四川中医药高等专科学校

AIM:This study aims to explore the views and experiences of people with dementia, informal caregivers and professionals regarding eating and drinking difficulties. DESIGN:A qualitative systematic review was conducted. METHODS:The Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines were used to conduct this systematic review. The quality of the included studies was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Qualitative Research, and the data were thematically synthesised using Thomas and Harden's three-stage method. DATA SOURCES:Six electronic databases (PubMed, EMBASE, Cochrane Library, Web of Science, CINAHL and PsycINFO) were searched from their respective inception dates to August 2025 to identify relevant studies. RESULTS:Thematic analysis of the 16 included studies identified four key themes: (1) Physiological and psychological changes in people with dementia and caregivers; (2) factors influencing eating and drinking in people with dementia; (3) needs and recommendations for people with dementia, informal caregivers and professionals; (4) selection of eating methods for end-stage people with dementia. CONCLUSIONS:Eating and drinking difficulties affect the well-being of both patients and caregivers. A good dining environment improves mealtime pleasure but demands caregivers' time and energy. All parties emphasised the importance of effective communication. In end-stage dementia, professional assistance is crucial for enteral nutrition decisions. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE:Collaboration among patients, caregivers and professionals is vital for creating tailored nutritional plans and improving mealtime environments, thereby enhancing nutritional intake. In advanced dementia, providers must provide balanced information on comfort feeding versus enteral nutrition to aid decision-making. IMPACT:What problems were addressed in this study? This study addressed the lack of a consolidated, tri-perspective understanding of eating and drinking difficulties in dementia care settings. What are the main findings? Four key themes were identified: physiological and psychological changes, influencing factors, stakeholder needs and end-of-life decision-making. Where and on whom will the research have an impact? This will impact care practices for people with dementia and inform the training and support of informal caregivers and healthcare professionals.

BackgroundEndothelial dysfunction is an early vascular abnormality in adults with overweight or obesity. Although exercise can improve vascular function, the most favorable exercise modality and weekly dose for improving brachial artery flow-mediated dilation (brachial FMD) remain unclear. This study compared exercise modalities and weekly dose-response relationships for brachial FMD.MethodsWe conducted a systematic review and Bayesian dose-response network meta-analysis of parallel-group randomized controlled trials. Eligible trials enrolled adults with overweight or obesity, compared exercise lasting at least 4 weeks with non-exercise control or another modality, and reported pre- and post-intervention brachial FMD. Exercise dose was expressed as MET-min/week. Effects were expressed as mean differences (MDs) in change in brachial FMD. Eight dose-response functions were compared using MBNMAdose in R, and certainty of evidence was assessed with CINeMA.ResultsForty-seven trials (2,211 participants) were included. The quadratic random-effects model provided the best fit (DIC = 322.6). Overall, the largest model-estimated improvement occurred at 1,500 MET-min/week (MD: 4.72 percentage points, 95% CrI: 3.43, 5.91). Among modalities, combined aerobic and resistance training (AE-RT) showed the largest estimated improvement at 1,500 MET-min/week (MD: 5.77, 95% CrI: 4.04, 7.52). AE-RT at 1,200–1,600 MET-min/week was most consistently ranked among the highest predicted responses, with 1,400 MET-min/week ranked first. Subgroup patterns were broadly consistent across baseline brachial FMD, BMI, health status, and duration. Sensitivity analysis excluding high-risk-of-bias trials remained consistent (1,500 MET-min/week; MD: 4.84, 95% CrI: 3.53, 6.05). Meta-regression did not identify statistically clear effect modification by age, baseline brachial FMD, intervention length, or baseline BMI. Across all 13 network comparisons, the certainty of evidence was low or very low. The mind-body exercise optimum lay below the observed range, an unreliable extrapolation.ConclusionIn adults with overweight or obesity, exercise showed dose-dependent improvement in brachial FMD, with AE-RT around 1,500 MET-min/week showing the most favorable model-estimated profile. These estimates should be interpreted as provisional, model-based prescription anchors rather than ready-to-apply clinical thresholds, and should be individualized according to clinical status, exercise tolerance, and safety considerations. The low-to-very-low certainty of evidence warrants cautious interpretation and confirmation in higher-quality head-to-head randomized trials using standardized brachial FMD protocols.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD420261402756, PROSPERO CRD420261402756.

