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Introduction: Aconiti Lateralis Radix Praeparata (lateral roots of Aconitum carmichaelii Debeaux, Fuzi), is commonly used to treat various cardiovascular diseases, particularly heart failure. However, its strong cardiotoxicity limits its clinical applicability. Glycyrrhizae radix et rhizoma, (the root of Glycyrrhiza uralensis Fisch., Gancao), is known to synergistically increase the cardiotonic effects of Fuzi and alleviate the myocardial injury caused by Fuzi to some extent. However, the detailed mechanism via which the combination of Fuzi and Gancao reduces toxicity and increases or preserves the efficacy of Fuzi requires further investigation. Methods: Oxidative stress injury models in H9C2 cells and mice with acute heart failure were established to evaluate the optimal synergistic protective concentration of Fuziline and Glycyrrhetinic acid (GA). A GA probe was then synthesized and used for target fishing using chemical and biological methods. Finally, the target and its function were verified using fluorescence co-localization, Western blotting, protein interaction analysis, molecular docking, and calcium ion imaging. Results: The best pharmacodynamic potential was achieved with a 1:1 or 2:1 ratio of Fuziline and GA concentrations. At these ratios, they regulated the protein levels of the downstream players of the Ca2+ signaling pathway via MDH2 and CALR, thereby balancing Ca2+ homeostasis in the myocardial tissue and mitigating the effects of heart failure. Conclusion: This study aimed to investigate the compatibility of Fuziline and GA, the active metabolites of a traditional Chinese medicine (TCM) pair, in exerting their cardiac effects, identify the direct biological targets and verify the mechanism of compatibility. [ABSTRACT FROM AUTHOR]

The dynamics of soil arthropod communities in annual monoculture grasslands is still unclear, which restricts the understanding of the degradation mechanism of cultivated grasslands. We cultivated two annual gramineae species, Lolium multiflorum and Avena sativa, separately in Hongyuan County, located on the eastern edge of the Qinghai-Tibet Plateau, in April 2019. We investigated soil arthropods, plant communities and soil properties in the cultivated grasslands and natural grassland in the late September every year from 2019 to 2022. The results showed that : 1) The taxonomic composition of soil arthropod communities differed significantly among three grasslands and sampling years. 2) There was no significant difference in the density, taxonomic richness, Shannon index and evenness index of soil arthropod communities among three grasslands. 3) The density of soil arthropod communities significantly fluctuated across years in three grasslands, and the taxonomic richness and Shannon index decreased significantly in the L. multiflorum and A. sativa grasslands, with the evenness index declining significantly only in the fourth year. The Shannon index fluctuated significantly and the evenness index varied little in natural grassland. 4) The above- and below-ground biomass, the contents of soil total P, total K and available N were the main factors influencing the taxonomic composition, density and diversity indices of soil arthropod communities. The results suggested that the cultivation of annual gramineae grasslands have significant effects on taxonomic composition, but not on density and diversity of soil arthropod communities, and those variables change significantly across different years. [ABSTRACT FROM AUTHOR]

One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to be further investigated, and drugs designed based on targets may not always have the desired drug efficacy. The emergence of machine learning provides a powerful tool to overcome the challenge. Herein, a machine learning‐based strategy is developed for de novo generation of novel compounds with drug efficacy termed DTLS (Deep Transfer Learning‐based Strategy) by using dataset of disease‐direct‐related activity as input. DTLS is applied in two kinds of disease: colorectal cancer (CRC) and Alzheimer's disease (AD). In each case, novel compound is discovered and identified in in vitro and in vivo disease models. Their mechanism of actionis further explored. The experimental results reveal that DTLS can not only realize the generation and identification of novel compounds with drug efficacy but also has the advantage of identifying compounds by focusing on protein targets to facilitate the mechanism study. This work highlights the significant impact of machine learning on the design of novel compounds with drug efficacy, which provides a powerful new approach to drug discovery. [ABSTRACT FROM AUTHOR]

Accurate clinical diagnosis of the stage of tumor development is essential for formulating a treatment plan. However, the stage of tongue tumor development among malignant, benign and leukoplakia is easily misdiagnosed, resulting differing treatment approaches, taking patients in danger and preventing them from receiving the deserved care. This study aimed to establish an automatic recognition system for tongue tumors at different stages of development using artificial intelligence methods along with pathological tissue section images. By improving Swin Transformer framework, a new framework in deep learning, the tissue slice image is used to identify the lesion and non‐lesion areas by the patch‐based method, and then an output is reconstructed by self‐Assembly method that the lesion areas is marked with heat map. Subsequently, an automatic recognition system with a friendly page is designed for the stage of tongue tumor development (malignant, benign, and leukoplakia). The proposed model has a high recognition accuracy (98.45%). The prediction accuracy of each category of the system is higher than that of specialist doctors with 13 years of experience. The Swin Transformer framework was improved in this study to accurately and automatically identify the various stages of tongue tumor development. [ABSTRACT FROM AUTHOR]

Fibrillins are microfibril-associated macro glycoproteins found in connective tissues and structurally related to latent TGF-β-binding proteins (LTBPs). The special cellular immunity and blocking glycoprotein receptors IIb and IIIa of fibrillins are emerging topics in recent years. In this study, Nile Tilapia type IIcollagen (NTCII) was extracted and purified from the skull cartilages by a pepsin-soluble method. Amino acid analysis indicated that NTCII consisted of 315/1000 glycine residues, 72/1000 hydroxyproline residues and 108/1000 proline residues. SDS-PAGE analysis showed that NTCII was composed of three identical 130 kDa α-chains. The results of glycoprotein/carbohydrate assay indicated that the total polysaccharide content of NTCII was 5.6–19.0%. The IR spectrum of NTCII displayed five characteristic peaks of amide I, II, III, A, B. NTCII at 10–100 μg/mL concentration downregulated the content of cytokines in the presence or absence of LPS, especially the secretion of cytokines IL-6, IL-1β and TNF-α. Interestingly, NTCII promoted the secretion of Fas/Apo-1 compared to the control group and 25 μg/mL of NTCII resulted in a higher Fas/Apo-1 secretion level in CD8+ T cells. FITC-TCII fluorescence images confirmed that NTCII could bind to the membrane surface of CD8+ T cells, leading to the induction of rigidity. NTCII could bind to the membrane surface of CD8+ T cells that leads to the induction of rigidity, as evidenced by the FITC-NTCII fluorescence images. The qRT-PCR gene expression analysis of caspase-8 collected with Fas/Apo-1 was upregulated significantly in the 1 and 50 μg/mL NTCII-treated groups compared with the control group. Overall, the results conclude that the rigidity did not lead to an increase in inflammatory factors in CD8+ T cells treated with NTCII. The oral administration of NTCII 3 mg/kg dosage caused more prominent repair of damaged ankle cartilage than the 1 mg/kg dosage in Freund's adjuvant-induced model of arthritis in rats. Therefore, this study disclosed the immunological and anti-arthritic effect of fibrillar collagen, which could be a potential biomaterial for practical applications with lower toxicity. [ABSTRACT FROM AUTHOR]