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Background: Epigenetic changes may contribute importantly to cognitive decline in late life including Alzheimer’s disease (AD) and vascular dementia (VaD). Bromodomain and extra-terminal (BET) proteins are epigenetic “readers” that may distort normal gene expression and contribute to chronic disorders. Objective: To assess the effects of apabetalone, a small molecule BET protein inhibitor, on cognitive performance of patients 70 years or older participating in a randomized trial of patients at high risk for major cardiovascular events (MACE). Methods: The Montreal Cognitive Assessment (MoCA) was performed on all patients 70 years or older at the time of randomization. 464 participants were randomized to apabetalone or placebo in the cognition sub-study. In a prespecified analysis, participants were assigned to one of three groups: MoCA score≥26 (normal performance), MoCA score 25–22 (mild cognitive impairment), and MoCA score≤21 (dementia). Exposure to apabetalone was equivalent in the treatment groups in each MoCA-defined group. Results: Apabetalone was associated with an increased total MoCA score in participants with baseline MoCA score of≤21 (p = 0.02). There was no significant difference in change from baseline in the treatment groups with higher MoCA scores. In the cognition study, more patients randomized to apabetalone discontinued study drug for adverse effects (11.3% versus 7.9%). Conclusion: In this randomized controlled study, apabetalone was associated with improved cognition as measured by MoCA scores in those with baseline scores of 21 or less. BET protein inhibitors warrant further investigation for late life cognitive disorders.

High-fidelity physics simulations such as finite element analyses (FEA) provide accurate solutions to complex physical problems that are encountered in equipment subjected to transient conditions. However, such simulations come with significant computational expense, which often prevents them from being used for condition monitoring as a ‘digital twin’. Physics-based reduced order models (ROMs) seek to reduce this computational cost by using classical equations, augmented with empirical factors, to predict some unknown physical quantity of interest over time. Such ROMs are fast to run and simple to tailor for different load cases, but often require significant bespoke development effort. Additionally, developing the formulation of such ROMs becomes increasingly difficult as the complexity of the modelled physical phenomena increases. Advances in Artificial Intelligence and Machine Learning have resulted in techniques that are ideally placed to address the shortfalls of ROMs and maintain their benefits. A common problem that a physics-based ROM attempts to solve is predicting an unknown time-varying parameter (such as stress) based on a set of known time-varying parameters. Recurrent Neural Networks (RNNs) are well suited to tackle exactly this problem as an RNN-ROM. An RNN-ROM has been developed and validated against finite element analyses for a piece of rotating machinery subjected to complex thermal transients. Transient thermal datasets were extracted from locations of interest in an FEA representation of a rotating component and used as inputs to the RNN-ROM to predict time-dependent transient thermal behavior. The results were compared to the high-fidelity finite element model from which the input data was extracted; these results showed significantly improved accuracy compared to physics-based ROMs.

Inhibition of bromodomain and extra-terminal (BET) proteins can modulate lipoprotein and inflammatory factors that mediate atherosclerosis. The impact of the BET inhibitor, apabetalone, on cardiovascular events is unknown. Our objective was to investigate the impact of apabetalone on cardiovascular event rates in a pooled analysis of clinical studies in patients with established coronary artery disease. We conducted a pooled analysis of patients (n = 798) with coronary artery disease who participated in clinical trials (ASSERT, ASSURE, SUSTAIN) that evaluated the impact of 3–6 months of treatment with apabetalone on lipid parameters and coronary atherosclerosis. The incidence of major adverse cardiovascular events (death, myocardial infarction, coronary revascularization, hospitalization for cardiovascular causes) in the treatment groups was evaluated. At baseline, patients treated with apabetalone were more likely to be Caucasian, have a history of dyslipidemia, and be undertreated with s-blocker and anti-platelet agents. Treatment with apabetalone produced the following dose-dependent changes compared with placebo: increases in apolipoprotein A-I (apoA-I) of up to 6.7% (P