Ginsenoside Rg3 promotes hepatic stellate cell ferroptosis by epigenetically regulating ACSL4 to suppress liver fibrosis progression.

BACKGROUND: Ginsenoside Rg3 (G-Rg3), extracted from Panax notoginseng, possesses hepatoprotective properties. Hepatic stellate cells (HSCs) activation is responsible for liver fibrosis. Recent studies have reported the suppressive effects of G-Rg3 on HSC activation and proliferation. Ferroptosis is a novel iron regulated cell death. ACSL4, a key indicator of ferroptosis, is commonly methylated in various diseases. PURPOSE: However, the role of ACSL4 methylation-mediated HSC ferroptosis in G-Rg3 inhibition of hepatic fibrosis needs to be explored. METHODS: Effects of G-Rg3 on inhibiting fibrosis were evaluated in vivo and in vitro. The impact of G-Rg3 on HSC ferroptosis was assessed in vitro. Furthermore, the expression of ACSL4, ACSL4 methylation and microRNA-6945-3p (miR-6945-3p) levels were determined. RESULTS: G-Rg3 significantly alleviated CCl4-induced liver fibrosis, accompanied by collagen downregulation. In vitro, G-Rg3 contributed to HSC inactivation, leading to decreased collagen production. G-Rg3 induced HSC ferroptosis, characterized by increased iron accumulation, depletion of glutathione, malondialdehyde levels, and generation of lipid reactive oxygen species. Moreover, G-Rg3 promoted ACSL4 demethylation and restored its expression. Notably, DNMT3B counteracted the effect of G-Rg3-mediated inhibition of ACSL4 methylation and was targeted by miR-6945-3p. Further investigations revealed that G-Rg3 suppressed ACSL4 methylation through miR-6945-3p-mediated DNMT3B inhibition. Consistent with this, miR-6945-3p inhibition reversed G-Rg3-induced ACSL4 expression and HSC ferroptosis. CONCLUSION: G-Rg3 inhibits ACSL4 methylation by miR-6945-3p-mediated DNMT3B inhibition, thereby promoting HSC ferroptosis and mitigating liver fibrosis.

Diagnostic Accuracy of CT-Guided Percutaneous Pulmonary Biopsy for Distinguishing Benign and Malignant Solitary Pulmonary Nodules.

Background: Isolated pulmonary nodules (SPNs) are small, circular lesions within lung tissue, often challenging to diagnose due to their size and lack of typical imaging features. Timely diagnosis is crucial for treatment decisions. However, the difficulty in qualitative diagnosis necessitates clinical biopsies. Objective: This study aimed to assess the diagnostic accuracy of CT-guided percutaneous lung biopsy for SPNs and identify potential risk factors for malignancy. Methods: We conducted a retrospective analysis of 112 patients with SPNs who underwent CT-guided core needle biopsy (CT-CNB) between June 2020 and June 2022. Histological and cytological results were obtained for all patients, and clinical data and imaging characteristics were compared between benign and malignant SPN groups. Binary logistic regression was used to analyze risk factors for malignancy, and complications were observed. Results: Cytological and histological specimens were successfully obtained for all patients. The cohort consisted of 43 patients with benign SPNs and 69 with malignant SPNs. Among the malignant SPN group, 67 cases were confirmed via CT-CNB and 2 through surgery, resulting in a sensitivity of 97.10% and specificity of 100.00%. The malignant nodules comprised 45 adenocarcinomas, 14 squamous cell carcinomas, 8 metastatic tumors, and 2 small cell carcinomas. Notably, 2 initially diagnosed as malignant cases were found to have chronic inflammation on preoperative biopsy but revealed adenocarcinoma and squamous cell carcinoma post-surgery. The benign nodules encompassed 20 granulomatous inflammation cases, 15 chronic inflammation, 3 fungal granulomas, 2 hamartomas, and 1 fibrous tissue. Cytological smears exhibited a sensitivity of 81.3% and a specificity of 100.0% for malignancy. Significantly, age ≥60, elevated tumor markers, and specific imaging signs (burr, foliation, pleural pull) were identified as risk factors for malignant SPNs using Binary Logistic regression (all P < .05). Conclusions: CT-guided percutaneous lung biopsy demonstrates excellent diagnostic efficacy and safety for distinguishing benign and malignant SPNs.

Disease- and Drug-Related Knowledge Extraction for Health Management from Online Health Communities Based on BERT-BiGRU-ATT.

Knowledge extraction from rich text in online health communities can supplement and improve the existing knowledge base, supporting evidence-based medicine and clinical decision making. The extracted time series health management data of users can help users with similar conditions when managing their health. By annotating four relationships, this study constructed a deep learning model, BERT-BiGRU-ATT, to extract disease-medication relationships. A Chinese-pretrained BERT model was used to generate word embeddings for the question-and-answer data from online health communities in China. In addition, the bidirectional gated recurrent unit, combined with an attention mechanism, was employed to capture sequence context features and then to classify text related to diseases and drugs using a softmax classifier and to obtain the time series data provided by users. By using various word embedding training experiments and comparisons with classical models, the superiority of our model in relation to extraction was verified. Based on the knowledge extraction, the evolution of a user's disease progression was analyzed according to the time series data provided by users to further analyze the evolution of the user's disease progression. BERT word embedding, GRU, and attention mechanisms in our research play major roles in knowledge extraction. The knowledge extraction results obtained are expected to supplement and improve the existing knowledge base, assist doctors' diagnosis, and help users with dynamic lifecycle health management, such as user disease treatment management. In future studies, a co-reference resolution can be introduced to further improve the effect of extracting the relationships among diseases, drugs, and drug effects.