Silybin prevented avermectin-induced cardiotoxicity in carp by modulating oxidative stress, inflammation, endoplasmic reticulum stress, mitochondrial apoptosis, and autophagy.

Avermectin is one of the widely used anthelmintics in aquaculture and exhibits substantial toxicity to aquatic organisms. Silybin is extensively used for its anti-inflammatory, antioxidant and anti-apoptotic biological properties. Heart is essential for the survival of fish and plays a vital role in pumping blood oxygen and nutrients. Residual avermectin in water poses harm to carp. However, there is still insufficient research on whether silybin can mitigate the toxicity of avermectin to carp heart tissues. In this research, we established a model involving carp subjected to acute avermectin exposure and administered diets containing silybin to explore the potential protective effects of silybin against avermectin-induced cardiotoxicity. The results revealed that avermectin induced oxidative stress, inflammation, endoplasmic reticulum (ER) stress, mitochondrial pathway apoptosis and autophagy in the cardiac tissues of carp. Compared with the avermectin group, silybin significantly reduced ROS accumulation in cardiac tissues, restored antioxidant enzyme activity, inhibited mRNA transcript levels of pro-inflammatory-related factors, and attenuated ER stress, mitochondrial pathway apoptosis and autophagy. Protein-protein interaction (PPI) analysis demonstrated that silybin mitigated avermectin-induced cardiac oxidative stress, inflammation, ER stress, mitochondrial pathway apoptosis and autophagy. Silybin exerted anti-inflammatory effects through the Nuclear Factor kappa B (NF-κB) pathway, antioxidant effects through the Nuclear factor erythroid 2-related factor 2 (Nrf2) - Kelch-like ECH-associated protein 1 (Keap1) pathway, alleviated cardiac ER stress through the Glucose-regulated protein 78 (GRP78)/Activating Transcription Factor 6 (ATF6)/C/EBP homologous protein (CHOP) axis, suppressed apoptosis through the mitochondrial pathway, and inhibited excessive autophagy initiation through the PTEN-induced putative kinase 1 (PINK1)/Parkin RBR E3 ubiquitin protein ligase (PARKIN) signaling pathway. This study provided evidence supporting the protective effect of silybin against avermectin-induced cardiotoxicity in carp, highlighting its potential as a dietary additive to protect fish from adverse effects caused by avermectin exposure.

Detection and Evaluation for High-Quality Cardiopulmonary Resuscitation Based on a Three-Dimensional Motion Capture System: A Feasibility Study.

High-quality cardiopulmonary resuscitation (CPR) and training are important for successful revival during out-of-hospital cardiac arrest (OHCA). However, existing training faces challenges in quantifying each aspect. This study aimed to explore the possibility of using a three-dimensional motion capture system to accurately and effectively assess CPR operations, particularly about the non-quantified arm postures, and analyze the relationship among them to guide students to improve their performance. We used a motion capture system (Mars series, Nokov, China) to collect compression data about five cycles, recording dynamic data of each marker point in three-dimensional space following time and calculating depth and arm angles. Most unstably deviated to some extent from the standard, especially for the untrained students. Five data sets for each parameter per individual all revealed statistically significant differences (p < 0.05). The correlation between Angle 1' and Angle 2' for trained (rs = 0.203, p < 0.05) and untrained students (rs = -0.581, p < 0.01) showed a difference. Their performance still needed improvement. When conducting assessments, we should focus on not only the overall performance but also each compression. This study provides a new perspective for quantifying compression parameters, and future efforts should continue to incorporate new parameters and analyze the relationship among them.

Impact of unrecovered shale gas reserve on methane emissions from abandoned shale gas wells.

Shale gas, with its abundance and lower carbon footprint compared to other fossil fuels, is an important bridge fuel in the ongoing energy transition. However, a notable concern in shale gas exploration is fugitive methane emissions during the extraction, development, and transport of natural gas. While most existing works evaluate methane emissions released by well fracking, completion and operation, the greenhouse footprint of unproductive shale gas wells (often abandoned or orphaned) has received little scrutiny. A large fraction of these emissions from abandoned shale gas wells are due to the diffusive transport of methane trapped in nanoporous shale matrix, which is poorly understood. Here, we develop a theoretical kinetic approach to predict methane diffusive flux from heterogeneous shale matrix. Our theoretical model is based on a layer sequence formulation and accurately considers multiple flow mechanisms, including viscous flow, gas slippage, and Knudsen diffusion and their mutual interactions. The model is validated against the observed methane diffusion data obtained from high-pressure and high-temperature experimental measurements on Marcellus shale. We find that methane diffusive flux increases as reservoir pressure decreases. We estimate methane emission due to diffusive transport up to 20 × 103 m3 per well per day, which is comparable to emissions from flowback fluid. For the first time, unrecovered natural gas in the shale matrix is demonstrated to be the main source of methane emissions from abandoned shale gas wells. Given the long-lasting nature of diffusive transport to shale gas seepage, it is suggested that regulatory requirements should be implemented to provide long-term monitoring of methane emissions from abandoned shale gas wells.

