PURPOSE: The present study seeks to determine if TLP would prevent HFD-induced NAFLD in vivo and its underlying mechanisms from the perspectives of gut microbiota, metabolites, and hepatic inflammation.
METHODS: TLP was subjected to extraction and chemo-profiling, and in vivo evaluation in HFD-fed rats on hepatic lipid and inflammation, intestinal microbiota, short-chain fatty acids (SCFAs) and permeability, and body weight and fat content profiles.
RESULTS: The TLP was primarily constituted of gallic acid, corilagin and chebulagic acid. Orally administered HFD-fed rats with TLP were characterized by the growth of Ligilactobacillus and Akkermansia, and SCFAs (acetic/propionic/butyric acid) secretion which led to increased claudin-1 and zonula occludens-1 expression that reduced the mucosal permeability to migration of lipopolysaccharides (LPS) into blood and liver. Coupling with hepatic cholesterol and triglyceride lowering actions, the TLP mitigated both inflammatory (ALT, AST, IL-1β, IL-6 and TNF-α) and pro-inflammatory (TLR4, MYD88 and NF-κB P65) activities of liver, and sequel to histopathological development of NAFLD in a dose-dependent fashion.
CONCLUSION: TLP is promisingly an effective therapy to prevent NAFLD through modulating gut microbiota, mucosal permeability and SCFAs secretion with liver fat and inflammatory responses.
METHODS: In contrast, ViTs have demonstrated proficiency in capturing global signal patterns. In light of these observations, we propose a novel approach to enhance AD risk assessment. Our proposition involves a hybrid architecture, merging the strengths of CNNs and ViTs to compensate for their respective feature extraction limitations. Our proposed Dual-Branch Feature Fusion Network (DBN) leverages both CNN and ViT components to acquire texture features and global semantic information from EEG signals. These elements are pivotal in capturing dynamic electrical signal changes in the cerebral cortex. Additionally, we introduce Spatial Attention (SA) and Channel Attention (CA) blocks within the network architecture. These attention mechanisms bolster the model's capacity to discern abnormal EEG signal patterns from the amalgamated features. To make well-informed predictions, we employ a two-factor decision-making mechanism. Specifically, we conduct correlation analysis on predicted EEG signals from the same subject to establish consistency.
RESULTS: This is then combined with results from the Clinical Neuropsychological Scale (MMSE) assessment to comprehensively evaluate the subject's susceptibility to AD. Our experimental validation on the publicly available OpenNeuro database underscores the efficacy of our approach. Notably, our proposed method attains an impressive 80.23% classification accuracy in distinguishing between AD, Frontotemporal dementia (FTD), and Normal Control (NC) subjects.
DISCUSSION: This outcome outperforms prevailing state-of-the-art methodologies in EEG-based AD prediction. Furthermore, our methodology enables the visualization of salient regions within pathological images, providing invaluable insights for interpreting and analyzing AD predictions.
METHODS: This study followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A systematic search of electronic databases was also conducted, including EBSCOhost, Scopus, PubMed, Web of Science, CNKI, Google Scholar, and Wanfang. The Physiotherapy Evidence Database (PEDro) scale was an effective indicator to evaluate the quality of studies included in the systematic review.
RESULTS: This systematic review included 474 participants aged 8-24 years old. The intervention period for most studies was 12 weeks. Among the included studies, 6 focused on muscle strength, 4 on jumping performance, and 11 on functional movement screen.
CONCLUSION: These articles have been analysed, and the positive impact of functional training interventions on muscle strength, jumping, and functional movement screen of wushu athletes has been verified.
METHODS: We examined e-cigarette market data from the Euromonitor Global Market Information Database (GMID) Passport database, searched in the academic literature, grey literature and news archives for any reports or studies of e-cigarette related diseases or injuries, e-cigarette marketing, and e-cigarette policy responses in Southeast Asian countries, and browsed the websites of online e-cigarette retailers catering to the region's active e-cigarette markets.
RESULTS: In 2019, e-cigarettes were sold in six Southeast Asian markets with a total market value of $595 million, projected to grow to $766 million by 2023. E-commerce is a significant and growing sales channel in the region, with most of the popular or featured brands in online shops originating from China. Southeast Asian youth are targeted with a wide variety of flavours, trendy designs and point of sale promotions, and several e-cigarette related injuries and diseases have been reported in the region. Policy responses vary considerably between countries, ranging from strict bans to no or partial regulations.
CONCLUSION: Although Southeast Asia's e-cigarette market is relatively nascent, this is likely to change if transnationals invest more heavily in the region. Populous countries with weak e-cigarette regulations, notably Indonesia, Malaysia, Vietnam and the Philippines, are desirable targets for the transnationals. Regulatory action is needed to prevent e-cigarette use from becoming entrenched into these societies, especially among young people.