Inflammation and Connexin 43 profiles in the prefrontal cortex are relevant to stress susceptibility and resilience in mice.

Depression is a major chronic mental illness worldwide, characterized by anhedonia and pessimism. Exposed to the same stressful stimuli, some people behave normally, while others exhibit negative behaviors and psychology. The exact molecular mechanisms linking stress-induced depressive susceptibility and resilience remain unclear. Connexin 43 (Cx43) forms gap junction channels between the astrocytes, acting as a crucial role in the pathogenesis of depression. Cx43 dysfunction could lead to depressive behaviors, and depression down-regulates the expression of Cx43 in the prefrontal cortex (PFC). Besides, accumulating evidence indicates that inflammation is one of the most common pathological features of the central nervous system dysfunction. However, the roles of Cx43 and peripheral inflammation in stress-susceptible and stress-resilient individuals have rarely been investigated. Thus, animals were classified into the chronic unpredictable stress (CUS)-susceptible group and the CUS-resilient group based on the performance of behavioral tests following the CUS protocol in this study. The protein expression of Cx43 in the PFC, the Cx43 functional changes in the PFC, and the expression levels including interleukin (IL)-1ß, tumor necrosis factor-α, IL-6, IL-2, IL-10, and IL-18 in the peripheral serum were detected. Here, we found that stress exposure triggered a significant reduction in Cx43 protein expression in the CUS-susceptible mice but not in the CUS-resilient mice accompanied by various Cx43 phosphorylation expression and the changes of inflammatory signals. Stress resilience is associated with Cx43 in the PFC and fluctuation in inflammatory signaling, showing that therapeutic targeting of these pathways might promote stress resilience.

Electrocardiogram-based artificial intelligence for the diagnosis of heart failure: a systematic review and meta-analysis.

BACKGROUND: The electrocardiogram (ECG) is an inexpensive and easily accessible investigation for the diagnosis of cardiovascular diseases including heart failure (HF). The application of artificial intelligence (AI) has contributed to clinical practice in terms of aiding diagnosis, prognosis, risk stratification and guiding clinical management. The aim of this study is to systematically review and perform a meta-analysis of published studies on the application of AI for HF detection based on the ECG. METHODS: We searched Embase, PubMed and Web of Science databases to identify literature using AI for HF detection based on ECG data. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria. Random-effects models were used for calculating the effect estimates and hierarchical receiver operating characteristic curves were plotted. Subgroup analysis was performed. Heterogeneity and the risk of bias were also assessed. RESULTS: A total of 11 studies including 104,737 subjects were included. The area under the curve for HF diagnosis was 0.986, with a corresponding pooled sensitivity of 0.95 (95% CI: 0.86-0.98), specificity of 0.98 (95% CI: 0.95-0.99) and diagnostic odds ratio of 831.51 (95% CI: 127.85-5407.74). In the patient selection domain of QUADAS-2, eight studies were designated as high risk. CONCLUSIONS: According to the available evidence, the incorporation of AI can aid the diagnosis of HF. However, there is heterogeneity among machine learning algorithms and improvements are required in terms of quality and study design.