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Background: Depression post-myocardial infarction (MI) is becoming more prevalent. The gut-brain axis (GBA), influenced by the gut microbiota, is a critical component in understanding depression post-MI. Despite the well-established connection between gut microbiota and depression post-MI, this relationship remains incompletely understood. Methods and analysis: This protocol will follow the Preferred Reporting Items for Systematic Review and Meta-analysis Protocol (PRISMA-P) 2020 statement. Beginning from inception to October 2023, a systematic search will be conducted across eight electronic databases, including PubMed, MEDLINE, Scopus, Embase, Cochrane Clinical Trials Database, Web of Science, China National Knowledge Infrastructure, and China Biomedical Literature Database. Pre-selected studies will be independently assessed by two researchers following a standard inclusion, data extraction and quality assessment protocol. The primary outcome measures are differences in the profile of gut microbiota and rating scale scores for depression. Fixed-effects models will be used when both clinical heterogeneity and statistical heterogeneity are low, otherwise random-effects models will be used. Furthermore, subgroup analyses will be conducted on the depression severity of the participants using the same psychiatric scales employed, study type and geographic region. Random forest plot runs and research-related statistical analyses will be carried out using Rev Man V.5.3 software. Expected results: This study will identify the association between the gut microbiota and the onset of depression post-MI, and provide evidence for the use of probiotics as an adjunctive treatment for depression post-MI. Trial registration: Prospero registration number:CRD42023444026. [ABSTRACT FROM AUTHOR]

With the popularity of Coronavirus disease 2019 (COVID‐19) vaccine and the development of vaccination strategies, the impact of COVID‐19 vaccine on cancer patients receiving immune checkpoint inhibitors (ICIs) is still unclear. In the systematic review and meta‐analysis of patients with ICIs, we assessed the serological response of cancer patients receiving COVID‐19 vaccine, and explored the risk of immune related adverse events (irAEs). We searched PubMed, EMBASE and Cochrane Library as of 10 June 2023, and included cancer patients who received ICIs and COVID‐19 vaccine. The systematic review and meta‐analysis include cohort study, cross‐sectional study and case report. The outcome included the serological response, Spike‐specific T‐cell response, irAEs and rare adverse events. When possible, the data were analysed by random effect analysis, and the statistical heterogeneity was assessed by Q‐test and I2 statistics. We explored the sources of heterogeneity through L'Abbe plots, Galbraith radial plots, and sensitivity analysis. The publication bias was evaluated by Egger's, Begg's linear regression test and funnel plot, and the impact of publication bias was further analysed by trim and fill method. 27 studies were eligible (19 cohort studies, 1 cross‐sectional study and 7 case reports), involving 8331 patients (with 4724 receiving ICIs). Most studies used mRNA vaccine (BNT162b2 or mRNA‐1273). Compared with cancer patients receiving chemotherapy, cancer patients receiving ICIs were significantly more likely to have seroconversion (RR = 1.05, 95%CI 1.01–1.10, P = 0.02). There were no statistically significant differences in seroconversion rates when comparing cancer patients receiving ICIs with controls without cancer (RR = 0.95, 95% CI 0.89–1.01, P = 0.09) or with cancer patients receiving targeted therapy (RR = 1.05, 95% CI 0.79–1.39, P = 0.75). The incidence of irAEs in patients receiving ICIs before and after COVID‐19 vaccination was (21.96%, 95%CI 16.66%–28.94%) and (14.88%, 95%CI 8.65%–25.57%), respectively. The most common irAEs were endocrine abnormalities, skin disorders, etc. The certainty of evidence was low in cancer patients with ICIs, compared with those receiving chemotherapy, and very low versus controls without cancer. Cancer patients treated with ICIs seem to be able to receive COVID‐19 vaccine safely without increasing the incidence of irAEs. [ABSTRACT FROM AUTHOR]

Background: Triglyceride-glucose (TyG) index is a surrogate marker of insulin resistance and metabolic abnormalities, which is closely related to the prognosis of a variety of diseases. Patients with both CHD and depression have a higher risk of major adverse cardiovascular and cerebrovascular events (MACCE) and worse outcome. TyG index may be able to predict the adverse prognosis of this special population., Methods: The retrospective cohort study involved 596 patients with both CHD and depression between June 2013 and December 2023. The primary outcome endpoint was the occurrence of MACCE, including all-cause death, stroke, MI and emergent coronary revascularization. The receiver operating characteristic (ROC) curve, Cox regression analysis, Kaplan-Meier survival analysis, and restricted cubic spline (RCS) analysis were used to assess the correlation between TyG index and MACCE risk of in patients with CHD complicated with depression., Results: With a median follow-up of 31 (15-62) months, MACCE occurred in 281(47.15%) patients. The area under the ROC curve of TyG index predicting the risk of MACCE was 0.765(0.726-0.804) ( P <0.01). Patients in the high TyG index group(69.73%) had a significantly higher risk of developing MACCE than those in the low TyG index group(23.63%) ( P <0.01). The multifactorial RCS model showed a nonlinear correlation (nonlinear P <0.01, overall P <0.01), with a critical value of 8.80 for the TyG index to predict the occurrence of MACCE. The TyG index was able to further improve the predictive accuracy of MACCE., Conclusions: TyG index is a potential predictor of the risk of MACCE in patients with CHD complicated with depression., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhao, Wang, Chen, Ding, Hou, Gui, Liu, Li, Liu, Sun and Zhao.)