occurred in the first year. Older age, male sex, longer initial hospital stay, and earlier discharge year were associated with poorer outcomes. HF poses a long-term burden, highlighting the need for ongoing care, in which nurses are central to improve outcomes and care quality.

PMID:42304054 | DOI:10.1038/s41598-026-58191-w

Sci Rep. 2026 Jun 16. doi: 10.1038/s41598-026-55097-5. Online ahead of print.

ABSTRACT

Recent advances in molecular tools have changed how researchers approach selective neural modulation, especially in rodent models where germline modifications and viral vector delivery are readily optimized. However, these tools have not advanced as rapidly for nonhuman primates, despite critical need for translational gene therapy models. A key barrier is targeting specific neuronal populations in the larger primate brain with cell-type and circuit specificity. Dopaminergic neurons pose a particular challenge due to their inaccessible ventral midbrain location, where local injection non-selectively targets all dopaminergic neurons. This work provides NHP researchers with a comparison of retrograde viral vectors, highlighting one which achieves efficient dopaminergic neuron transduction, establishing a foundation for combining vectors with cell-type-specific enhancers for basic and translational applications.

PMID:42304030 | DOI:10.1038/s41598-026-55097-5

Sci Rep. 2026 Jun 16. doi: 10.1038/s41598-026-57115-y. Online ahead of print.

ABSTRACT

Continued progress in inertial confinement fusion (ICF) requires solving inverse problems relating experimental observations to simulation input parameters, followed by design optimization. However, such high-dimensional dynamic PDE-constrained optimization problems are extremely challenging or even intractable. It has been recently shown that inverse problems can be solved by only considering certain robust features. Here we consider the ICF capsule’s deuterium-tritium (DT) interface, and construct a causal, dynamic, multifidelity reduced-order surrogate that maps from a time-dependent radiation temperature drive to the interface’s radius and velocity dynamics. The surrogate targets an ODE embedding of DT interface dynamics, and is constructed by learning a controller for a base analytical model using low- and high-fidelity simulation training data with respect to radiation energy group structure. After demonstrating excellent accuracy of the surrogate interface model, we use machine learning (ML) models with surrogate-generated data to solve inverse problems optimizing radiation temperature drive to reproduce observed interface dynamics. For sparse snapshots in time, the ML model further characterizes the most informative times at which to sample dynamics. Altogether we demonstrate how operator learning, causal architectures, and physical inductive bias can be integrated to accelerate discovery, design, and diagnostics in high-energy-density systems.

PMID:42304029 | DOI:10.1038/s41598-026-57115-y

Sci Rep. 2026 Jun 16. doi: 10.1038/s41598-026-58350-z. Online ahead of print.

ABSTRACT

Attendants are continuously involved in coordinating and supporting cancer care, yet their service quality perceptions are rarely modelled as determinants of satisfaction. This study investigates the association between demographic factors and service quality dimensions related to attendants’ satisfaction in oncology hospitals. A cross-sectional survey using a structured questionnaire was conducted among 480 attendants (accompanying patients) across oncology hospitals in Tamil Nadu, India. Service quality was assessed using six dimensions: tangibility, responsiveness, empathy, assurance, service reliability, and process reliability. Differences across demographic groups were evaluated using non-parametric statistical tests. Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to examine the relationships between service quality dimensions and attendant satisfaction, followed by Importance-Performance Map Analysis (IPMA) to prioritize improvement areas. Significant differences in service quality perceptions were observed across gender, age, education, residential status, financing category, and hospital visit frequency, while income and occupation did not show meaningful effects. Tangibility (β = 0.374), responsiveness (β = 0.313), and empathy (β = 0.196) demonstrated significant positive associations with satisfaction, collectively explaining 80.9% of its variance (R² = 0.809), whereas assurance, service reliability, and process reliability were not significantly associated with satisfaction. IPMA result indicate that empathy represents a key area for refinement, while tangibility and responsiveness demonstrate high importance alongside strong performance, suggesting areas to be maintained. Overall, the findings provide insights into factors associated with attendant satisfaction, improving patient-centered oncology services.

PMID:42304028 | DOI:10.1038/s41598-026-58350-z

Sci Rep. 2026 Jun 16. doi: 10.1038/s41598-026-57298-4. Online ahead of print.

ABSTRACT

Multiple sequence alignment (MSA) is a fundamental task in bioinformatics, underpinning comparative genomics, structural analysis, and evolutionary inference. However, MSA remains a challenging multi-objective optimization problem due to the need to simultaneously maximize alignment accuracy, preserve conserved regions, and control gap proliferation, particularly in large and heterogeneous sequence collections. In this work, we propose MOFSACEO-MSA, a novel hybrid optimization framework for multiple sequence alignment that integrates a fuzzy multi-objective evaluation scheme with the Equilibrium Optimizer (EO) and a Soft Actor-Critic (SAC)-based adaptive control mechanism. The proposed framework formulates MSA as a dynamic multi-objective optimization problem, in which alignment quality is assessed using complementary residue-level and column-level criteria, including Sum-of-Pairs score, column conservation, entropy, and gap statistics. Fuzzy membership functions are employed to harmonize competing objectives into a unified optimization landscape, while EO provides robust global exploration. To further enhance adaptability, SAC dynamically regulates key EO parameters during the search process, enabling an effective balance between exploration and exploitation across datasets of varying size and heterogeneity. Extensive experiments werew conducted on diverse biological sequence datasets, with a primary focus on RNA benchmarks, including structured families from Rfam, large-scale repositories from RNAcentral and GenBank, and organism-specific tRNA datasets from GtRNAdb. Comparative evaluations against classical alignment tools (ClustalW, MAFFT, MUSCLE, PRANK, KAlign, and T-Coffee), metaheuristic methods (SAGA, Sequoya and EAFSA), and a reinforcement learning-based approach (RLALIGN) demonstrate that MOFSACEO-MSA consistently achieves competitive or superior Sum-of-Pairs scores while significantly reducing gap proportions and maintaining compact alignment lengths. Notably, the proposed framework exhibits improved robustness on large and highly heterogeneous datasets, where existing methods often suffer from excessive gap insertion or unstable convergence. Overall, MOFSACEO-MSA provides a flexible and extensible optimization paradigm that effectively bridges evolutionary search and reinforcement learning for high-quality multiple sequence alignment, with demonstrated effectiveness on challenging RNA alignment tasks.