Background and Objective Lung cancer is the leading cause of cancer-related death worldwide, with a particularly high mortality risk among patients admitted to the intensive care unit (ICU). Accurate and timely prediction of in-hospital mortality in this population is critical for clinical decision-making and resource allocation. However, existing severity scoring systems have demonstrated limited discriminative performance in this specific context. This study aimed to develop and validate an interpretable machine learning (ML) model for in-hospital mortality prediction in ICU patients with lung cancer using real-world clinical data from the MIMIC-IV(Medical Information Mart for Intensive Care IV) database. Materials and Methods Retrospective cohort data were extracted from the MIMIC-IV database. A total of 1,120 ICU admissions of lung cancer patients were included (239 deaths, 21.3%). Candidate variables were systematically screened through univariate analysis, variance inflation factor (VIF) assessment, and bidirectional stepwise logistic regression-all performed exclusively on the training set (70%, n = 785) to prevent data leakage. Eight machine learning algorithms were developed: Logistic Regression (LR), Random Forest (RF), Neural Network (NN), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Adaptive Boosting (AdaBoost), and Decision Tree (DT). Model performance was evaluated on an independent test set (30%, n = 335) using six metrics-Brier score, calibration slope, F1 score, Youden index, area under the receiver operating characteristic curve (AUC), and accuracy (ACC)-with a composite scoring system to rank models. Interpretability was assessed using SHapley Additive exPlanations (SHAP). Clinical utility was evaluated through decision curve analysis (DCA) and comparison with traditional severity scores (Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score II (SAPS II), and Oxford Acute Severity of Illness Score (OASIS)). Subgroup and robustness analyses were also conducted. Results Five variables were selected as final predictors: SOFA score, SAPS II score, OASIS score, Charlson Comorbidity Index (CCI), and minimum peripheral oxygen saturation (SpO2). The Logistic Regression model achieved the highest comprehensive score (42/48), with an AUC of 0.791 (95% CI: 0.731-0.850), Brier score of 0.137, F1 score of 0.550, Youden index of 0.482, accuracy of 0.746, and calibration slope of 0.801. The LR model significantly outperformed SOFA (AUC: 0.749, P = 0.034), SAPS II (AUC: 0.719, P = 0.011), and OASIS (AUC: 0.678, P < 0.001). SHAP analysis identified SOFA score as the dominant predictor, with SpO2contributing a negative influence. The model demonstrated stable and consistent performance across subgroups (mechanical ventilation, age, sex) and across 50 repeated random splits. DCA confirmed the model's net clinical benefit at threshold probabilities of 0.10-0.70. Conclusion We developed an explainable, LR-based mortality prediction model for ICU lung cancer patients, which outperforms traditional severity scoring systems and provides individual-level clinical interpretability through SHAP values. This model offers a practical and transparent tool to support critical care decision-making in this high-risk population.

BackgroundRoot-knot nematodes (RKNs) pose a severe threat to Trichosanthes kirilowii production, but the molecular mechanisms of its response to RKN infection remain unclear.MethodsAn integrated multi-omics strategy that combined physiological trait analysis, hormone profiling, transcriptome sequencing, and untargeted metabolome analysis was used to systematically clarify the response mechanisms of T. kirilowii to Meloidogyne incognita infection.ResultsComprehensive phenotypic observations combined with antioxidant enzyme activity measurements and hormone profiling identified 6 days post-inoculation (dpi) as a critical response timepoint, characterized by initial gall formation, minimum superoxide dismutase (SOD) activity, peak catalase (CAT) activity, and maximal content of auxin (IAA), abscisic acid (ABA), and salicylic acid (SA). Notably, cytokinin-type hormones were significantly upregulated after RKN infection, with zeatin increasing by 202% at 12 dpi and zeatin riboside reaching 38.5-fold that of the control at 24 dpi. Transcriptomic analysis identified 1,705 differentially expressed genes (DEGs), predominantly enriched in plant hormone signal transduction, zeatin biosynthesis, and plant-pathogen interaction pathways. Untargeted metabolomic analysis identified 658 differentially accumulated metabolites (DAMs), primarily involving carboxylic acid derivatives, amino acids, phospholipids, and isopentenyl alcohol esters; combined analysis further revealed that zeatin biosynthesis was the only significantly enriched common pathway; within this pathway, changes in 8 key genes and 5 core metabolites acted synergistically and were significantly correlated with gall number, soluble sugar content, and multiple hormones.ConclusionsThe results indicate that T. kirilowii responds to RKN infection through an integrated mechanism involving physiological regulation, hormonal coordination, metabolic reprogramming, and molecular defense, with the zeatin biosynthesis pathway serving as a central hub. These findings provide a basis for molecular breeding and develop green control strategies against RKNs in T. kirilowii cultivation.