Introduction Photoplethysmography (PPG) is used as a surrogate of electrocardiograms (ECG) for heart rate variability (HRV) analysis or respiratory rate monitoring. PPG is a more convenient way to measure HRV than ECG at rest, since respiration could be a confounding factor in HRV evaluation. However, it remains unclear whether or not controlled breathing affects breath-volume and breathing rate when HRV and pulse rate variability (PRV) are measured in different situations. Consciously controlled breathing was performed to alter the autonomic nervous states of subjects caused by respiratory sinus arrhythmia (RSA). The aim of this study was to investigate the coherence between parameters derived from ECG and PPG on healthy subjects with/without controlled breathing. Method With 30 healthy volunteers, we measured their respiratory frequency and recorded their ECG and PPG signals during spontaneous breathing and controlled breathing, including natural paced breathing, rapid and deep breathing, slow and deep breathing, rapid and shallow breathing, and slow and shallow breathing. Results Obvious coherence was observed between pulse rate and heart rate in both spontaneous breathing and controlled breathing tasks. However, a comparison of PRV and HRV indices demonstrated significant differences during controlled breathing. The results based on time domain and nonlinear method analysis showed that the frequency-dependent changes have more of an impact. The results also indicated that breathing corresponded well in ECG-derived parameters comparing with PPG-derived ones. Conclusion We concluded that PPG-based devices cannot be applied as a precision screening tool to detect HRV, particularly during the cardiopulmonary analysis for the controlled breathing maneuver.

Purpose Management of peptic ulcer bleeding is clinically challenging. Accurate characterization of the bleeding during endoscopy is key for endoscopic therapy. This study aimed to assess whether a deep learning model can aid in the classification of bleeding peptic ulcer disease. Methods Endoscopic still images of patients (n = 1694) with peptic ulcer bleeding for the last 5 years were retrieved and reviewed. Overall, 2289 images were collected for deep learning model training, and 449 images were validated for the performance test. Two expert endoscopists classified the images into different classes based on their appearance. Four deep learning models, including Mobile Net V2, VGG16, Inception V4, and ResNet50, were proposed and pre-trained by ImageNet with the established convolutional neural network algorithm. A comparison of the endoscopists and trained deep learning model was performed to evaluate the model’s performance on a dataset of 449 testing images. Results The results first presented the performance comparisons of four deep learning models. The Mobile Net V2 presented the optimal performance of the proposal models. The Mobile Net V2 was chosen for further comparing the performance with the diagnostic results obtained by one senior and one novice endoscopists. The sensitivity and specificity were acceptable for the prediction of “normal” lesions in both 3-class and 4-class classifications. For the 3-class category, the sensitivity and specificity were 94.83% and 92.36%, respectively. For the 4-class category, the sensitivity and specificity were 95.40% and 92.70%, respectively. The interobserver agreement of the testing dataset of the model was moderate to substantial with the senior endoscopist. The accuracy of the determination of endoscopic therapy required and high-risk endoscopic therapy of the deep learning model was higher than that of the novice endoscopist. Conclusions In this study, the deep learning model performed better than inexperienced endoscopists. Further improvement of the model may aid in clinical decision-making during clinical practice, especially for trainee endoscopist.

A simple-stepped growth process for the synthesis of carbon nanotubes that exhibit excellent field emission properties is reported. In the process, the growth was interrupted, and during the interruption the catalyst was re-activated in situ, resulting in enhanced growth of the CNTs after the interruption. A film of CNTs re-grows on top of an existing CNT film at much higher rates, which can be up to 669% higher. The tubular structure continues during the re-growth. The structural continuity creates an opportunity for the fabrication of junction CNTs for nano-electronic applications. The resulting CNTs also have excellent field emission properties, exhibiting an extremely low turn-on field of 0.10 V microm(-1).

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With the decreasing incidence of peptic ulcer bleeding (PUB) over the past two decades, the clinician experience of managing patients with PUB has also declined, especially for young endoscopists. A patient with PUB management requires collaborative care involving the emergency department, gastroenterologist, radiologist, and surgeon, from initial assessment to hospital discharge. The application of artificial intelligence (AI) methods has remarkably improved people’s lives. In particular, AI systems have shown great potential in many areas of gastroenterology to increase human performance. Colonoscopy polyp detection or diagnosis by an AI system was recently introduced for commercial use to improve endoscopist performance. Although PUB is a longstanding health problem, these newly introduced AI technologies may soon impact endoscopists’ clinical practice by improving the quality of care for these patients. To update the current status of AI application in PUB, we reviewed recent relevant literature and provided future perspectives that are required to integrate such AI tools into real-world practice.