Comparison of conventional scoring systems to machine learning models for the prediction of major adverse cardiovascular events in patients undergoing coronary computed tomography angiography.

Background: The study aims to compare the prognostic performance of conventional scoring systems to a machine learning (ML) model on coronary computed tomography angiography (CCTA) to discriminate between the patients with and without major adverse cardiovascular events (MACEs) and to find the most important contributing factor of MACE. Materials and methods: From November to December 2019, 500 of 1586 CCTA scans were included and analyzed, then six conventional scores were calculated for each participant, and seven ML models were designed. Our study endpoints were all-cause mortality, non-fatal myocardial infarction, late coronary revascularization, and hospitalization for unstable angina or heart failure. Score performance was assessed by area under the curve (AUC) analysis. Results: Of 500 patients (mean age: 60 ± 10; 53.8% male subjects) referred for CCTA, 416 patients have met inclusion criteria, 46 patients with early (<90 days) cardiac evaluation (due to the inability to clarify the reason for the assessment, deterioration of the symptoms vs. the CCTA result), and 38 patients because of missed follow-up were not enrolled in the final analysis. Forty-six patients (11.0%) developed MACE within 20.5 ± 7.9 months of follow-up. Compared to conventional scores, ML models showed better performance, except only one model which is eXtreme Gradient Boosting had lower performance than conventional scoring systems (AUC:0.824, 95% confidence interval (CI): 0.701-0.947). Between ML models, random forest, ensemble with generalized linear, and ensemble with naive Bayes were shown to have higher prognostic performance (AUC: 0.92, 95% CI: 0.85-0.99, AUC: 0.90, 95% CI: 0.81-0.98, and AUC: 0.89, 95% CI: 0.82-0.97), respectively. Coronary artery calcium score (CACS) had the highest correlation with MACE. Conclusion: Compared to the conventional scoring system, ML models using CCTA scans show improved prognostic prediction for MACE. Anatomical features were more important than clinical characteristics.

Neuro-Immuno-Endocrine Interactions in Early Life Stress and Heroin Withdrawal Timeline.

Both heroin abuse and early life stress (ELS) affect the immune system and the hypothalamic-pituitary-adrenal (HPA) axis. Additionally, accelerated aging due to mild inflammation has been indicated in these conditions. The present study aims to compare plasma levels of apoptosis markers, inflammatory markers, and stress hormones during early heroin abstinence period. Thirty-one individuals with heroin/opioid use disorder who had heroin-ELS and 26 of their siblings who were not abusing substances (ELS), and 32 individuals with heroin/opioid use disorder without a history of ELS (heroin-no ELS) were included in the study. The levels of interleukin-6, C-reactive protein, erythrocyte sedimentation rate, albumin, alanine transaminase, aspartate transaminase, and white blood cell count were assessed as the inflammatory and biochemistry markers. Also, apoptosis markers including tumor necrosis factor (TNF)-related weak inducer of apoptosis, TNF-related apoptosis-inducing ligand, soluble tumor necrosis factor receptor type I as apoptosis markers were detected by enzyme-linked immunosorbent assay. ELS was simultaneously evaluated using the Childhood Trauma Questionnaire, Minnesota Multiphasic Personality Inventory, and beck depression inventory scales. Besides, heroin craving was assessed by Daily Drinking/Drug Questionnaire score in individuals with heroin use disorder. This is the first study to evaluate the inflammatory, stress, and apoptosis markers during heroin abstinence, supporting the association between ELS and peripheral pro-inflammatory markers' levels and HPA axis.

Experimental characterization and modelization of ion exchange kinetics for a carboxylic resin in infinite solution volume conditions. Application to monovalent-trivalent cations exchange.

This study is devoted to the characterization of ion exchange inside a microsphere of carboxylic resin. It aims at describing the kinetics of this exchange reaction which is known to be controlled by interdiffusion in the particle. The fractional attainment of equilibrium function of time depends on the concentration of the cations in the resin which can be modelized by the Nernst-Planck equation. A powerful approach for the numerical resolution of this equation is introduced in this paper. This modeling is based on the work of Helfferich but involves an implicit numerical scheme which reduces the computational cost. Knowing the diffusion coefficients of the cations in the resin and the radius of the spherical exchanger, the kinetics can be hence completely determined. When those diffusion parameters are missing, they can be deduced by fitting experimental data of fractional attainment of equilibrium. An efficient optimization tool coupled with the implicit resolution has been developed for this purpose. A monovalent/trivalent cation exchange had been experimentally characterized for a carboxylic resin. Diffusion coefficients and concentration profiles in the resin were then deduced through this new model.