Facemask pneumotachography is a common, precise experimental approach for measuring respiratory outcomes in neonate rodents (P7-8), particularly in autoresuscitation experiments that inform upon Sudden Infant Death Syndrome where failure of autoresuscitation is hypothesized to be a common endpoint. However, current approaches rely on direct, real-time waveform inspection by an observer and their highly variable reaction times to execute responsive changes in assay gas exposures. Looper, represents a novel and significant improvement on typical observer-based interpretation of live data to initiate challenge and recovery gas applications to uncover nuanced features in neonate cardiorespiratory physiology. Leveraging our previously published Breathe Easy software, we have expanded the analytical capabilities of the software to analyze key features within a typical autoresuscitation cycle. Utilizing the software output, we have further developed machine learning approaches that enable real-time prediction of cardio-respiratory failure based distinct waveform features more than 10min in advance, setting the stage for novel experiments to identify critical interventional windows for enhancing resuscitation. R01HL161142 R01HL130249. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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