inhibits M2 tumor-associated macrophage polarization Wnt/β-catenin pathway modulation to suppress liver cancer.

Background: (CB), used in traditional Chinese medicine, exhibits anti-tumor effects in various cancer models. It also constitutes an integral component of a compound formulation known as Pien Tze Huang, which is indicated for the treatment of liver cancer. However, its impact on the liver cancer tumor microenvironment, particularly on tumor-associated macrophages (TAMs), is not well understood.

Plant-derived exosome-like nanoparticles for microRNA delivery in cancer treatment.

Plant-derived exosome-like nanoparticles (PELNs) are natural nanocarriers and effective delivery systems for plant microRNAs (miRNAs). These PELN-carrying plant miRNAs can regulate mammalian genes across species, thereby increasing the diversity of miRNAs in mammals and exerting multi-target effects that play a crucial role in diseases, particularly cancer. PELNs demonstrate exceptional stability, biocompatibility, and targeting capabilities that protect and facilitate the up-take and cross-kingdom communication of plant miRNAs in mammals.

Small RNA sequencing highlights a potential regulatory network mediated by Gecko miRNA affecting the prognosis of hepatocellular carcinoma.

Background: Gecko has been widely documented in Chinese scientific literature as an anti-tumor agent for various illnesses for thousands of years, and more recently, it has been examined for its anti-tumor effects on several cancers. The effect of Gecko microRNAs (miRNAs) on hepatocellular carcinoma (HCC) has not yet been reported.

Objectives: This study was designed to identify miRNAs in Gecko through small RNA sequencing and utilize bioinformatics techniques to construct a potential regulatory network and explore the possible mechanisms of exogenous miRNAs involved in HCC.

Gear Fault Diagnosis and Life Prediction of Petroleum Drilling Equipment Based on SOM Neural Network.

In order to solve the problem that variable working conditions and fault types cannot be diagnosed in gear fault diagnosis of petroleum drilling equipment, four kinds of faults, namely, gear broken tooth, gear crack, gear pitting, and gear wear, are studied in this paper. Based on the SOM neural network algorithm, an intelligent diagnosis model of gear fault is proposed, and the PCA method is used to reduce data dimension and fuse features. The state index of life prediction is determined, and the remaining service life prediction of gear transmission system is predicted based on exponential degradation model.