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.

Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm.

Complement inhibition has shown promise in various disorders, including COVID-19. A prediction tool including complement genetic variants is vital. This study aims to identify crucial complement-related variants and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID-19 patients hospitalized between April 2020 and April 2021 at three referral centres were analysed using an artificial intelligence-based algorithm to predict disease outcome (ICU vs. non-ICU admission). A recently introduced alpha-index identified the 30 most predictive genetic variants. DERGA algorithm, which employs multiple classification algorithms, determined the optimal pattern of these key variants, resulting in 97% accuracy for predicting disease outcome. Individual variations ranged from 40 to 161 variants per patient, with 977 total variants detected. This study demonstrates the utility of alpha-index in ranking a substantial number of genetic variants. This approach enables the implementation of well-established classification algorithms that effectively determine the relevance of genetic variants in predicting outcomes with high accuracy.

Investigation on the Mechanical Recycling of Carbon Fiber-Reinforced Polymers by Peripheral Down-Milling.

Carbon fiber-reinforced plastics (CFRPs) are composite materials that play a significant role in the growth of many industrial fields where high performance and lightness of the structures are required. At the same time, the management at the end of their life has required the development of more and more sustainable and efficient recycling solutions. Considering this, the present research work aims to investigate a mechanical recycling method and the cutting strategies able to machine CFRP components in their entirety, using a common milling machine in a job shop scheme, making a shorter supply chain, and leading to economic and environmental benefits. In detail, laminates obtained by unidirectional carbon fiber prepregs were worked through the peripheral down-milling process, by varying the spindle speed and the feed rate. The recording of the cutting forces enabled the evaluation of features such as the cutting power and the specific cutting energy. Moreover, the chips from the milling process were classified as a function of their dimensions. Finally, specimens made of chips and epoxy resin were characterized under bending conditions, to evaluate the effectiveness of using the chips from CFRP peripheral milling as the polymer's reinforcement and, in addition, to appreciate the goodness of this recycling strategy.

Optimization of Single-Point Incremental Forming of Polymer Sheets through FEM.

Incremental sheet forming represents a relatively new process appointed to form sheets of pure metals, alloys, polymers, and composites for the manufacture of components in fields where customized production in a short time and at a low cost is required. Its most common variant, named single-point incremental forming, is a flexible process using very simple tooling; the sheet is clamped along the edges and a hemispherical-headed tool follows a required path, to deform the sheet locally. In so doing, better formability is reached without any dedicated dies and for low-forming forces, which represent some of the attractive features of this process. Nevertheless, and with special reference to thermoplastic sheets, incremental formed parts suffer from peculiar defects like twisting and wrinkling. In this numerical work, analyses were conducted through a commercial finite element code by varying the toolpath strategy of the incremental forming of polycarbonate sheets. The investigation of some features like the forming forces, the deformation states, the energy levels, and the forming time was carried out, to determine the toolpath strategy able to optimize the incremental forming process of polymer sheets. The results of the numerical analyses highlight a reduction of the forming forces when using toolpaths alternating diagonal up and vertical down steps and, presumably, a reduced risk of failures and defects. Furthermore, these toolpath strategies solutions also have a positive impact on the environment in terms of energy and do not significantly increase the manufacturing time.