Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset.

Mechanistic understanding of organic reactions can facilitate reaction development, impurity prediction, and in principle, reaction discovery. While several machine learning models have sought to address the task of predicting reaction products, their extension to predicting reaction mechanisms has been impeded by the lack of a corresponding mechanistic dataset. In this study, we construct such a dataset by imputing intermediates between experimentally reported reactants and products using expert reaction templates and train several machine learning models on the resulting dataset of 5,184,184 elementary steps. We explore the performance and capabilities of these models, focusing on their ability to predict reaction pathways and recapitulate the roles of catalysts and reagents. Additionally, we demonstrate the potential of mechanistic models in predicting impurities, often overlooked by conventional models. We conclude by evaluating the generalizability of mechanistic models to new reaction types, revealing challenges related to dataset diversity, consecutive predictions, and violations of atom conservation.

Clinical implications of renin-angiotensin system inhibitors for development and progression of non-alcoholic fatty liver disease.

Recently, there has been an increasing interest in the therapeutic efficacy of RAS inhibitors (RASi) in patients with non-alcoholic fatty liver disease (NAFLD) because they may reduce oxidative stress, inflammatory markers, and enhanced fibrosis. An objective of this study was to investigate the role of RASi on NAFLD development and progression in a large cohort. We conducted a nested case-control study. Study subjects were classified into two study cohorts according to baseline NAFLD status: non-NAFLD (n = 184,581) and established NALFD (n = 27,565). An NAFLD development or progression case was defined as a patient with newly developed NAFLD or new progression of advanced fibrosis from non-NAFLD and established NALFD cohorts, respectively. A conditional logistic regression analysis was conducted to estimate the associations between RASi exposure and NAFLD development/progression. Overall, no significant association was evident between RASi use and NAFLD development or progression (NAFLD development; ever-user vs. never-user: OR 1.017; 95% CI 0.842-1.230, NAFLD progression; ever-user vs. never-user: aOR 0.942; 95% CI 0.803-1.105). RASi ever-use in cases of individuals who were obese or who had normal fasting plasma glucose (FPG) was associated with reduced risk of both NAFLD development (body mass index (BMI) ≥ 25 kg/m2: 0.708 [95% confidence interval (CI) 0.535-0.937], FPG of < 100 mg/mL: 0.774 [95% CI 0.606-0.987]) and progression (BMI ≥ 25 kg/m2: 0.668 [95% CI 0.568-0.784], FPG of < 100 mg/mL: 0.732 [95% CI 0.582-0.921]). The present study did not verify a significant overall association between RASi use and NAFLD development/progression but suggested that RASi might prevent NAFLD development and progression among specific subjects.

Metabolic Syndrome and Parkinson's Disease Incidence: A Nationwide Study Using Propensity Score Matching.

Background: Metabolic syndrome (MetS) and Parkinson's disease (PD) share common pathophysiological mechanisms. This study aimed to investigate the influence of MetS on PD incidence. Materials and Methods: A propensity score-matched cohort study was conducted using the National Health Insurance Service-National Health Screening Cohort (NHIS-HealS) data (2002-2015) from the Korean National Health Insurance Service. Individuals with MetS were identified from those who underwent a health checkup in 2009-2010 and were 1:1 matched to individuals without MetS (non-MetS) using the propensity score method. Among 314,737 eligible individuals, 85,530 MetS and non-MetS pairs were selected. Results: During a mean follow-up of 7.23 years, 819 (0.48%) PD cases occurred. Individuals with MetS exhibited 1.23 times greater PD incidence (95% confidence interval [CI], 1.06-1.43; P = 0.006). The risk of PD increased with the number of MetS components, with the presence of five MetS components altogether doubling the incidence of PD (odds ratio [OR], 2.00; 95% CI, 1.30-3.04; P = 0.001). High blood pressure, low high-density lipoprotein cholesterol, and high fasting blood glucose increased PD incidence by 1.34 times (95% CI, 1.15-1.58; P < 0.001), 1.31 times (95% CI, 1.13-1.52; P < 0.001), and 1.20 times (95% CI, 1.04-1.38; P = 0.013), respectively. Elevated waist circumference was not associated with PD incidence (OR, 1.11; 95% CI, 0.96-1.28; P = 0.176). High triglycerides exerted a protective effect against PD incidence especially in men (OR, 0.66; 95% CI, 0.54-0.81; P < 0.001). Conclusions: MetS may be a risk factor for PD incidence, and individual components of MetS exert different effects depending on sex.