Sarcopenia is a disease of gradual loss of muscle mass in elderly people, and the most common treatment options include nutritional supplementation and exercise. Vitamin D has potential beneficial effects for skeletal muscle tissue and has often been included in nutritional therapy formulations. However, the therapeutic effect of vitamin D for the treatment of sarcopenia has not yet been determine and there is a lack of high-quality supporting evidence. We searched three databases for randomized controlled trials (RCTs) on this topic. Changes in hand grip strength, gait speed, chair-stand test, fat mass, relative skeletal muscle index, and muscle mass were assessed for analysis. Network meta-analysis was further employed, based on the frequentist approach. Outcomes were reported as weighted mean differences (WMD) with 95% confidence intervals (CIs). A total of 9 RCTs (n = 1420) met our eligibility criteria, which treated patients with vitamin D (D), protein (P, n = 165), exercise (E, n = 124), iso-caloric product (I, n = 226), usual care without nutritional supplement (n = 65), P + D (n = 467), D + E (n = 72), P + E (n = 69), D + E + I (n = 73), and P + D + E (n = 159). The pooled estimate showed that the P + D + E intervention induced a greater improvement in hand grip strength than iso-caloric product intervention (WMD = 3.86; 95%CI, 0.52–7.21). Vitamin D intervention could lead to shorter chair-stand time (WMD = −1.32; 95%CI, −1.98 to −0.65), but no significant findings could be found for gait speed and muscle mass outcomes. Our synthesis found that combining vitamin D supplementation with protein supplementation and exercise can significantly increase grip strength and also showed a trend toward increasing muscle mass. This result implies that adding vitamin D to a standard treatment protocol for sarcopenia may be helpful for regaining function.

Background: Developing a stroke health-education mobile app (SHEMA) and examining its effectiveness on improvement of knowledge of stroke risk factors and health-related quality of life (HRQOL) in patients with stroke. Methods: We recruited 76 stroke patients and randomly assigned them to either the SHEMA intervention (n = 38) or usual care where a stroke health-education booklet was provided (n = 38). Knowledge of stroke risk factors and HRQOL were assessed using the stroke-knowledge questionnaire and European Quality of Life–Five Dimensions (EQ-5D) questionnaire, respectively. Results: Sixty-three patients completed a post-test survey (the SHEMA intervention, n = 30; traditional stroke health-education, n = 33). Our trial found that patients’ mean knowledge score of stroke risk factors was improved after the SHEMA intervention (Mean difference = 2.83; t = 3.44; p = .002), and patients’ knowledge was also improved in the after traditional stroke health-education (Mean difference = 2.79; t = 3.68; p = .001). However, patients after the SHEMA intervention did not have significantly higher changes of the stroke knowledge or HRQOL than those after traditional stroke health-education. Conclusions: Both the SHEMA intervention and traditional stroke health-education can improve patients’ knowledge of stroke risk factors, but the SHEMA was not superior to traditional stroke health-education. Trial registration: NCT02591511 Verification Date 2015-10-01.

(1) Although emerging evidence suggests that proton pump inhibitor (PPI)-induced dysbiosis negatively alters treatment response to immune checkpoint inhibitors (ICIs) in cancer patients, no study systematically investigates the association between PPIs, ICIs, and chemotherapy; (2) Cochrane Library, Embase, Medline, and PubMed were searched from inception to 20 May 2022, to identify relevant studies involving patients receiving ICIs or chemotherapy and reporting survival outcome between PPI users and non-users. Survival outcomes included overall survival (OS) and progression-free survival (PFS). Network meta-analyses were performed using random-effects models. p-scores, with a value between 0 and 1, were calculated to quantify the treatment ranking, with a higher score suggesting a higher probability of greater effectiveness. We also conducted pairwise meta-analyses of observational studies to complement our network meta-analysis; (3) We identified 62 studies involving 26,484 patients (PPI = 8834; non-PPI = 17,650), including non-small cell lung cancer (NSCLC), urothelial carcinoma (UC), melanoma, renal cell carcinoma (RCC), hepatocellular carcinoma (HCC), and squamous cell carcinoma (SCC) of the neck and head. Eight post-hoc analyses from 18 randomized–controlled trials were included in our network, which demonstrated that, in advanced NSCLC and UC, patients under ICI treatment with concomitant PPI (p-score: 0.2016) are associated with both poorer OS (HR, 1.49; 95% CI, 1.37 to 1.67) and poorer PFS (HR, 1.41; 95% CI, 1.25 to 1.61) than those without PPIs (p-score: 1.000). Patients under ICI treatment with concomitant PPI also had poorer OS (HR, 1.18; 95% CI, 1.07 to 1.31) and poorer PFS (HR, 1.30; 95% CI, 1.14 to 1.48) in comparison with those receiving chemotherapy (p-score: 0.6664), implying that PPIs may compromise ICI’s effectiveness, making it less effective than chemotherapy. Our pairwise meta-analyses also supported this association. Conversely, PPI has little effect on patients with advanced melanoma, RCC, HCC, and SCC of the neck and head who were treated with ICIs; (4) “PPI-induced dysbiosis” serves as a significant modifier of treatment response in both advanced NSCLC and UC that are treated with ICIs, compromising the effectiveness of ICIs to be less than that of chemotherapy. Thus, clinicians should avoid unnecessary PPI prescription in these patients. “PPI-induced dysbiosis”, on the other hand, does not alter the treatment response to ICIs in advanced melanoma, RCC, HCC, and SCC of the head and neck.

