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Background: To investigate if intraoperative very short-term variability in blood pressure measured by sample entropy improves discrimination of postoperative acute kidney injury after noncardiac surgery., Methods: Adult surgical patients undergoing general, thoracic, urological, or gynecological surgery between August 2016 to June 2017 at Seoul National University Hospital were included. The primary outcome was acute kidney injury stage 1, defined by the Kidney Disease: Improving Global Outcomes guidelines. Exploratory and explanatory variables included sample entropy of the mean arterial pressure and standard demographic, surgical, anesthesia and hypotension over time indices known to be associated with acute kidney injury respectively. Random forest classification and L1 logistic regression were used to assess four models for discriminating acute kidney injury: (1) Standard risk factors which included demographic, anesthetic, and surgical variables (2) Standard risk factors and cumulative hypotension over time (3) Standard risk factors and sample entropy (4) Standard risk factors, cumulative hypotension over time and sample entropy., Results: Two hundred and thirteen (7.4%) cases developed postoperative acute kidney injury. The median and interquartile range for sample entropy of mean arterial pressure was 0.34 and [0.26, 0.42] respectively. C-statistics were identical between the random forest and L1 logistic regression models. Results demonstrated no improvement in discrimination of postoperative acute kidney injury with the addition of the sample entropy of mean arterial pressure: Standard risk factors: 0.81 [0.76, 0.85], Standard risk factors and hypotension over time indices: 0.80 [0.75, 0.85], Standard risk factors and sample entropy of mean arterial pressure: 0.81 [0.76, 0.85] and Standard risk factors, sample entropy of mean arterial pressure and hypotension over time indices: 0.81 [0.76, 0.86]., Conclusion: Assessment of very short-term blood pressure variability does not improve the discrimination of postoperative acute kidney injury in patients undergoing non-cardiac surgery in this sample., (© 2024. The Author(s).)

The multivariate time series classification (MTSC) task aims to predict a class label for a given time series. Recently, modern deep learning-based approaches have achieved promising performance over traditional methods for MTSC tasks. The success of these approaches relies on access to the massive amount of labeled data (i.e., annotating or assigning tags to each sample that shows its corresponding category). However, obtaining a massive amount of labeled data is usually very time-consuming and expensive in many real-world applications such as medicine, because it requires domain experts' knowledge to annotate data. Insufficient labeled data prevents these models from learning discriminative features, resulting in poor margins that reduce generalization performance. To address this challenge, we propose a novel approach: supervised contrastive learning for time series classification (SupCon-TSC). This approach improves the classification performance by learning the discriminative low-dimensional representations of multivariate time series, and its end-to-end structure allows for interpretable outcomes. It is based on supervised contrastive (SupCon) loss to learn the inherent structure of multivariate time series. First, two separate augmentation families, including strong and weak augmentation methods, are utilized to generate augmented data for the source and target networks, respectively. Second, we propose the instance-level, and cluster-level SupCon learning approaches to capture contextual information to learn the discriminative and universal representation for multivariate time series datasets. In the instance-level SupCon learning approach, for each given anchor instance that comes from the source network, the low-variance output encodings from the target network are sampled as positive and negative instances based on their labels. However, the cluster-level approach is performed between each instance and cluster centers among batches, as opposed to the instance-level approach. The cluster-level SupCon loss attempts to maximize the similarities between each instance and cluster centers among batches. We tested this novel approach on two small cardiopulmonary exercise testing (CPET) datasets and the real-world UEA Multivariate time series archive. The results of the SupCon-TSC model on CPET datasets indicate its capability to learn more discriminative features than existing approaches in situations where the size of the dataset is small. Moreover, the results on the UEA archive show that training a classifier on top of the universal representation features learned by our proposed method outperforms the state-of-the-art approaches., Competing Interests: Conflict of interest Not applicable.

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 (V̇O 2 ), carbon dioxide production (V̇CO 2 ), 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 V̇O 2 , V̇CO 2 , 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 V̇O 2 . 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., (© 2022 The Authors. Physiological Reports published by Wiley Periodicals LLC on behalf of The Physiological Society and the American Physiological Society.)

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.