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PubMed2026

Meta-analysis and pharmacoeconomic study of rasagiline versus selegiline in the treatment of Parkinson's disease.

OBJECTIVE: Given the persistent absence of direct head-to-head trials, this study aimed to evaluate the comparative efficacy, safety, and cost-effectiveness of rasagiline versus selegiline as early-stage monotherapy for Parkinson's disease (PD), informing clinical selection and healthcare policies in China. METHODS: A systematic search of PubMed, Embase, and the Cochrane Library identified randomized controlled trials (RCTs) up to April 2026. Focusing on short-term outcomes (10-16 weeks), an adjusted indirect treatment comparison (ITC) using placebo as a common anchor evaluated symptom improvement (UPDRS total scores) and adverse event (AE) incidence. For economic evaluation, a 2-year Markov model was constructed from a Chinese healthcare-system perspective. The incremental cost-effectiveness ratio (ICER) was calculated alongside robust sensitivity analyses. RESULTS: Ten RCTs (rasagiline: 6; selegiline: 4) were included. The ITC revealed no statistically significant differences between rasagiline and selegiline in short-term symptomatic relief (Mean Difference = -0.82, 95% CI [-2.08, 0.44], p = 0.203) or AE risk (Odds Ratio = 0.83, 95% CI [0.50, 1.38], p = 0.475). The overall evidence certainty was rated as moderate. Economically, the base-case simulation indicated rasagiline yielded a marginal benefit of 0.0088 QALYs over selegiline but incurred an additional 17,111.10 Yuan. This resulted in an ICER of 1,951,505.55 Yuan/QALY, substantially exceeding the conventional willingness-to-pay threshold. CONCLUSION: Supported by moderate-certainty evidence, rasagiline and selegiline provide comparable short-term efficacy and safety for early-stage PD monotherapy. However, at its current pricing, rasagiline is not cost-effective. Significant price reductions or definitive proof of long-term superiority are required to justify its economic value.

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PubMed2026

Advancing nursing education through social and emotional learning: A systematic review guided by the Collaborative for Academic, Social, and Emotional Learning framework.

BACKGROUND: With Generation Z entering the nursing workforce in growing numbers, strengthening social and emotional learning is critical for academic success, professional adaptation, and safe practice. However, the existing evidence remains fragmented because of varied interventions and inconsistent approaches. OBJECTIVES: This systematic review examined (1) the social and emotional learning essential for nursing students and nurses within the Collaborative for Academic, Social, and Emotional Learning framework, (2) their impact on educational and clinical outcomes, and (3) implications for advancing nursing education and practice. METHODS: Following Joanna Briggs Institute methodology and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, five international (PubMed, EMBASE, CINAHL, PsycINFO, Cochrane) and three Korean (RISS, KoreaMed, KMBASE) databases were searched up to June 2025. Eighteen studies involving 2,952 participants met the inclusion criteria, including quasi-experimental quantitative studies, descriptive quantitative studies, qualitative studies, and mixed-methods studies. The methodological quality of the included studies was appraised using the Mixed Methods Appraisal Tool. RESULTS: Within the Collaborative for Academic, Social, and Emotional Learning framework, relationship skills and self-management were the most frequently studied competencies, emphasizing teamwork, communication, and stress regulation. Self-awareness and social awareness were underexplored, despite their importance in empathy, resilience, and reflective practice. Responsible decision-making was the least studied competency, despite its importance in ethical reasoning. Social and emotional learning was consistently associated with enhanced adaptation, communication, leadership, relationships, and clinical performance. Effective strategies included blended learning, simulation, reflective activities, and mentorship, which are aligned with Generation Z's learning preferences. CONCLUSION: Although social and emotional learning integration is associated with improvements in educational and clinical outcomes in nursing, current research has largely centered on relational and stress-related competencies while underrepresenting responsible decision-making. To cultivate reflective, empathetic, and ethically grounded nurses, curricula should integrate social and emotional learning through a balanced and structured approach. REGISTRATION: This study was registered on PROSPERO (ID: CRD420251005683).

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PubMed2026

Beyond antigen matching: compatibility intelligence theory for transfusion as an emergent biological system.