Learning self-efficacy, defined as learners’ confidence in their capability to learn specific subjects, is crucial for the enhancement of academic progress, because it is positively correlated with academic achievements and effective learning strategy use. In this study, we developed a universal scale called the Learning Self-Efficacy Scale (L-SES) for Clinical Skills for undergraduate medical students and validated it through item analysis and content validity index (CVI) calculation. The L-SES was developed based on the framework of Bloom’s taxonomy, and the questions were generated through expert consensus and CVI calculation. A pilot version of the L-SES was administered to 235 medical students attending a basic clinical skills course. The collected data were then examined through item analysis. The first draft of the L-SES comprised 15 questions. After expert consensus and CVI calculation, 3 questions were eliminated; hence, the pilot version comprised 12 questions. The CVI values of the 12 questions were between .88 and 1, indicating high content validity. Moreover, the item analysis indicated that the quality of L-SES reached the qualified threshold. The results showed that the L-SES scores were unaffected by gender (t = −0.049; 95% confidence interval [−.115, .109], p > .05). The L-SES is a short, well-developed scale that can serve as a generic assessment tool for measuring medical students’ learning self-efficacy for clinical skills. Moreover, the L-SES is unaffected by gender differences. However, additional analyses in relevant educational settings are needed.

Background: Artificial intelligence (AI) for gastric cancer diagnosis has been discussed in recent years. The role of AI in early gastric cancer is more important than in advanced gastric cancer since early gastric cancer is not easily identified in clinical practice. However, to our knowledge, past syntheses appear to have limited focus on the populations with early gastric cancer. Objective: The purpose of this study is to evaluate the diagnostic accuracy of AI in the diagnosis of early gastric cancer from endoscopic images. Methods: We conducted a systematic review from database inception to June 2020 of all studies assessing the performance of AI in the endoscopic diagnosis of early gastric cancer. Studies not concerning early gastric cancer were excluded. The outcome of interest was the diagnostic accuracy (comprising sensitivity, specificity, and accuracy) of AI systems. Study quality was assessed on the basis of the revised Quality Assessment of Diagnostic Accuracy Studies. Meta-analysis was primarily based on a bivariate mixed-effects model. A summary receiver operating curve and a hierarchical summary receiver operating curve were constructed, and the area under the curve was computed. Results: We analyzed 12 retrospective case control studies (n=11,685) in which AI identified early gastric cancer from endoscopic images. The pooled sensitivity and specificity of AI for early gastric cancer diagnosis were 0.86 (95% CI 0.75-0.92) and 0.90 (95% CI 0.84-0.93), respectively. The area under the curve was 0.94. Sensitivity analysis of studies using support vector machines and narrow-band imaging demonstrated more consistent results. Conclusions: For early gastric cancer, to our knowledge, this was the first synthesis study on the use of endoscopic images in AI in diagnosis. AI may support the diagnosis of early gastric cancer. However, the collocation of imaging techniques and optimal algorithms remain unclear. Competing models of AI for the diagnosis of early gastric cancer are worthy of future investigation. Trial Registration: PROSPERO CRD42020193223; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=193223