PMID:42304027 | DOI:10.1038/s41598-026-57298-4

Clin Neurol Neurosurg. 2026 Jun 10;269:109534. doi: 10.1016/j.clineuro.2026.109534. Online ahead of print.

ABSTRACT

BACKGROUND: Post-stroke vascular dementia (VaD) affects 20-30% of ischemic stroke survivors within the first year, yet existing prediction models lack comprehensive integration of novel blood biomarkers and validated risk stratification strategies. This study aimed to develop and temporally validate a risk-stratified prediction model incorporating clinical, neuroimaging, and serum biomarker variables.

METHODS: This retrospective cohort study comprised a development cohort (n = 998, 2020-2022) and temporal validation cohort (n = 249, 2023-2024). Consecutive acute ischemic stroke patients aged ≥ 18 years with available baseline magnetic resonance imaging (MRI) and 12-month follow-up were included. Candidate predictors encompassed 25 variables: demographics, vascular risk factors, stroke severity assessed by the National Institutes of Health Stroke Scale (NIHSS), neuroimaging markers (Fazekas white matter hyperintensity score, brain atrophy index [BAI]), and serum biomarkers including neurofilament light chain (NFL) and glial fibrillary acidic protein (GFAP) measured by single-molecule array (Simoa). The primary outcome was incident VaD diagnosed by NINDS-AIREN criteria at 12 months.

RESULTS: Of 1247 included patients, 354 (28.4%) developed VaD. Least absolute shrinkage and selection operator (LASSO) selected 8 predictors: age, education level, NIHSS score, Fazekas score ≥ 2, BAI, previous stroke, plasma NFL, and plasma GFAP. In the development cohort, the model demonstrated excellent discrimination (C-statistic 0.89, 95% CI 0.86-0.92) and good calibration. Bootstrap validation yielded optimism-corrected C-statistic 0.88. In temporal validation, performance remained robust (C-statistic 0.85, 95% CI 0.81-0.89). Risk stratification revealed distinct cognitive trajectories: high-risk patients (25% of cohort) exhibited 67.3% VaD incidence and steep cognitive decline (mean Montreal Cognitive Assessment [MoCA] change -6.8 points), capturing 59.3% of all VaD cases.

CONCLUSIONS: This biomarker-enhanced prediction model demonstrates excellent discrimination and calibration for post-stroke VaD. Risk stratification effectively identifies high-risk patients for targeted interventions, providing a practical tool for precision-based clinical management.

PMID:42302346 | DOI:10.1016/j.clineuro.2026.109534

Lung Cancer. 2026 Jun 14;218:109499. doi: 10.1016/j.lungcan.2026.109499. Online ahead of print.

ABSTRACT

BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed cancer therapy but can cause rare, severe cardiotoxicity. The real-world incidence and impact of a broader spectrum of cardiovascular events remains poorly defined. This study aims to evaluate the real-world incidence of cardiovascular events following ICI therapy, explore predictive biomarkers, and assess the impact of treatment interruptions.

METHODS: A retrospective observational study using Optum’s de-identified Market Clarity Data was conducted in 29,503 patients receiving ICI therapy for any cancer type with a minimum follow-up of 6 months. Cardiovascular events including myocarditis, arrhythmias, and reduced left ventricular ejection fraction (LVEF < 50%), were analyzed. Kaplan-Meier survival curves were used to evaluate the timing of these events. Biomarkers such as NT-proBNP and troponin were evaluated for their predictive value.

RESULTS: Out of 29,503 ICI-treated patients, 27.6% experienced a cardiac event during the follow-up period (2 years). Patients with pre-existing cardiovascular conditions who were receiving cardioprotective treatment prior to ICI therapy had an increased risk of cardiovascular events (35% vs 20%, p < 0.001). Patients who experienced a cardiac event had a significantly higher mortality rate (39% vs 25.4%, p < 0.001). Elevated troponin and NT-proBNP levels were associated with increased mortality (p < 0.001).

CONCLUSIONS: Cardiovascular events are frequent in ICI-treated patients, particularly in those with pre-existing cardiac conditions. Elevated troponin and NT-proBNP levels may serve as useful biomarkers for predicting post-ICI cardiovascular events. These findings demonstrate that these events significantly interrupt ICI therapy and increase mortality. They support biomarker-guided risk stratification and prompt collaboration between oncologists and cardio-oncologists to preserve treatment integrity.

PMID:42302339 | DOI:10.1016/j.lungcan.2026.109499