Genomic and epidemiological analysis of Vibrio parahaemolyticus O10:K4, a newly emerging serotype in China.

Optimizing post-printing drying conditions for point-of-care manufacturing of SSE-3D printed medicines.

Patients with stomach adenocarcinoma (STAD) have a poor prognosis, and the efficacy of immunotherapy varies widely. The present study aimed to screen effective long non-coding RNAs (lncRNAs) as molecular targets for assessing the prognosis of STAD and guiding precision immunotherapy. A total of five key prognostic senescence-related lncRNAs (SenRLs; AL139147.1, LINC02057, AC093801.1, AL353804.2 and AC005363.2) were screened using bioinformatics methods. A novel validated prognostic risk model was constructed for STAD based on the SenRLs signature. For the training, test and entire set, the 5-year area under the curve values were 0.828, 0.703 and 0.772, respectively. A nomogram combining clinical variables and risk scores effectively predicted overall survival (OS) in patients with STAD. Clinical tissue samples were collected from patients with STAD, and quantitative PCR (qPCR) was employed to assess the tissue expression of SenRLs. The qPCR results showed that the expression of LINC02057, AL139147.1 and AC093801.1 was significantly higher in STAD tissues than in paracancerous tissues. By contrast, the expression of AC005363.2 and AL353804.2 was decreased in STAD tissues. The tissue expression profiles of these lncRNAs were consistent with the bioinformatics findings. The risk groups differed in their immune infiltrating cells, immune checkpoints and susceptibility to chemotherapeutic agents. Following clustering analysis, the two clusters had distinct tumor microenvironment profiles. Notably, based on well-established immunological characteristics, Cluster 2 was identified as representing hot tumors that are presumably more likely to benefit from immunotherapy. The newly developed SenRLs signature can serve as an independent prognostic biological marker of STAD. These SenRLs can effectively distinguish between hot and cold tumors, thereby better facilitating the screening of the beneficiary population for STAD immunotherapy.

The optimization of complex manufacturing processes, such as Chemical Vapor Deposition, requires integrated approaches that combine physical modeling with advanced data-driven methodologies. This review synthesizes recent advances in hybrid modeling frameworks that merge equation-based computational fluid dynamics, machine learning, and natural language processing models to enhance process understanding, prediction, optimization and control. In particular, natural language processing techniques are leveraged to generate embedding-based predictors that inform learning tasks. The proposed framework integrates data acquisition, dimensionality reduction, and feature engineering with contextual language processing embeddings, surrogate modeling, and sensitivity analysis. This results in improved forecasting accuracy and interpretability. Key applications include coating thickness prediction, process regime classification, and critical parameter identification using SHAP analysis and Sobol’ indices. Nevertheless, significant challenges remain, including limitations in sensor infrastructure, assessment of dataset sufficiency for specific industrial objectives, and restricted generalizability across reactor designs. This work highlights how hybrid frameworks, together with natural language processing models applied to industrial process datasets, can bridge the gap between data availability in industrial environments and the actionable insights required for practical implementation, while identifying necessary future directions for robust, scalable, and interpretable modeling systems in advanced manufacturing. • Hybrid framework that unifies CFD, machine learning, and NLP for industrial CVD. • Dimensionality reduction and surrogate models accelerate predictive CVD analytics. • Clustering and feature-importance analysis reveal dominant process mechanisms. • NLP-based encoding enhances predictive modeling of categorical industrial variables.

A quantifiable grading standard for Codonopsis Radix based on the multidimensional quality evaluation system of phenotype, chemistry, and bioactivity.

Classification of martian rocks and soils at Tianwen-1 landing site based on laser-induced breakdown spectroscopy data of MarSCoDe.

Geochemical assessment of potentially toxic elements in river sediments from a highly contaminated mining area - Cavnic, Romania.

Aberrant hippocampal-cortical connectivity and network coupling in facial emotion recognition-based subtypes of depression.

A new paradigm for the discrimination of Polygonatum geographical authenticity based on the coupling of stable isotope and secondary metabolites

Health and digital health literacy and self-rated health in migrants residing in Portugal.

Expenditure structures and operating patterns of non-profit preschools in Taiwan, 2014-2018: A five-year longitudinal study.

Revealing ROC-derived optimal thresholds of specific seminal plasma proteins as markers of bull semen freezability.

Machine learning-empowered nanozyme-based aptasensor arrays for accurate discrimination and sensitive quantification of kynurenine pathway metabolites.

Chemical pollution alters detoxification capacity and behavior in the native freshwater fish Galaxias maculatus under in situ exposure.

Single-probe Pd-modified ZnO-polymer fluorescent sensor array for discrimination of thymidine analogues in aqueous media.

Effects of obacunone against myocardial fibrosis: Mechanistic insights from network pharmacology and experimental validation.

