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Objectives: This study aimed to investigate the potential of Dendrobium officinale Kimura & Migo (DOKM) in ameliorating high cholesterol-induced atherosclerosis (AS) in zebrafish, focusing on the development of safe and effective drugs with low toxicity for AS treatment. Methods: A zebrafish AS model was established by feeding zebrafish a high cholesterol diet (HCD). Treatment was administered using 1 mg/L and 10 mg/L DOKM water extract. Fluorescence microscopy was utilised to observe the effects of DOKM on HCD-induced cholesterol accumulation, neutrophil accumulation, and macrophage infiltration in the vasculature. The impact of DOKM on HCD-induced disorders of lipid metabolism was assessed using Nile red staining. Total cholesterol (TC), triglycerides (TG), and low-density lipoprotein (LDL-C) levels in zebrafish were measured using kits. The expression of pcsk9, srebf2, and hmgcr genes in zebrafish was determined using RT-PCR. HE staining and Elvitegravir (EVG) staining were employed to assess the effect of DOKM on plaque formation in the blood vessels of AS zebrafish. Results: A total of 145 compounds were collected from Dendrobium officinale, along with 680 corresponding targets. Additionally, 1583 atherosclerosis-related targets were identified, with 202 representing interaction targets between Dendrobium officinale and atherosclerosis-related targets. Key compounds identified included Gigantol, Chrysotobibenzyl, Naringenin Chalcone, Hesperetin, and Tangeretin. The core targets of Dendrobium officinale for atherosclerosis treatment were STAT3, PTPN11, JAK2, epidermal growth factor receptor (EGFR), platelet-derived growth factor receptor alpha (PDGFRA), proto-oncogene tyrosine-protein kinase Src (SRC), STAT1, TP53, ESR1, and AKT1. GO functional enrichment analysis revealed a total of 2090 GO biological functional entries. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed 200 AS signalling pathways associated with DOKM treatment for atherosclerosis. DOKM significantly attenuated cholesterol accumulation, neutrophil aggregation, and macrophage infiltration in the blood vessels of AS zebrafish, ultimately leading to reduced plaque formation. AS zebrafish exhibited lipid metabolism disorders, characterised by significant increases in TC, TG, and LDL-C levels, as well as mRNA expression of pcsk9, srebf2, and hmgcr genes. Treatment with DOKM significantly improved these lipid metabolism disorders. Conclusion: DOKM effectively attenuates HCD-induced AS, and this therapeutic effect on AS may be related to its role in improving lipid metabolism dysfunction. [ABSTRACT FROM AUTHOR]

Background: The rationale for the pathogenesis of Alzheimer's disease (AD) is the production of amyloid-beta (Aβ) and its resultant toxic effects. Purpose: In this work, rats with AD were used as models to assess the modulatory effects of tanshinone IIA (tan IIA) in alleviating cognitive impairment, oxidative damage, inflammation and apoptosis. Materials and Methods: Aβ1–42 was injected bilaterally in the hippocampus to establish an AD rat model. The effects of tan II A on the learning and memory capacities of AD rats were detected by ethological experiments. Tan IIA's anti-inflammatory and antioxidant capacities were examined in this study using biochemical and immunohistochemical methods on rat hippocampus tissues. Results: The experimental data showed that the cognitive ability of rats could be significantly improved by tan IIA. Furthermore, fewer injured neuron apoptosis was demonstrated in the tan IIA rats than in the AD group. More importantly, on the one hand, we discovered that the tan IIA group had considerably lower levels of interleukin 1 beta, tumour necrosis factor-alpha and inducible nitric oxide synthase. On the other hand, tan IIA was discovered to prevent oxidative stress by raising glutathione and superoxide dismutase activity and lowering malondialdehyde, protein carbonyl and 8-hydroxy-2′-deoxyguanosine levels. Conclusion: Tan IIA can alleviate cognitive impairment and neuronal cell damage by inhibiting oxidative stress and inflammatory responses during AD. [ABSTRACT FROM AUTHOR]

Presently, road and traffic control construction on most university campuses cannot keep up with the growth of the universities. Campus roads are not very wide, crossings do not have lights, and there are no full-time traffic management personnel. Teachers and students are prone to forming a peak flow of people when going to and from classes. This has led to a constant stream of traffic accidents. It is critical to conduct a comprehensive analysis of this issue by utilizing voluminous data pertaining to school traffic incidents in order to safeguard the lives of faculty and students. In the case of domestic universities, fewer studies have studied knowledge graph construction methods for traffic safety incidents. In event knowledge graph construction, the reasonable release and recycling of computational resources are inefficient, and existing entity–relationship joint extraction methods are unable to deal with ternary overlapping and entity boundary ambiguity problems in relationship extraction. In response to the above problems, this paper proposes a knowledge graph construction method for university on-campus traffic safety events with improved dynamic resource scheduling algorithms and multi-layer semantic graph convolutional neural networks. The experiment's results show that the proposed dynamic computational resource scheduling method increases GPU and CPU use by 25% and 9%. On the public dataset, the proposed data extraction model's F1 scores for event triples increase by 1.3% on the NYT dataset and by 0.4% on the WebNLG dataset. This method can help the relevant university personnel in dealing with unexpected traffic incidents and reduce the impact on public opinion. [ABSTRACT FROM AUTHOR]