Biological functions and potential mechanisms of miR­143­3p in cancers (Review).

In recent years, microRNAs (miRNAs or miRs) have been increasingly studied for their role in cancer and have shown potential as cancer biomarkers. miR­143­3p and miR­143­5p are the mature miRNAs derived from pre­miRNA­143. At present, there are numerous studies on the function of miR­143­3p in cancer progression, but there are no systematic reviews describing the function of miR­143­3p in cancer. It is widely considered that miR­143­3p is downregulated in most malignant tumors and that upstream regulators can act on this gene, which in turn regulates the corresponding target to act on the tumor. In addition, miRNA­143­3p can regulate target genes to affect the biological process of tumors through various signaling pathways, such as the PI3K/Akt, Wnt/ß­catenin, AKT/STAT3 and Ras­Raf­MEK­ERK pathways. The present review comprehensively described the biogenesis of miR­143­3p, the biological functions of miR­143­3p and the related roles and mechanisms in different cancer types. The potential of miR­143­3p as a biomarker for cancer was also highlighted and valuable future research directions were discussed.

Research Progress on the Role of Epigenetic Methylation Modification in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) stands as the prevailing form of primary liver cancer, characterized by a poor prognosis and high mortality rate. A pivotal factor in HCC tumorigenesis is epigenetics, specifically the regulation of gene expression through methylation. This process relies significantly on the action of proteins that modify methylation, including methyltransferases, their associated binding proteins, and demethylases. These proteins are crucial regulators, orchestrating the methylation process by regulating enzymes and their corresponding binding proteins. This orchestration facilitates the reading, binding, detection, and catalysis of gene methylation sites. Methylation ences the development, prolisignificantly influferation, invasion, and prognosis of HCC. Furthermore, methylation modification and its regulatory mechanisms activate distinct biological characteristics in HCC cancer stem cells, such as inducing cancer-like differentiation of stem cells. They also influence the tumor microenvironment (TME) in HCC, modulate immune responses, affect chemotherapy resistance in HCC patients, and contribute to HCC progression through signaling pathway feedback. Given the essential role of methylation in genetic information, it holds promise as a potential tool for the early detection of HCC and as a target to improve drug resistance and promote apoptosis in HCC cells.

Research progress in the metabolic reprogramming of hepatocellular carcinoma (Review).

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and its morbidity is increasing worldwide due to increasing prevalence. Metabolic reprogramming has been recognized as a hallmark of cancer and serves a role in cancer progression. Glucose, lipids and amino acids are three major components whose altered metabolism can directly affect the energy production of cells, including liver cancer cells. Nutrients and energy are indispensable for the growth and proliferation of cancer cells, thus altering the metabolism of hepatoma cells can inhibit the progression of HCC. The present review summarizes recent studies on tumour regulatory molecules, including numerous noncoding RNAs, oncogenes and tumour suppressors, which regulate the metabolic activities of glucose, lipids and amino acids by targeting key enzymes, signalling pathways or interactions between the two. These regulatory molecules can regulate the rapid proliferation of cancer cells, tumour progression and treatment resistance. It is thought that these tumour regulatory factors may serve as therapeutic targets or valuable biomarkers for HCC, with the potential to mitigate HCC drug resistance. Furthermore, the advantages and disadvantages of metabolic inhibitors as a treatment approach for HCC, as well as possible solutions are discussed, providing insights for developing more effective treatment strategies for HCC.

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.