Associations between momentary mental states and concurrent social functioning after remission from first episode psychosis: A HAMLETT ecological momentary assessment study.

Background: Symptom severity and social functioning are important outcomes after first episode psychosis (FEP), yet current evidence about associations between them is inconsistent and lacks (subclinical) momentary insights.

Methods: The current Ecological Momentary Assessment (EMA) study was conducted in 58 people in remission from FEP, as part of the HAMLETT (Handling Antipsychotic Medication: Long-term Evaluation of Targeted Treatment) trial. At baseline, participants were prompted to report momentary mental states and social context 10x/day for eight consecutive days, including psychotic experiences (PEs), motivation/drive and negative affect, that may indicate proxies of (subclinical) psychotic, negative and general affective symptoms, respectively.

Synergistic biophysics and machine learning modeling to rapidly predict cardiac growth probability.

Computational models that can predict growth and remodeling of the heart could have important clinical applications. However, the time it takes to calibrate and run current models while considering data uncertainty and variability makes them impractical for routine clinical use. This study aims to address this need by creating a computational framework to efficiently predict cardiac growth probability.

Spike by spike frequency analysis of amperometry traces provides statistical validation of observations in the time domain.

Amperometry is a commonly used electrochemical method for studying the process of exocytosis in real-time. Given the high precision of recording that amperometry procedures offer, the volume of data generated can span over several hundreds of megabytes to a few gigabytes and therefore necessitates systematic and reproducible methods for analysis. Though the spike characteristics of amperometry traces in the time domain hold information about the dynamics of exocytosis, these biochemical signals are, more often than not, characterized by time-varying signal properties.

Real-world effectiveness and tolerability of switching to doravirine-based antiretroviral therapy in people with HIV: a nationwide, matched, prospective cohort study.

Background: Currently, real-world data on doravirine are scarce. In a national prospective cohort, we assessed the effectiveness and tolerability of switching to doravirine-based antiretroviral therapy (ART) in people with HIV.

Methods: We did a nationwide, matched, prospective cohort study of people with HIV without previous virological failure and stable for at least 12 months on non-doravirine-containing triple or dual ART switching to doravirine before Sept 1, 2020 (exposed group).

Synergistic Biophysics and Machine Learning Modeling to Rapidly Predict Cardiac Growth Probability.

Computational models that can predict growth and remodeling of the heart could have important clinical applications. However, the time it takes to calibrate and run current models while considering data uncertainty and variability makes them impractical for routine clinical use. This study aims to address this need by creating a computational framework to efficiently predict cardiac growth probability.