Network and cluster analyses reveal the central role of disability and the presence of nociplastic pain features in cervical radiculopathy.

The Centers for Medicare and Medicaid Services (CMS) now require the collection of preoperative and postoperative patient-reported outcome measures (PROMs) for elective inpatient Medicare total joint arthroplasty procedures. There are concerns CMS will utilize the percentage of patients meeting the substantial clinical benefit (SCB) threshold in determining hospital reimbursement. However, understanding of what drives SCB achievement remains limited. Using a prospectively collected registry of total knee arthroplasty patients between January 2020 and June 2023, eligible patients were categorized into two groups, designated "improvement" and "nonimprovement," based on their attainment or lack of attainment of the established SCB threshold. Cohort demographics and preoperative/postoperative PROMs were compared. Cluster analyses were performed to evaluate for patient characteristics impacting SCB achievement status. In total, 1,240 of 1,860 patients (66.7%) completed preoperative and postoperative PROMs data and met inclusion criteria for this study. Patients achieving SCB represented 66.1% of included patients (820 of 1,240). There were very few demographic differences between "improvement" and "nonimprovement" groups; the improvement group was notable for more women (66.6 versus 58.3%, P = 0.02). Cluster analyses revealed characteristics associated with likely SCB achievement included younger age, non-White race, Hispanic/Latino ethnicity, lower level of education, and worse preoperative pain and functioning scores. The implication of PROMs and SCB in reimbursement within the CMS patient population has far-reaching consequences across the field of arthroplasty. We found that Medicaid insurance and lower education level (factors associated with lower socioeconomic status) were not associated with failure to achieve SCB. In addition, non-White race and Hispanic/Latino ethnicity were associated with SCB achievement, indicating that these policies may not be exacerbating existing inequities in arthroplasty care. Further work is necessary to maximize PROMs collection and SCB achievement while prioritizing patient outcomes. Prognostic level III.

This study investigates changes in NK cell subsets in the blood of NPC patients and explores JAB1's role in shaping the tumor immune environment. We performed RNA sequencing analyses on NPC PBMCs and tissue samples to identify genes associated with JAB1. Dimensionality reduction and clustering analyses were conducted on paired single-cell RNA sequencing data to explore differences in NK cell subsets. Functional assays assessed the roles of these subsets in various immune environments. Flow cytometry characterised NK cell subsets and cytokine profiles. A humanised immune system mouse model with NPC xenografts supported our findings. Higher levels of CD16 + CD57 + NK cells in blood correlated with better patient outcomes, while increased CD16- NK cells indicated worse prognoses. JAB1 enhanced NK cell cytotoxicity, indicating its role in immune regulation. NK subsets showed distinct distributions: CD16hiCD57- and CD16hiCD57+ cells were mainly in blood, while CD16loCD57- cells accumulated in tumors. Functional tests revealed some subsets promoted tumor growth while others suppressed it. Expression of JAB1 and CD107a in NK cells demonstrated superior diagnostic and prognostic value compared to traditional tumor markers like SCC and CEA. This study identifies key roles of NK cell subsets and JAB1 in NPC immunity, offering insights for biomarker and immunotherapy target development.

This study examines the diversity of entrepreneurial intentions among university students by extending the construct to include business versus social orientations and founding (“build”) versus participation (“activity”) roles. A cross-sectional survey was conducted with first-year students in Japan. Cluster analysis revealed three student types: Value-Initiator , Value-Contributor , and Traditional-worker . To understand each type's characteristics, the study compared their profiles using Theory of Planned Behavior (TPB), entrepreneurial competence based on EntreComp framework, and prior educational experience. Value-Initiator type exhibited high intentions across business and social domains, with strong attitudes, social norms, and initiative-oriented competencies. The Value-Contributor type showed high activity intentions but lower build intentions, indicating motivation for team-based value creation rather than for leadership. The Traditional-worker type demonstrated low entrepreneurial intentions, reflecting limited engagement with entrepreneurial or social initiatives. Comparative analysis revealed business practical education was strongly associated with higher build intentions, while social practical education mainly influenced activity intentions. These findings suggest entrepreneurship education should adopt differentiated approaches: experiential business practice to foster Value-Contributor into Value-Initiator, and career design education to encourage Traditional-workers to explore entrepreneurial mindsets. This study contributes to entrepreneurship education by proposing a multidimensional model of entrepreneurial intention and demonstrating pedagogical implications for an inclusive learning design. • Expands entrepreneurial intention into business/social and build/activity roles. • Identify three student types: Value-Initiator, Value-Contributor, and Traditional-worker. • Identify the characteristics of each type by TPB, EntreComp, and prior education. • Suggests differentiated pedagogical strategies for diverse student profiles.