Isobavachalcone attenuates liver fibrosis via activation of the Nrf2/HO-1 pathway in rats.

Liver fibrosis, a progression of chronic liver disease, is a significant concern worldwide due to the lack of effective treatment modalities. Recent studies have shown that natural products play a crucial role in preventing and treating liver fibrosis. Isobavachalcone (IBC) is a chalcone compound with anti-inflammatory, antioxidant, and anti-cancer properties. However, its potential antifibrotic effects remain to be elucidated. This study aimed to investigate the antifibrotic effects of IBC on liver fibrosis and its underlying mechanisms in rats. The results showed that IBC significantly ameliorated the pathological damage and collagen deposition in liver tissues; it also reduced the levels of hydroxyproline (HYP), alanine aminotransferase (ALT), and aspartate aminotransferase (AST). In addition, IBC activated Nuclear factor E2-associated factor 2/Hemeoxygenase-1 (Nrf2/HO-1) signaling, leading to the nuclear translocation of Nrf2. This translocation subsequently increased the levels of superoxide dismutase (SOD) and glutathione (GSH) and decreased the levels of malondialdehyde (MDA) and reactive oxygen species (ROS), thereby alleviating oxidative stress-induced damage. Moreover, it inhibited the expression of nuclear factor kappa B (NF-κB), which further reduced the levels of downstream inflammatory factors, such as tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and IL-1 beta (IL-1ß), thereby suppressing the activation of HSCs and weakening liver fibrosis. In HSC-T6 cell experiments, changes observed in inflammatory responses, oxidative stress indicators, and protein expression were consistent with the in vivo results. Furthermore, the Nrf2 inhibitor (ML385) attenuated the effect of IBC on inhibiting the activation of quiescent HSCs. Consequently, IBC could alleviate liver fibrosis by activating Nrf2/ HO-1 signaling.

Hearables: Heart Rate Variability from Ear Electrocardiogram and Ear Photoplethysmogram (Ear-ECG and Ear-PPG).

This work aims to classify physiological states using heart rate variability (HRV) features extracted from electrocardiograms recorded in the ears (ear-ECG). The physiological states considered in this work are: (a) normal breathing, (b) controlled slow breathing, and (c) mental exercises. Since both (b) and (c) cause higher variance in heartbeat intervals, breathing-related features (SpO2 and mean breathing interval) from the ear Photoplethysmogram (ear-PPG) are used to facilitate classification. This work: 1) proposes a scheme that, after initialization, automatically extracts R-peaks from low signal-to-noise ratio ear-ECG; 2) verifies the feasibility of extracting meaningful HRV features from ear-ECG; 3) quantitatively compares several ear-ECG sites; and 4) discusses the benefits of combining ear-ECG and ear-PPG features.

Long-term liver lesion tracking in contrast-enhanced ultrasound videos via a siamese network with temporal motion attention.

Propose: Contrast-enhanced ultrasound has shown great promises for diagnosis and monitoring in a wide range of clinical conditions. Meanwhile, to obtain accurate and effective location of lesion in contrast-enhanced ultrasound videos is the basis for subsequent diagnosis and qualitative treatment, which is a challenging task nowadays. Methods: We propose to upgrade a siamese architecture-based neural network for robust and accurate landmark tracking in contrast-enhanced ultrasound videos. Due to few researches on it, the general inherent assumptions of the constant position model and the missing motion model remain unaddressed limitations. In our proposed model, we overcome these limitations by introducing two modules into the original architecture. We use a temporal motion attention based on Lucas Kanade optic flow and Karman filter to model the regular movement and better instruct location prediction. Moreover, we design a pipeline of template update to ensure timely adaptation to feature changes. Results: Eventually, the whole framework was performed on our collected datasets. It has achieved the average mean IoU values of 86.43% on 33 labeled videos with a total of 37,549 frames. In terms of tracking stability, our model has smaller TE of 19.2 pixels and RMSE of 27.6 with the FPS of 8.36 ± 3.23 compared to other classical tracking models. Conclusion: We designed and implemented a pipeline for tracking focal areas in contrast-enhanced ultrasound videos, which takes the siamese network as the backbone and uses optical flow and Kalman filter algorithm to provide position prior information. It turns out that these two additional modules are helpful for the analysis of CEUS videos. We hope that our work can provide an idea for the analysis of CEUS videos.