Mechanisms and Therapeutic Strategies for MAFLD Targeting TLR4 Signaling Pathways.

BACKGROUND: Metabolic-associated fatty liver disease (MAFLD) is one of the most common chronic liver diseases. The underlying pathophysiological mechanisms are intricate and involve various factors. Unfortunately, there is currently a lack of available effective treatment options. Toll-like receptors (TLRs) are a group of pattern-recognition receptors that are responsible for activating the innate immune system. Research has demonstrated that TLR4 plays a pivotal role in the progression of MAFLD by facilitating the pathophysiological mechanisms. SUMMARY: Lipid peroxidation, pro-inflammatory factors, insulin resistance (IR), and dysbiosis of intestinal microbiota are considered as the pathogenic mechanisms of MAFLD. This review summarizes the impact of TLR4 signaling pathways on the progression of MAFLD, specifically in relation to lipid metabolic disorders, IR, oxidative stress, and gut microbiota disorders. Additionally, we emphasize the potential therapeutic approaches for MAFLD that target TLR4 signaling pathways, including the use of plant extracts, traditional Chinese medicines, probiotics, pharmaceuticals such as peroxisome proliferator-activated receptor antagonists and farnesol X agonists, and lifestyle modifications such as dietary changes and exercise also considered. Furthermore, TLR4 signaling pathways have also been linked to the lean MAFLD. KEY MESSAGES: TLR4 plays a crucial role in MAFLD by triggering IR, buildup of lipids, imbalance in gut microbiota, oxidative stress, and initiation of immune responses. The mitigation of MAFLD can be accomplished by suppressing the TLR4 signaling pathway. In the future, it could potentially emerge as a therapeutic target for the condition.

Deep sparse transfer learning for remote smart tongue diagnosis.

People are exploring new ideas based on artificial intelligent infrastructures for immediate processing, in which the main obstacles of widely-deploying deep methods are the huge volume of neural network and the lack of training data. To meet the high computing and low latency requirements in modeling remote smart tongue diagnosis with edge computing, an efficient and compact deep neural network design is necessary, while overcoming the vast challenge on modeling its intrinsic diagnosis patterns with the lack of clinical data. To address this challenge, a deep transfer learning model is proposed for the effective tongue diagnosis, based on the proposed similar-sparse domain adaptation (SSDA) scheme. Concretely, a transfer strategy of similar data is introduced to efficiently transfer necessary knowledge, overcoming the insufficiency of clinical tongue images. Then, to generate simplified structure, the network is pruned with transferability remained in domain adaptation. Finally, a compact model combined with two sparse networks is designed to match limited edge device. Extensive experiments are conducted on the real clinical dataset. The proposed model can use fewer training data samples and parameters to produce competitive results with less power and memory consumptions, making it possible to widely run smart tongue diagnosis on low-performance infrastructures.

A Unified Smart Chinese Medicine Framework for Healthcare and Medical Services.

Smart Chinese medicine has emerged to contribute to the evolution of healthcare and medical services by applying machine learning together with advanced computing techniques like cloud computing to computer-aided diagnosis and treatment in the health engineering and informatics. Specifically, smart Chinese medicine is considered to have the potential to treat difficult and complicated diseases such as diabetes and cancers. Unfortunately, smart Chinese medicine has made very limited progress in the past few years. In this paper, we present a unified smart Chinese medicine framework based on the edge-cloud computing system. The objective of the framework is to achieve computer-aided syndrome differentiation and prescription recommendation, and thus to provide pervasive, personalized, and patient-centralized services in healthcare and medicine. To accomplish this objective, we integrate deep learning and deep reinforcement learning into the traditional Chinese medicine. Furthermore, we propose a multi-modal deep computation model for syndrome recognition that is a crucial part of syndrome differentiation. Finally, we conduct experiments to validate the proposed model by comparing with the staked auto-encoder and multi-modal deep learning model for syndrome recognition of hypertension and cold.

The effect of Chinese Jinzhida recipe on the hippocampus in a rat model of diabetes-associated cognitive decline.

BACKGROUND: To investigate the effects of treatment with Multi component Chinese Medicine Jinzhida (JZD) on behavioral deficits in diabetes-associated cognitive decline (DACD) rats and verify our hypothesis that JZD treatment improves cognitive function by suppressing the endoplasmic reticulum stress (ERS) and improving insulin signaling transduction in the rats' hippocampus. METHODS: A rat model of type 2 diabetes mellitus (T2DM) was established using high fat diet and streptozotocin (30 mg/kg, ip). Insulin sensitivity was evaluated by the oral glucose tolerance test and the insulin tolerance test. After 7 weeks, the T2DM rats were treated with JZD. The step-down test and Morris water maze were used to evaluate behavior in T2DM rats after 5 weeks of treatment with JZD. Levels of phosphorylated proteins involved in the ERS and in insulin signaling transduction pathways were assessed by Western blot for T2DM rats' hippocampus. RESULTS: Compared to healthy control rats, T2DM rats initially showed insulin resistance and had declines in acquisition and retrieval processes in the step-down test and in spatial memory in the Morris water maze after 12 weeks. Performance on both the step-down test and Morris water maze tasks improved after JZD treatment. In T2DM rats, the ERS was activated, and then inhibited the insulin signal transduction pathways through the Jun NH2-terminal kinases (JNK) mediated. JZD treatment suppressed the ERS, increased insulin signal transduction, and improved insulin resistance in the rats' hippocampus. CONCLUSIONS: Treatment with JZD improved cognitive function in the T2DM rat model. The possible mechanism for DACD was related with ERS inducing the insulin signal transduction dysfunction in T2DM rats' hippocampus. The JZD could reduce ERS and improve insulin signal transduction and insulin resistance in T2DM rats' hippocampus and as a result improved the cognitive function.