Perspective and Therapeutic Potential of the Noncoding RNA-Connexin Axis.

Noncoding RNAs (ncRNAs) are a class of nucleotide sequences that cannot be translated into peptides. ncRNAs can function post-transcriptionally by splicing complementary sequences of mRNAs or other ncRNAs or by directly engaging in protein interactions. Over the past few decades, the pervasiveness of ncRNAs in cell physiology and their pivotal roles in various diseases have been identified. One target regulated by ncRNAs is connexin (Cx), a protein that forms gap junctions and hemichannels and facilitates intercellular molecule exchange. The aberrant expression and misdistribution of connexins have been implicated in central nervous system diseases, cardiovascular diseases, bone diseases, and cancer. Current databases and technologies have enabled researchers to identify the direct or indirect relationships between ncRNAs and connexins, thereby elucidating their correlation with diseases. In this review, we selected the literature published in the past five years concerning disorders regulated by ncRNAs via corresponding connexins. Among it, microRNAs that regulate the expression of Cx43 play a crucial role in disease development and are predominantly reviewed. The distinctive perspective of the ncRNA-Cx axis interprets pathology in an epigenetic manner and is expected to motivate research for the development of biomarkers and therapeutics.

Control for multiple risk factors and incident heart failure and mortality in patients with diabetes mellitus: Insights from the Kailuan cohort study.

BACKGROUND: This study evaluated the relationship between controlling multiple risk factors and diabetes-related heart failure and all-cause mortality, and the extent to which the excess risk can be reduced. METHODS: 17,676 patients with diabetes and 69,493 matched non-diabetic control subjects were included in the Kailuan study, with a median follow-up of 11.19 years. The risk factor control was defined by the attainment of target values for systolic blood pressure, body mass index, low-density lipoprotein cholesterol, fasting blood glucose, high-sensitive C-reactive protein and smoking. Fine-Gray and Cox models were used to estimate associations between the degree of risk factor control and risk of heart failure and all-cause mortality respectively. RESULTS: Among diabetes patients, there was a gradual reduction in the risk of outcomes as the degree of risk factor control increased. For each additional risk factor that was controlled, there was an associated 16 % decrease in heart failure risk and a 10 % decrease in all-cause mortality risk. Among diabetes patients with ≥5 well-controlled risk factors, the adjusted hazard ratio compared to controls for heart failure and all-cause mortality was 1.25 (95 %CI: 0.99-1.56) and 1.17(95 %CI: 1.05-1.31) respectively. The protective effect of comprehensive risk factor control on the risk of heart failure was more pronounced in men and those using antihypertensive medications. CONCLUSIONS: Control for multiple risk factors is associated with reduced heart failure and all-cause mortality risks in a cumulative and sex-specific manner. However, despite optimization of risk factor control, diabetes patients still face increased risks compared to the general population.

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.

Automated valvular heart disease detection using heart sound with a deep learning algorithm.

Background: Insufficient clinicians' auscultation ability delays the diagnosis and treatment of valvular heart disease (VHD); artificial intelligence provides a solution to compensate for the insufficiency in auscultation ability by distinguishing between heart murmurs and normal heart sounds. However, whether artificial intelligence can automatically diagnose VHD remains unknown. Our objective was to use deep learning to process and compare raw heart sound data to identify patients with VHD requiring intervention. Methods: Heart sounds from patients with VHD and healthy controls were collected using an electronic stethoscope. Echocardiographic findings were used as the gold standard for this study. According to the chronological order of enrollment, the early-enrolled samples were used to train the deep learning model, and the late-enrollment samples were used to validate the results. Results: The final study population comprised 499 patients (354 in the algorithm training group and 145 in the result validation group). The sensitivity, specificity, and accuracy of the deep-learning model for identifying various VHDs ranged from 71.4 to 100.0%, 83.5-100.0%, and 84.1-100.0%, respectively; the best diagnostic performance was observed for mitral stenosis, with a sensitivity of 100.0% (31.0-100.0%), a specificity of 100% (96.7-100.0%), and an accuracy of 100% (97.5-100.0%). Conclusions: Based on raw heart sound data, the deep learning model effectively identifies patients with various types of VHD who require intervention and assists in the screening, diagnosis, and follow-up of VHD.

Application of artificial intelligence in the diagnosis of sleep apnea.

STUDY OBJECTIVES: Machine learning (ML) models have been employed in the setting of sleep disorders. This review aims to summarize the existing data about the role of ML techniques in the diagnosis, classification, and treatment of sleep-related breathing disorders. METHODS: A systematic search in Medline, EMBASE, and Cochrane databases through January 2022 was performed. RESULTS: Our search strategy revealed 132 studies that were included in the systematic review. Existing data show that ML models have been successfully used for diagnostic purposes. Specifically, ML models showed good performance in diagnosing sleep apnea using easily obtained features from the electrocardiogram, pulse oximetry, and sound signals. Similarly, ML showed good performance for the classification of sleep apnea into obstructive and central categories, as well as predicting apnea severity. Existing data show promising results for the ML-based guided treatment of sleep apnea. Specifically, the prediction of outcomes following surgical treatment and optimization of continuous positive airway pressure therapy can be guided by ML models. CONCLUSIONS: The adoption and implementation of ML in the field of sleep-related breathing disorders is promising. Advancements in wearable sensor technology and ML models can help clinicians predict, diagnose, and classify sleep apnea more accurately and efficiently. CITATION: Bazoukis G, Bollepalli SC, Chung CT, et al. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med. 2023;19(7):1337-1363.

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.