Heart rate and gas exchange dynamic responses to multiple brief exercise bouts (MBEB) in early- and late-pubertal boys and girls.

Natural patterns of physical activity in youth are characterized by brief periods of exercise of varying intensity interspersed with rest. To better understand systemic physiologic response mechanisms in children and adolescents, we examined five responses [heart rate (HR), respiratory rate (RR), oxygen uptake (V̇O2 ), carbon dioxide production (V̇CO2 ), and minute ventilation (V̇E), measured breath-by-breath] to multiple brief exercise bouts (MBEB). Two groups of healthy participants (early pubertal: 17 female, 20 male; late-pubertal: 23 female, 21 male) performed five consecutive 2-min bouts of constant work rate cycle-ergometer exercise interspersed with 1-min of rest during separate sessions of low- or high-intensity (~40% or 80% peak work, respectively). For each 2-min on-transient and 1-min off-transient we calculated the average value of each cardiopulmonary exercise testing (CPET) variable (Y̅). There were significant MBEB changes in 67 of 80 on- and off-transients. Y̅ increased bout-to-bout for all CPET variables, and the magnitude of increase was greater in the high-intensity exercise. We measured the metabolic cost of MBEB, scaled to work performed, for the entire 15 min and found significantly higher V̇O2 , V̇CO2 , and V̇E costs in the early-pubertal participants for both low- and high-intensity MBEB. To reduce breath-by-breath variability in estimation of CPET variable kinetics, we time-interpolated (second-by-second), superimposed, and averaged responses. Reasonable estimates of τ (<20% coefficient of variation) were found only for on-transients of HR and V̇O2 . There was a remarkable reduction in τHR following the first exercise bout in all groups. Natural patterns of physical activity shape cardiorespiratory responses in healthy children and adolescents. Protocols that measure the effect of a previous bout on the kinetics of subsequent bouts may aid in the clinical utility of CPET.

Deep Learning for Whole-Slide Tissue Histopathology Classification: A Comparative Study in the Identification of Dysplastic and Non-Dysplastic Barrett's Esophagus.

The gold standard of histopathology for the diagnosis of Barrett's esophagus (BE) is hindered by inter-observer variability among gastrointestinal pathologists. Deep learning-based approaches have shown promising results in the analysis of whole-slide tissue histopathology images (WSIs). We performed a comparative study to elucidate the characteristics and behaviors of different deep learning-based feature representation approaches for the WSI-based diagnosis of diseased esophageal architectures, namely, dysplastic and non-dysplastic BE. The results showed that if appropriate settings are chosen, the unsupervised feature representation approach is capable of extracting more relevant image features from WSIs to classify and locate the precursors of esophageal cancer compared to weakly supervised and fully supervised approaches.