Complicated atheromatous plaque as integral atherogenesis

Revealing the nature of cardiovascular disease using DERGA, a novel data ensemble refinement greedy algorithm

BACKGROUND

The study aimed to determine the most crucial parameters associated with CVD and employ a novel data ensemble refinement procedure to uncover the optimal pattern of these parameters that can result in a high prediction accuracy.

METHODS AND RESULTS

Data were collected from 369 patients in total, 281 patients with CVD or at risk of developing it, compared to 88 otherwise healthy individuals. Within the group of 281 CVD or at-risk patients, 53 were diagnosed with coronary artery disease (CAD), 16 with end-stage renal disease, 47 newly diagnosed with diabetes mellitus 2 and 92 with chronic inflammatory disorders (21 rheumatoid arthritis, 41 psoriasis, 30 angiitis). The data were analyzed using an artificial intelligence-based algorithm with the primary objective of identifying the optimal pattern of parameters that define CVD. The study highlights the effectiveness of a six-parameter combination in discerning the likelihood of cardiovascular disease using DERGA and Extra Trees algorithms. These parameters, ranked in order of importance, include Platelet-derived Microvesicles (PMV), hypertension, age, smoking, dyslipidemia, and Body Mass Index (BMI). Endothelial and erythrocyte MVs, along with diabetes were the least important predictors. In addition, the highest prediction accuracy achieved is 98.64%. Notably, using PMVs alone yields a 91.32% accuracy, while the optimal model employing all ten parameters, yields a prediction accuracy of 0.9783 (97.83%).

CONCLUSIONS

Our research showcases the efficacy of DERGA, an innovative data ensemble refinement greedy algorithm. DERGA accelerates the assessment of an individual's risk of developing CVD, allowing for early diagnosis, significantly reduces the number of required lab tests and optimizes resource utilization. Additionally, it assists in identifying the optimal parameters critical for assessing CVD susceptibility, thereby enhancing our understanding of the underlying mechanisms.

Comparative effects of quercetin and hydroalcoholic extract of Otostegia persica boiss with atorvastatin on atherosclerosis complication in male wistar rats

The use of herbal remedies is significantly considered in the atherosclerosis treatment, reduction of fatty elements, and prevention of activity of oxidative stress factors. The present study was conducted on 48 rats in 6 groups. The experimental and sham groups were fed with 2% cholesterol for 40 days; and experimental groups were separately treated by atorvastatin, quercetin, and hydroalcoholic extract for 4 weeks. After treatment procedure, some serum factors such as low-density lipoprotein (LDL), total cholesterol (TC), malondialdehyde (MDA), and reactive oxygen species (ROS) were evaluated. Serum levels of LDL, TC, MDA, and ROS were significantly lower in experimental groups than sham group (p < .01). There was a significant decrease in serum MDA levels of these two groups in comparison with the atorvastatin-treated group (p < .05). Blood pressure parameters were decreased in treated with quercetin and hydroalcoholic extract in comparison with the sham group (p < .05). Quercetin and hydroalcoholic extract similar to atorvastatin could decrease serum lipids [except high-density lipoprotein (HDL)], oxidative stress factors, aorta contraction, weight gain, and blood pressure. These reagents improved the vascular structure and prevented the plaque formation.