DYNamic Assessment of Multi-Organ level dysfunction in patients recovering from COVID-19: DYNAMO COVID-19.

We evaluated the impacts of COVID-19 on multi-organ and metabolic function in patients following severe hospitalised infection compared to controls. Patients (n = 21) without previous diabetes, cardiovascular or cerebrovascular disease were recruited 5-7 months post-discharge alongside controls (n = 10) with similar age, sex and body mass. Perceived fatigue was estimated (Fatigue Severity Scale) and the following were conducted: oral glucose tolerance (OGTT) alongside whole-body fuel oxidation, validated magnetic resonance imaging and spectroscopy during resting and supine controlled exercise, dual-energy X-ray absorptiometry, short physical performance battery (SPPB), intra-muscular electromyography, quadriceps strength and fatigability, and daily step-count. There was a greater insulin response (incremental area under the curve, median (inter-quartile range)) during the OGTT in patients [18,289 (12,497-27,448) mIU/min/L] versus controls [8655 (7948-11,040) mIU/min/L], P < 0.001. Blood glucose response and fasting and post-prandial fuel oxidation rates were not different. This greater insulin resistance was not explained by differences in systemic inflammation or whole-body/regional adiposity, but step-count (P = 0.07) and SPPB scores (P = 0.004) were lower in patients. Liver volume was 28% greater in patients than controls, and fat fraction adjusted liver T1, a measure of inflammation, was raised in patients. Patients displayed greater perceived fatigue scores, though leg muscle volume, strength, force-loss, motor unit properties and post-exercise muscle phosphocreatine resynthesis were comparable. Further, cardiac and cerebral architecture and function (at rest and on exercise) were not different. In this cross-sectional study, individuals without known previous morbidity who survived severe COVID-19 exhibited greater insulin resistance, pointing to a need for physical function intervention in recovery.

Computerized Assessment of Motor Imitation as a Scalable Method for Distinguishing Children With Autism.

BACKGROUND: Imitation deficits are prevalent in autism spectrum conditions (ASCs) and are associated with core autistic traits. Imitating others' actions is central to the development of social skills in typically developing populations, as it facilitates social learning and bond formation. We present a Computerized Assessment of Motor Imitation (CAMI) using a brief (1-min), highly engaging video game task. METHODS: Using Kinect Xbox motion tracking technology, we recorded 48 children (27 with ASCs, 21 typically developing) as they imitated a model's dance movements. We implemented an algorithm based on metric learning and dynamic time warping that automatically detects and evaluates the important joints and returns a score considering spatial position and timing differences between the child and the model. To establish construct validity and reliability, we compared imitation performance measured by the CAMI method to the more traditional human observation coding (HOC) method across repeated trials and two different movement sequences. RESULTS: Results revealed poorer imitation in children with ASCs than in typically developing children (ps < .005), with poorer imitation being associated with increased core autism symptoms. While strong correlations between the CAMI and HOC methods (rs = .69-.87) confirmed the CAMI's construct validity, CAMI scores classified the children into diagnostic groups better than the HOC scores (accuracyCAMI = 87.2%, accuracyHOC = 74.4%). Finally, by comparing repeated movement trials, we demonstrated high test-retest reliability of CAMI (rs = .73-.86). CONCLUSIONS: Findings support the CAMI as an objective, highly scalable, directly interpretable method for assessing motor imitation differences, providing a promising biomarker for defining biologically meaningful ASC subtypes and guiding intervention.