EXCLI Journal最新文献

Chronic atrophic gastritis (CAG) is considered as a significant risk factor for triggering gastric cancer incidence, if not effectively treated. Sijunzi decoction (SD) is a well-known classic formula for treating gastric disorders, and Sijunzi-similar formulae (SF) derived from SD have also been highly regarded by Chinese clinical practitioners for their effectiveness in treating chronic atrophic gastritis. Currently, there is a lack of meta-analysis for these formulae, leaving unclear which exhibits optimal efficacy. Therefore, we employed Bayesian network meta-analysis (BNMA) to evaluate the efficacy and safety of SF as an intervention for CAG and to establish a scientific foundation for the clinical utilization of SF. The result of meta-analysis demonstrated that the combination of SF and basic therapy outperformed basic therapy alone in terms of clinical efficacy rate, eradication rate of H. pylori, and incidence of adverse events. As indicated by the SUCRA value, Chaishao Liujunzi decoction (CLD) demonstrated superior efficacy in enhancing clinical effectiveness and ameliorating H. pylori infection, and it also showed remarkable effectiveness in minimizing the occurrence of adverse events. Comprehensive analysis of therapeutic efficacy suggests that CLD is most likely the optimal choice among these six formulations, holding potential value for optimizing clinical treatment strategies. See also the graphical abstract(Fig. 1).

Oral cancer retains one of the lowest survival rates worldwide, despite recent therapeutic advancements signifying a tenacious challenge in healthcare. Artificial intelligence exhibits noteworthy potential in escalating diagnostic and treatment procedures, offering promising advancements in healthcare. This review entails the traditional imaging techniques for the oral cancer treatment. The role of artificial intelligence in prognosis of oral cancer including predictive modeling, identification of prognostic factors and risk stratification also discussed significantly in this review. The review also encompasses the utilization of artificial intelligence such as automated image analysis, computer-aided detection and diagnosis integration of machine learning algorithms for oral cancer diagnosis and treatment. The customizing treatment approaches for oral cancer through artificial intelligence based personalized medicine is also part of this review. See also the graphical abstract(Fig. 1).

The shortage of treatment options for leishmaniasis, especially those easy to administer and viable for deployment in the world's poorest regions, highlights the importance of employing these strategies to cost-effectively investigate repurposing candidates. This scoping review aims to map the studies using in silico methodologies for drug repurposing against leishmaniasis. This study followed JBI recommendations for scoping reviews. Articles were searched on PubMed, Scopus, and Web of Science databases using keywords related to leishmaniasis and in silico methods for drug discovery, without publication date restrictions. The selection was based on primary studies involving computational methods for antileishmanial drug repurposing. Information about methodologies, obtained data, and outcomes were extracted. After the full-text appraisal, 34 studies were included in this review. Molecular docking was the preferred method for evaluating repurposing candidates (n=25). Studies reported 154 unique ligands and 72 different targets, sterol 14-alpha demethylase and trypanothione reductase being the most frequently reported. In silico screening was able to correctly pinpoint some known active pharmaceutical classes and propose previously untested drugs. Fifteen drugs investigated in silico exhibited low micromolar inhibition (IC₅₀ < 10 µM) of Leishmania spp. in vitro. In conclusion, several in silico repurposing candidates are yet to be investigated in vitro and in vivo. Future research could expand the number of targets screened and employ advanced methods to optimize drug selection, offering new starting points for treatment development. See also the graphical abstract(Fig. 1).

Cartilage tissue, characterized by its limited regenerative capacity, presents significant challenges in clinical therapy. Recent advancements in cartilage regeneration have focused on integrating stem cell therapies, tissue engineering strategies, and advanced modeling techniques to overcome existing limitations. Stem cells, particularly Mesenchymal Stem Cells (MSCs) and induced pluripotent stem cells (iPSCs), hold promise for cartilage repair due to their ability to differentiate into chondrocytes, the key cells responsible for cartilage formation. Tissue engineering approaches, including 3D models, organ-on-a-chip systems, and organoids, offer innovative methods to mimic natural tissue microenvironments and evaluate potential treatments. MSC-based techniques, such as cell sheet tissue engineering, address challenges associated with traditional therapies, including cell availability and culture difficulties. Furthermore, advancements in 3D bioprinting enable the fabrication of complex tissue structures, while organ-on-a-chip systems provide microfluidic platforms for disease modeling and physiological mimicry. Organoids serve as simplified models of organs, capturing some complexity and enabling the monitoring of pathophysiological aspects of cartilage diseases. This comprehensive review underscores the transformative potential of integrating stem cell therapies, tissue engineering strategies, and advanced modeling techniques to improve cartilage regeneration and pave the way for more effective clinical treatments.

Atherosclerotic cardiovascular diseases are the leading causes of morbidity and mortality worldwide. In our previous study, a panel of miRNA including miR-134-5p was deregulated in young acute coronary syndrome (ACS) patients. However, the roles of these ACS-associated miRNAs in endothelial dysfunction, an early event preceding atherosclerosis, remain to be investigated. In the present study, human aortic endothelial cells (HAECs) were treated with 7-ketocholesterol (7-KC) to induce endothelial dysfunction. Following treatment with 20 μg/ml 7-KC, miR-134-5p was significantly up-regulated and endothelial nitric oxide synthase (eNOS) expression was suppressed. Endothelial barrier disruption was evidenced by the deregulation of adhesion molecules including the activation of focal adhesion kinase (FAK), down-regulation of VE-cadherin, up-regulation of adhesion molecules (E-selectin and ICAM-1), increased expression of inflammatory genes (IL1B, IL6 and COX2) and AKT activation. Knockdown of miR-134-5p in 7-KC-treated HAECs attenuated the suppression of eNOS, the activation of AKT, the down-regulation of VE-cadherin and the up-regulation of E-selectin. In addition, the interaction between miR-134-5p and FOXM1 mRNA was confirmed by the enrichment of FOXM1 transcripts in the pull-down miRNA-mRNA complex. Knockdown of miR-134-5p increased FOXM1 expression whereas transfection with mimic miR-134-5p decreased FOXM1 protein expression. In summary, the involvement of an ACS-associated miRNA, miR-134-5p in endothelial dysfunction was demonstrated. Findings from this study could pave future investigations into utilizing miRNAs as a supplementary tool in ACS diagnosis or as targets for the development of therapeutics.