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Using AI to assess changes in physiological function with ageing: from single cell to organism

Waterloo Campus, London

11 Dec ageing-ark-lecture Part of Ageing Research at King's (ARK) Lectures

Speaker: Joachim A. Behar, head of the Artificial Intelligence in Medicine Laboratory at the Technion faculty of Biomedical Engineering

 

Single cell: Age-related deterioration of pacemaker function has been documented in mammals, including humans. In aged isolated sinoatrial node tissues and cells, reduction in the spontaneous action potential (AP) firing rate was associated with deterioration of intracellular and membrane mechanisms; however, their relative contribution to age-associated deficient pacemaker function is not known. Interestingly, pharmacological interventions that increase posttranslation modification signaling activities can restore the basal and maximal AP firing rate, but the identities of the protein targets responsible for AP firing rate restoration are not known. We developed a numerical model that simulates the function of a single mouse pacemaker cell. In addition to describing membrane and intracellular mechanisms, the model includes descriptions of autonomic receptor activation pathways and posttranslation modification signaling cascades. The numerical model is used to provide new insights on age-related deterioration of pacemaker function that we will illustrate in this talk.

Organism: The need for a mass screening tool for OSA has motivated the research and development of sleep questionnaires (e.g. STOP-BANG, NoSaS) and single channels monitors in identifying patients at risk of OSA. Oximetry has been studied as a candidate for single channel monitoring of OSA. However, the performance of these screening options on a representative population sample have not been studied and their robustness against using different hypopnea rules (recommended/alternative) and scoring indexes (AHI/RDI) has not been assessed. We showed, for the first time, that biomarkers derived from oximetry are accurate predictors for mass OSA screening on a large (n=887) representative population sample. The final machine learning model is termed the OxyDOSA. In this talk we will show how the OxyDOSA model behaves when used on an aging population sample and comment on the different physiological phenotype of older adults presenting OSA.

 

Articles associated with this talk:

Behar, Joachim, and Yael Yaniv. "Age-related pacemaker deterioration is due to impaired intracellular and membrane mechanisms: Insights from numerical modeling." The Journal of general physiology 149.10 (2017): 935-949.

Behar, Joachim A., et al. "Feasibility of Single Channel Oximetry for Mass Screening of Obstructive Sleep Apnea." EClinicalMedicine (2019).


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