Systematic Development of an AI Chatbot for Physical Activity Habit Formation in Prehypertension Individual Integrating Needs, Theories, and Evidence.

Prehypertension, an early stage in the development of hypertension, impacts a substantial segment of the adult population worldwide. Addressing this issue, our study introduces HabitBot, an AI-driven chatbot tailored to encourage physical activity (PA) habits among individuals with prehypertension. HabitBot combines natural language processing with multidisciplinary approaches, drawing from both theoretical frameworks and empirical studies. The chatbot development followed a systematic, five-phase process: comprehensive needs assessment, literature review on behavior change theories, analysis for selecting effective behavior change techniques (BCTs), prototype design through intervention mapping, and refining the intervention based on user feedback. The outcome includes a prototype that integrates the Health Action Process Approach and Habit Formation Theory, utilizing twelve identified BCTs effective in fostering PA habits. User feedback further refined the chatbot across multiple dimensions such as user interface, content accessibility, and privacy. HabitBot exemplifies an innovative integration of behavior change strategies with advanced language model technology, paving the way for digital health interventions in chronic disease prevention. Future studies should assess its long-term efficacy in habit formation and explore its applicability to various demographic groups.

Detection and Evaluation for High-Quality CPR Based on Motion Capture Devices: A Preliminary Test.

High-quality cardiopulmonary resuscitation (CPR) is important for successful revival. This test aimed to accurately and effectively assess parameters in CPR operations, particularly focusing on the non-quantified arm posture, to assist instructors in guiding students to enhance the quality of their performance. We used motion capture system (Mars series, Nokov, China) to collect compression data from four trained students about five rounds of CPR, recording dynamic data of each marker in three-dimensional space following time. Using the processed data, we calculated compression depth, frequency, and arm angles. Their performance still needed improvement, and there were variations in data within individuals. This suggests that when instructors conduct assessments, they should focus on not only the overall performance but also each compression. This experiment provides a new perspective for quantifying compression parameters, and future efforts should continue to optimize and incorporate new parameters for assessment.

The Acceptance of Using Large Language Model Among Older Adults: A Study Based on TAM.

This study investigates the acceptance of large language models (LLMs) among older adults using the Technology Acceptance Model (TAM). The research, conducted through a cross-sectional survey, explores the influence of perceived ease of use and perceived usefulness on intension to use among older adults. The results show that the subjective norm, image, job relevance, output quality, result demonstrability, perceived ease of use have a significant positive and direct impact on perceived usefulness (ß=0.138, 0.240, 0.213, 0.280, 0.181, 0.176, P<0.05). Perceived ease of use and perceived usefulness have a significant positive and direct impact on Intension to use (ß=0.335, 0.307, P<0.05). The study's practical implications highlight the need for tailored chatbots, offering valuable insights for developers and policymakers aiming to enhance the integration of innovative technologies among older populations.

Effects of Mobile Applications on Weight-Related, Behavior and Metabolic Outcomes for Adults with Overweight and Obesity: A Systematic Review and Meta-Analysis.

The meta-analysis aimed to explore the effects of mobile phone applications on weight-related, behavior, and metabolic outcomes among adults with overweight and obesity. Six databases were searched for relevant randomized controlled trials (RCTs) published between January 1, 2010 and November 7, 2023 in English. Two independent authors conducted study selection, data extraction, quality assessment. The effect size of interventions was calculated using mean difference. A random-effects model was applied for data analysis. A total of 27 studies were included. The results indicated that mobile phone application intervention reduced weight (MD=-1.38 kg, P<0.001, 95% CI -1.97 to -0.80), BMI (MD=-0.44 kg/m2, P<0.001, 95% CI -0.57 to -0.30), WC (MD=-2.13 cm, P=0.004, 95% CI -3.57 to -0.69), fat mass, and DBP (MD=-2.04 mmHg, P=0.01, 95% CI -3.65 to 0.44) with statistical significance. Future studies could consider how to optimize app interventions through behavior change strategies to enhance their overall effectiveness.