BACKGROUND: Despite major advances in serologic testing, extended phenotyping, and blood group genomics, clinically similar transfusion exposures may result in markedly different immune and clinical outcomes. Existing compatibility strategies do not fully explain this biological variability. OBJECTIVES: To examine transfusion compatibility as an emergent donor-recipient biological state and propose a systems-level conceptual framework that integrates established biological determinants into a testable model for future precision transfusion medicine. METHODS: This narrative review critically synthesizes current evidence from blood group genomics, recipient immunobiology, inflammation, disease-specific biology, transfusion medicine, and computational prediction. The proposed framework distinguishes Compatibility Intelligence Theory (CIT) as a biological interpretation from Precision Transfusion Intelligence (PTI) as its potential clinician-supervised translational application. RESULTS: The review argues that transfusion compatibility is shaped by interactions among donor genetics, recipient immune biology, inflammatory physiology, disease context, transfusion history, and longitudinal adaptation rather than by antigen matching alone. CIT provides an organizational framework for integrating these determinants, whereas PTI describes a possible clinician-supervised translation. To address current feasibility, the revised framework separates variables into routinely measurable, contextually available but incompletely standardized, and research-stage domains, and proposes a staged strategy for deriving rather than assuming their quantitative weights. Any clinical implementation would require comparative validation against current serologic, phenotypic, and genotype-based practice. CONCLUSIONS: Compatibility Intelligence Theory offers a testable systems-level framework for understanding transfusion compatibility without replacing established transfusion practices. The framework is not presented as a ready-to-use score: currently measurable variables can be organized for structured risk review, whereas inflammatory, immunogenetic, and multi-omic inputs require prospective standardization and validation. If future studies demonstrate incremental predictive and patient-centered benefit, CIT-informed PTI could support an adaptive, evidence-based extension of current precision transfusion practice.

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PubMed2026

A study on the differences in recovery effects of different types of nutritional supplements on competitive performance of esports athletes under mental fatigue.

BACKGROUND: To compare the effects of different nutritional supplements on the recovery of core competitive performance abilities in esports athletes following mental fatigue and to observe changes in the autonomic nervous system during recovery after nutritional supplementation by monitoring heart rate variability (HRV). METHODS: A randomized crossover within-subject controlled experimental design was adopted, including nutritional supplement type (caffeine, nitrate, Ginkgo biloba extract, catechins, placebo) × mental fatigue state (initial, fatigued, post-supplementation). Twenty high-level first-person shooter (FPS) esports athletes were recruited. Mental fatigue was induced using a Stroop task. After ingesting the different supplements and resting for 60 minutes, the participants completed assessments of shooting accuracy, shooting stability, spatial localization, and multitasking ability using the KovaaK's simulation trainer. HRV indices were also recorded to evaluate changes in autonomic regulation. RESULTS: For shooting accuracy, compared with the placebo condition, all four supplements significantly improved shooting accuracy scores following mental fatigue (all p < 0.05); however, no significant differences were observed among the effects of the different supplements. For shooting stability, caffeine, nitrate, and catechins produced significant recovery effects on shooting stability (all p  < 0.05); however, no significant differences were observed among the effects of these three supplements. For spatial localization and multitasking ability, the improvements in these two abilities in the post-supplementation state may have resulted from natural recovery, and none of the four nutritional supplements demonstrated a significant recovery effect. The HRV results showed that indices including RMSSD and SDNN changed under some supplement conditions. CONCLUSIONS: Mental fatigue significantly reduced the competitive performance of esports athletes. Four types of nutritional supplements all promoted the recovery of shooting accuracy, while caffeine, nitrate, and catechins promoted the recovery of shooting stability. However, no additional recovery advantages of the nutritional supplements over placebo were identified for spatial localization or multitasking ability. Changes in HRV may reflect changes in autonomic regulation during recovery, but further research is still warranted.

Humans↗

PubMed2026

Machine learning-based prediction of unplanned readmission and construction of an online calculator for elderly patients with mild ischemic stroke.

OBJECTIVE: To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and validate an online risk prediction calculator based on an interpretable machine learning model, thereby providing a promising practical tool for accurate clinical assessment of 30‑day all‑cause unplanned readmission risk in this population. METHODS: A prospective cohort study was conducted, including 1050 patients aged ≥ 60 years with mild ischemic stroke admitted between August 2023 and September 2024. Participants were randomly divided into a training set (840 cases) and a test set (210 cases) at a ratio of 8:2. Risk factors were screened by univariate analysis and multivariable Logistic regression. Four machine learning models, namely LightGBM, XGBoost, Random Forest, and K‑Nearest Neighbors (KNN), were developed and their performance was evaluated using AUC, accuracy, sensitivity, and specificity as metrics. The SHAP framework was used for interpretability analysis, and an online calculator was subsequently developed based on the optimal model. RESULTS: Univariate analysis showed significant differences (P < 0.05) in 13 factors including age, smoking, AIP, TyG index, HALP score, etc. Multivariable Logistic regression identified age (OR = 9.752), smoking (OR = 5.171), AIP (OR = 6.691), TyG index (OR = 4.393), HALP score (OR = 2.831), and ≥ 2 comorbidities (OR = 3.664) as independent risk factors. All four machine learning models demonstrated good predictive performance. Based on a comprehensive evaluation of multiple metrics and computational efficiency, the LightGBM model exhibited the best predictive performance (AUC = 0.884, accuracy = 0.829, sensitivity = 0.812, specificity = 0.875). SHAP analysis showed that age, AIP, TyG index, smoking, and HALP score were key predictors. An online calculator developed based on this model enables individualized risk predictions. CONCLUSION: Key risk factors associated with 30‑day unplanned readmission in elderly patients with mild ischemic stroke were identified. The LightGBM model demonstrated high predictive accuracy, and together with the interpretability analysis and online calculator, offers a practical tool to support clinical risk assessment. However, this tool requires future external validation.

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