Optimizing experimental designs for model selection of ion channel drug-binding mechanisms.

The rapid delayed rectifier current carried by the human Ether-à-go-go-Related Gene (hERG) channel is susceptible to drug-induced reduction, which can lead to an increased risk of cardiac arrhythmia. Establishing the mechanism by which a specific drug compound binds to hERG can help reduce uncertainty when quantifying pro-arrhythmic risk. In this study, we introduce a methodology for optimizing experimental voltage protocols to produce data that enable different proposed models for the drug-binding mechanism to be distinguished.

Examining the relationship between income and both mental and physical health among adults in the UK: Analysis of 12 waves (2009-2022) of Understanding Society.

There is growing evidence of a causal relationship between income and health. At the same time, pressure on reactive health and care services in the UK is increasing. Previous work to quantify the relationship has focused on particular age groups, conditions, or single-item self-rated health.

The socioeconomic context of stigma: examining the relationship between economic conditions and attitudes towards people with mental illness across European countries.

Introduction: Efforts to reduce the stigma associated with mental illness have intensified over the past 30 years with a particular focus on improving public attitudes. Difficult economic circumstances can be harmful to intergroup relations, but little is known about whether there is a relationship between socioeconomic conditions and attitudes towards people with mental illnesses.

Methods: Random effects logistic regression modelling was employed to explore the relationship between individual financial circumstances, contextual socioeconomic factors and difficulty speaking to a person with a significant mental illness across European countries.

Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators.

Cardiac pump function arises from a series of highly orchestrated events across multiple scales. Computational electromechanics can encode these events in physics-constrained models. However, the large number of parameters in these models has made the systematic study of the link between cellular, tissue, and organ scale parameters to whole heart physiology challenging.

Oxidation modulates LINGO2-induced inactivation of large conductance, Ca-activated potassium channels.

Ca and voltage-activated K (BK) channels are ubiquitous ion channels that can be modulated by accessory proteins, including β, γ, and LINGO1 BK subunits. In this study, we utilized a combination of site-directed mutagenesis, patch clamp electrophysiology, and molecular modeling to investigate if the biophysical properties of BK currents were affected by coexpression of LINGO2 and to examine how they are regulated by oxidation. We demonstrate that LINGO2 is a regulator of BK channels, since its coexpression with BK channels yields rapid inactivating currents, the activation of which is shifted ∼-30 mV compared to that of BKα currents.

Quantifying the impact of shape uncertainty on predicted arrhythmias.

Background: Personalised computer models are increasingly used to diagnose cardiac arrhythmias and tailor treatment. Patient-specific models of the left atrium are often derived from pre-procedural imaging of anatomy and fibrosis. These images contain noise that can affect simulation predictions.

Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds.

Models of electrical excitation and recovery in the heart have become increasingly detailed, but have yet to be used routinely in the clinical setting to guide personalized intervention in patients. One of the main challenges is calibrating models from the limited measurements that can be made in a patient during a standard clinical procedure. In this work, we propose a novel framework for the probabilistic calibration of electrophysiology parameters on the left atrium of the heart using local measurements of cardiac excitability.

Corrigendum: Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators.

[This corrects the article DOI: 10.3389/fphys.2021.

Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators.

Calibration of cardiac electrophysiology models is a fundamental aspect of model personalization for predicting the outcomes of cardiac therapies, simulation testing of device performance for a range of phenotypes, and for fundamental research into cardiac function. Restitution curves provide information on tissue function and can be measured using clinically feasible measurement protocols. We introduce novel "restitution curve emulators" as probabilistic models for performing model exploration, sensitivity analysis, and Bayesian calibration to noisy data.

Gaussian process manifold interpolation for probabilistic atrial activation maps and uncertain conduction velocity.

In patients with atrial fibrillation, local activation time (LAT) maps are routinely used for characterizing patient pathophysiology. The gradient of LAT maps can be used to calculate conduction velocity (CV), which directly relates to material conductivity and may provide an important measure of atrial substrate properties. Including uncertainty in CV calculations would help with interpreting the reliability of these measurements.

Considering discrepancy when calibrating a mechanistic electrophysiology model.

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterize uncertainty in model inputs and how that propagates through to outputs or predictions; examples of this can be seen in the papers of this issue. In this review and perspective piece, we draw attention to an important and under-addressed source of uncertainty in our predictions-that of uncertainty in the model structure or the equations themselves.

Quantifying atrial anatomy uncertainty from clinical data and its impact on electro-physiology simulation predictions.

Patient-specific computational models of structure and function are increasingly being used to diagnose disease and predict how a patient will respond to therapy. Models of anatomy are often derived after segmentation of clinical images or from mapping systems which are affected by image artefacts, resolution and contrast. Quantifying the impact of uncertain anatomy on model predictions is important, as models are increasingly used in clinical practice where decisions need to be made regardless of image quality.

Discrediting experiences: outcomes of eligibility assessments for claimants with psychiatric compared with non-psychiatric conditions transferring to personal independence payments in England.

Background: Recent qualitative research suggests that changes to the way eligibility for welfare payments is determined in the UK may be detrimental to claimants with mental illnesses. No large-scale analysis has been undertaken to date.AimsTo examine differences between claimants with psychiatric conditions compared with non-psychiatric conditions in the number of claims disallowed following a personal independence payment (PIP) eligibility assessment for existing disability living allowance (DLA) claimants.

Probabilistic Interpolation of Uncertain Local Activation Times on Human Atrial Manifolds.

Objective: Local activation time (LAT) mapping of the atria is important for targeted treatment of atrial arrhythmias, but current methods do not interpolate on the atrial manifold and neglect uncertainties associated with LAT observations. In this paper, we describe novel methods to, first, quantify uncertainties in LAT arising from bipolar electrogram analysis and assignment of electrode recordings to the anatomical mesh, second, interpolate uncertain LAT measurements directly on left atrial manifolds to obtain complete probabilistic activation maps, and finally, interpolate LAT jointly across both the manifold and different S1-S2 pacing protocols.

Methods: A modified center of mass approach was used to process bipolar electrograms, yielding a LAT estimate and error distribution from the electrogram morphology.

Active learning in Gaussian process interpolation of potential energy surfaces.

Three active learning schemes are used to generate training data for Gaussian process interpolation of intermolecular potential energy surfaces. These schemes aim to achieve the lowest predictive error using the fewest points and therefore act as an alternative to the status quo methods involving grid-based sampling or space-filling designs like Latin hypercubes (LHC). Results are presented for three molecular systems: CO-Ne, CO-H, and Ar.

Interpolation of intermolecular potentials using Gaussian processes.

A procedure is proposed to produce intermolecular potential energy surfaces from limited data. The procedure involves generation of geometrical configurations using a Latin hypercube design, with a maximin criterion, based on inverse internuclear distances. Gaussian processes are used to interpolate the data, using over-specified inverse molecular distances as covariates, greatly improving the interpolation.

Molecular simulation of the thermophysical properties and phase behaviour of impure CO relevant to CCS.

Impurities from the CCS chain can greatly influence the physical properties of CO. This has important design, safety and cost implications for the compression, transport and storage of CO. There is an urgent need to understand and predict the properties of impure CO to assist with CCS implementation.

Activation of STING-Dependent Innate Immune Signaling By S-Phase-Specific DNA Damage in Breast Cancer.

Background: Previously we identified a DNA damage response-deficient (DDRD) molecular subtype within breast cancer. A 44-gene assay identifying this subtype was validated as predicting benefit from DNA-damaging chemotherapy. This subtype was defined by interferon signaling.