Nonalcoholic Fatty Liver Disease as an Emerging Risk Factor and Potential Intervention Target for Atherosclerotic Cardiovascular Diseases.

ABSTRACT: Nonalcoholic fatty liver disease (NAFLD) is an underappreciated independent risk factor for atherosclerotic cardiovascular diseases (ASCVDs). In recent years, the risk of ASCVD has increased along with the prevalence of NAFLD. ASCVD events are highly prevalent and are the main contributor to death in patients with NAFLD. The association between NAFLD and ASCVD has been validated in numerous observational, cohort, and genetic studies. Most of these studies agree that NAFLD significantly increases the risk of developing atherosclerosis and ASCVD. In addition, the underlying proatherosclerotic mechanisms of NAFLD have been gradually revealed; both disorders share several common pathophysiologic mechanisms including insulin resistance, whereas systemic inflammation and dyslipidemia driven by NAFLD directly promote atherosclerosis. Recently, NAFLD, as an emerging risk enhancer for ASCVD, has attracted attention as a potential treatment target for ASCVD. This brief review aims to illustrate the potential mechanistic insights, present recent clinically relevant investigations, and further explore the emerging therapies such as novel antidiabetic and lipid-lowering agents that could improve NAFLD and reduce ASCVD risk.

Elevated postprandial triglyceride-rich lipoproteins in patients with diabetes and stable coronary artery disease correlated with early renal damage and systemic inflammation.

BACKGROUND: Dyslipidaemia is key in the development of coronary heart disease (CHD) in patients with diabetes mellitus (DM). Accumulated evidence supports that diabetic nephropathy increases the mortality risk of patients with CHD, while the influence of diabetic dyslipidaemia on renal damage in patients with DM and CHD remains unknown. Moreover, recent data indicate that postprandial dyslipidaemia has predictive value in terms of CHD prognosis, especially in patients with DM. The study aimed to determine the relationship of triglyceride-rich lipoproteins (TRLs) after daily Chinese breakfast on systemic inflammation and early renal damage in Chinese patients with DM and SCAD. METHODS: Patients with DM diagnosed with SCAD while in the Department of Cardiology of Shengjing Hospital from September 2016 to February 2017 were enrolled in this study. Fasting and 4-h postprandial blood lipids, fasting blood glucose, glycated haemoglobin, urinary albumin-to-creatinine ratio (UACR), serum interleukin-6 (IL-6) and tumour necrosis factor-α (TNF-α) concentrations, and other parameters were measured. Fasting and postprandial blood lipid profiles and inflammatory cytokines were analysed using a paired t-test. The association between variables was analysed using Pearson or Spearman bivariate analysis. P < 0.05 was considered to be statistically significant. RESULTS: The study enrolled 44 patients in total. Compared with fasting state, postprandial total cholesterol high-density lipoprotein-cholesterol (HDL-C),low-density lipoprotein-cholesterol (LDL-C) and non-high-density lipoprotein-cholesterol (non-HDL-C) all showed no significant change. Postprandial serum triglyceride (TG) concentration increased significantly compared with that at fasting (1.40 ± 0.40 vs. 2.10 ± 0.94 mmol/L, P < 0.001), as did serum remnant lipoprotein-cholesterol (RLP-C) (0.54 ± 0.18 mmol/L vs. 0.64 ± 0.25 mmol/L). Pearson analysis revealed that serum TG and RLP-C positively correlated before and after breakfast. Moreover, during fasting, positive correlations were observed between TG and serum IL-6, TNF-α, and UACR. Positive correlations were observed between RLP-C and IL-6, UACR under fasting condition, while both TG and RLP-C were positively correlated with postprandial serum IL-6, TNF-α, and UACR concentrations. Finally, positive correlations were observed between UACR and IL-6 and TNF-α concentration under both fasting and postprandial conditions. CONCLUSIONS: An increase in postprandial TRLs was observed in Chinese patients with DM and SCAD after daily breakfast, and this increase may be related to early renal injury via the induction of systemic inflammation.

Advances in machine learning-based bacteria analysis for forensic identification: identity, ethnicity, and site of occurrence.

When faced with an unidentified body, identifying the victim can be challenging, particularly if physical characteristics are obscured or masked. In recent years, microbiological analysis in forensic science has emerged as a cutting-edge technology. It not only exhibits individual specificity, distinguishing different human biotraces from various sites of occurrence (e.g., gastrointestinal, oral, skin, respiratory, and genitourinary tracts), each hosting distinct bacterial species, but also offers insights into the accident's location and the surrounding environment. The integration of machine learning with microbiomics provides a substantial improvement in classifying bacterial species compares to traditional sequencing techniques. This review discusses the use of machine learning algorithms such as RF, SVM, ANN, DNN, regression, and BN for the detection and identification of various bacteria, including Bacillus anthracis, Acetobacter aceti, Staphylococcus aureus, and Streptococcus, among others. Deep leaning techniques, such as Convolutional Neural Networks (CNN) models and derivatives, are also employed to predict the victim's age, gender, lifestyle, and racial characteristics. It is anticipated that big data analytics and artificial intelligence will play a pivotal role in advancing forensic microbiology in the future.