Search for: All records

Abstract Silent hypoxemia, or "happy hypoxia", is a puzzling phenomenon in which patients who have contracted COVID-19 exhibit very low oxygen saturation ( ${\text{SaO}}_2$< 80%) but do not experience discomfort in breathing. The mechanism by which this blunted response to hypoxia occurs is unknown. We have previously shown that a computational model of the respiratory neural network (Diekman et al. in J Neurophysiol 118(4):2194–2215, 2017) can be used to test hypotheses focused on changes in chemosensory inputs to the central pattern generator (CPG). We hypothesize that altered chemosensory function at the level of the carotid bodies and/or thenucleus tractus solitariiare responsible for the blunted response to hypoxia. Here, we use our model to explore this hypothesis by altering the properties of the gain function representing oxygen sensing inputs to the CPG. We then vary other parameters in the model and show that oxygen carrying capacity is the most salient factor for producing silent hypoxemia. We call for clinicians to measure hematocrit as a clinical index of altered physiology in response to COVID-19 infection. 

Abstract Alzheimer’s disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progressive loss of neuronal function. Hippocampal neurons in transgenic mice with amyloidopathy or tauopathy exhibit altered intrinsic excitability properties. We used deep hybrid modeling (DeepHM), a recently developed parameter inference technique that combines deep learning with biophysical modeling, to map experimental data recorded from hippocampal CA1 neurons in transgenic AD mice and age-matched wildtype littermate controls to the parameter space of a conductance-based CA1 model. Although mechanistic modeling and machine learning methods are by themselves powerful tools for approximating biological systems and making accurate predictions from data, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. DeepHM addresses these shortcomings by using conditional generative adversarial networks to provide an inverse mapping of data to mechanistic models that identifies the distributions of mechanistic modeling parameters coherent to the data. Here, we demonstrated that DeepHM accurately infers parameter distributions of the conductance-based model on several test cases using synthetic data generated with complex underlying parameter structures. We then used DeepHM to estimate parameter distributions corresponding to the experimental data and infer which ion channels are altered in the Alzheimer’s mouse models compared to their wildtype controls at 12 and 24 months. We found that the conductances most disrupted by tauopathy, amyloidopathy, and aging are delayed rectifier potassium, transient sodium, and hyperpolarization-activated potassium, respectively. 

Cardiac myocytes synchronize through electrical signaling to contract heart muscles, facilitated by gap junctions (GJs) located in the intercalated disc (ID). GJs provide low-resistance pathways for electrical impulse propagation between myocytes, considered the primary mechanism for electrical communication in the heart. However, research indicates that conduction can persist without GJs. Ephaptic coupling (EpC), which depends on electrical fields in the narrow ID between adjacent myocytes, offers an alternative mechanism for cardiac conduction when GJs are impaired. Research suggests that EpC can enhance conduction velocity (CV) and reduce the likelihood of conduction block (CB), particularly when GJs are impaired, demonstrating the anti-arrhythmic potential of EpC. Reduced GJ communication increases the susceptibility of heart to arrhythmias due to ectopic or triggered activity, highlighting the pro-arrhythmic effect of GJ uncoupling. However, the interplay between GJs and EpC, and their roles in the initiation, dynamics, and termination of arrhythmias, remain unclear. Reentry, characterized by a loop of electrical activity, is a common mechanism underlying arrhythmogenesis in the heart. This study aims to explore the interplay between EpC and GJs on reentry initiation and its underlying dynamics. Specifically, we employed a two-dimensional (2D) discrete bidomain model that integrates EpC to simulate ephaptic conduction during reentry. We quantitatively assessed the outcomes of reentry initiation and the resulting dynamics across different levels of EpC, GJs, and initial perturbations. The results show that sufficiently strong EpC (i.e., sufficiently narrow clefts) tends to suppress reentry initiation, resulting in absent or non-sustained reentrant activity, while also introducing transient instability and heterogeneity into the cardiac dynamics. In contrast, relatively weak EpC (wide clefts) support sustained reentry with a stable rotor. Furthermore, we found that sufficiently strong EpC can lower the maximal dominant frequency observed during reentrant activity. Overall, this suggests that strong EpC exerts an anti-arrhythmic effect. 

Circadian clocks regulate many aspects of human physiology, including cardiovascular function and drug metabolism. Administering drugs at optimal times of the day may enhance effectiveness and reduce side effects. Certain cardiac antiarrhythmic drugs have been withdrawn from the market due to unexpected proarrhythmic effects such as fatal Torsade de Pointes (TdP) ventricular tachycardia. The Comprehensive in vitro Proarrhythmia Assay (CiPA) is a recent global initiative to create guidelines for the assessment of drug-induced arrhythmias that recommends a central role for computational modeling of ion channels andin silicoevaluation of compounds for TdP risk. We simulated circadian regulation of cardiac excitability and explored how dosing time of day affects TdP risk for 11 drugs previously classified into risk categories by CiPA. The model predicts that a high-risk drug taken at the most optimal time of day may actually be safer than a low-risk drug taken at the least optimal time of day. Based on these proof-of-concept results, we advocate for the incorporation of circadian clock modeling into the CiPA paradigm for assessing drug-induced TdP risk. Since cardiotoxicity is the leading cause of drug discontinuation, modeling cardiac-related chronopharmacology has significant potential to improve therapeutic outcomes.