The impact of Astragaloside IV on the inflammatory response and gut microbiota in cases of acute lung injury is examined through the utilization of the PI3K/AKT/mTOR pathway.

OBJECTIVES: Astragaloside IV (AS-IV) is a natural triterpenoid saponin compound with a variety of pharmacological effects, and several studies have clarified its anti-inflammatory effects, which may make it an effective alternative treatment against inflammation. In the study, we aimed to investigate whether AS-IV could attenuate the inflammatory response to acute lung injury and its mechanisms. METHODS: Different doses of AS-IV (20mg·kg-1, 40mg·kg-1, and 80mg·kg-1) were administered to the ALI rat model, followed by collection of serum and broncho alveolar lavage fluid (BALF) for examination of the inflammatory response, and HE staining of the lung and colon tissues, and interpretation of the potential molecular mechanisms by quantitative real-time PCR (qRT-PCR), Western blotting (WB). In addition, fecal samples from ALI rats were collected and analyzed by 16S rRNA sequencing. RESULTS: AS-IV decreased the levels of TNF-α, IL-6, and IL-1ß in serum and BALF of mice with Acute lung injury (ALI). Lung and colon histopathology confirmed that AS-IV alleviated inflammatory infiltration, tissue edema, and structural changes. qRT-PCR and WB showed that AS-IV mainly improved inflammation by inhibiting the expression of PI3K, AKT and mTOR mRNA, and improved the disorder of intestinal microflora by increasing the number of beneficial bacteria and reducing the number of harmful bacteria. CONCLUSION: AS-IV reduces the expression of inflammatory factors by inhibiting the PI3K/AKT/mTOR pathway and optimizes the composition of the gut microflora in AIL rats.

Prediction of the benign and malignant nature of masses in COPD background based on Habitat-based enhanced CT radiomics modeling: A preliminary study.

BACKGROUND: It is difficult to differentiate between chronic obstructive pulmonary disease (COPD)-peripheral bronchogenic carcinoma (COPD-PBC) and inflammatory masses. OBJECTIVE: This study aims to predict COPD-PBC based on clinical data and preoperative Habitat-based enhanced CT radiomics (HECT radiomics) modeling. METHODS: A retrospective analysis was conducted on clinical imaging data of 232 cases of postoperative pathological confirmed PBC or inflammatory masses. The PBC group consisted of 82 cases, while the non-PBC group consisted of 150 cases. A training set and a testing set were established using a 7:3 ratio and a time cutoff point. In the training set, multiple models were established using clinical data and radiomics texture changes within different enhanced areas of the CT mass (HECT radiomics). The AUC values of each model were compared using Delong's test, and the clinical net benefit of the models was tested using decision curve analysis (DCA). The models were then externally validated in the testing set, and a nomogram of predicting COPD-PBC was created. RESULTS: Univariate analysis confirmed that female gender, tumor morphology, CEA, Cyfra21-1, CT enhancement pattern, and Habitat-Radscore B/C were predictive factors for COPD-PBC (P< 0.05). The combination model based on these factors had significantly higher predictive performance [AUC: 0.894, 95% CI (0.836-0.936)] than the clinical data model [AUC: 0.758, 95% CI (0.685-0.822)] and radiomics model [AUC: 0.828, 95% CI (0.761-0.882)]. DCA also confirmed the higher clinical net benefit of the combination model, which was validated in the testing set. The nomogram developed based on the combination model helped predict COPD-PBC. CONCLUSION: The combination model based on clinical data and Habitat-based enhanced CT radiomics can help differentiate COPD-PBC, providing a new non-invasive and efficient method for its diagnosis, treatment, and clinical decision-making.