Background: Radiation‐induced lung injury (RILI) is a common consequence of thoracic radiation therapy that lacks effective preventative and treatment strategies. Dihydroartemisinin (DHA), a derivative of artemisinin, affects oxidative stress, immunomodulation, and inflammation. It is uncertain whether DHA reduces RILI. In this work, we investigated the specific mechanisms of action of DHA in RILI.Methods: Twenty‐four C57BL/6J mice were randomly divided into four groups of six mice each: Control group, irradiation (IR) group, IR + DHA group, and IR + DHA + Brusatol group. The IR group received no interventions along with radiation treatment. Mice were killed 30 days after the irradiation. Morphologic and pathologic changes in lung tissue were observed with hematoxylin and eosin staining. Detection of hydroxyproline levels for assessing the extent of pulmonary fibrosis. Tumor necrosis factor α (TNF‐α), transforming growth factor‐β (TGF‐β), glutathione peroxidase (GPX4), Nuclear factor erythroid 2‐related factor 2 (Nrf2), and heme oxygenase‐1 (HO‐1) expression in lung tissues were detected. In addition, mitochondrial ultrastructural changes in lung tissues were also observed, and the glutathione (GSH) content in lung tissues was assessed.Results: DHA attenuated radiation‐induced pathological lung injury and hydroxyproline levels. Additionally, it decreased TNF‐α and TGF‐β after irradiation. DHA may additionally stimulate the Nrf2/HO‐1 pathway. DHA upregulated GPX4 and GSH levels and inhibited cellular ferroptosis. Brusatol reversed the inhibitory effect of DHA on ferroptosis and its protective effect on RILI.Conclusion: DHA modulated the Nrf2/HO‐1 pathway to prevent cellular ferroptosis, which reduced RILI. Therefore, DHA could be a potential drug for the treatment of RILI.

Purpose: To determine the effect of X-ray irradiation combined with PD-1 immune checkpoint inhibitor administration on lung tissue injury in a mouse model and its potential mechanism. Methods: In all, 20 C57BL/6J mice were randomly divided into four groups with five mice in each group: control group, PD-1 inhibitor group, irradiation group, and irradiation combined with PD-1 inhibitor group. Hematoxylin-eosin staining of the lung tissue was performed 30 days after the end of irradiation to evaluate the morphological and pathological changes in the tissue. Masson staining and analysis of hydroxyproline were used to evaluate the degree of pulmonary fibrosis. The levels of transforming growth factor-β1 (TGF-β1) and tumor necrosis factor α(TNF-α) were evaluated by Enzyme-Linked immunosorbent assay (ELISA). CD3+, CD4+, and CD8+ T lymphocytes in the lung tissue were detected by immunohistochemistry. The expression levels of TGF-β1, Smad3, cGAS, and STING in the lung tissue were evaluated by Western blotting. Results: The lung injury scores and pulmonary fibrosis indices in the irradiation group were higher than those in the control group. Meanwhile, lung pneumonia score, pulmonary fibrosis index, percentage of CD4 cells and expression of TGF-β1, p-Smad3, and STING in the lung tissue of mice in irradiation combined with PD-1 inhibitor group were higher than those in the other three groups. Conclusion: Lung injury and pulmonary fibrosis were induced by whole chest X-ray irradiation in mice, and PD-1 inhibitor could aggravate lung injury and pulmonary fibrosis in mice. Thus, radiotherapy combined with PD-1 inhibitors may affect the immune inflammatory microenvironment in the lung tissues of mice by activating TGF-β1/Samd3 and cGAS/STING signaling pathways, thus aggravating lung tissue damage induced by radiation.

Dihydroartemisinin (DHA) is a derivative of artemisinin, which affects inflammation, oxidative stress, and immune regulation. However, the mechanism underlying its effects remains largely unknown. This study aims to explore the mechanism by which DHA affects radiation‐induced lung injury (RILI), providing new insights for lung radiotherapy. To elucidate its mechanism of action, C57BL/6 J mice were irradiated with 15 Gy whole chest. RILI was evaluated by qRT‐PCR, ELISA, histology, Western blot analysis, immunohistochemistry, and RILI signaling cascade studies. In addition, small interfering RNAs were employed to knockdown cGAS proteins in the cGAS‐STING signaling pathway in the human bronchial epithelium cell line (BEAS‐2B). Both In Vivo and Vitro experiments were conducted to investigate the specific mechanism by which DHA alleviated RILI. We observed the activation of the cGAS‐STING pathway, along with the phosphorylation of the downstream target NF‐κB and an increase in inflammatory factor levels in the mouse model following radiation exposure. In the cell model, irradiation also triggered the activation of the cGAS‐STING signaling pathway and its downstream targets, leading to elevated levels of inflammatory factors. Notably, knocking down the cGAS using small interfering RNA in the BEAS‐2B cells significantly alleviated RILI in the cell model. Our study elucidated the mechanism of DHA reducing RILI through the cGAS/STING/NF‐κB signaling pathway, and revealed that the GAS/STING/NF‐κB axis may be a potential therapeutic target for RILI.

Objective: To develop an interpretable machine learning (ML) model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic data, dosimetric parameters, and clinical data for predicting radiation-induced hepatic toxicity (RIHT) in patients with hepatocellular carcinoma (HCC) following intensity-modulated radiation therapy (IMRT). Methods: A retrospective analysis of 150 HCC patients was performed, with a 7:3 ratio used to divide the data into training and validation cohorts. Radiomic features from the original MRI sequences and Delta-radiomic features were extracted. Seven ML models based on radiomics were developed: logistic regression (LR), random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), adaptive boosting (AdaBoost), decision tree (DT), and artificial neural network (ANN). The predictive performance of the models was evaluated using receiver operating characteristic (ROC) curve analysis and calibration curves. Shapley additive explanations (SHAP) were employed to interpret the contribution of each variable and its risk threshold. Results: Original radiomic features and Delta-radiomic features were extracted from DCE-MRI images and filtered to generate Radiomics-scores and Delta-Radiomics-scores. These were then combined with independent risk factors (Body Mass Index (BMI), V5, and pre-Child-Pugh score(pre-CP)) identified through univariate and multivariate logistic regression and Spearman correlation analysis to construct the ML models. In the training cohort, the AUC values were 0.8651 for LR, 0.7004 for RF, 0.6349 for SVM, 0.6706 for XGBoost, 0.7341 for AdaBoost, 0.6806 for Decision Tree, and 0.6786 for ANN. The corresponding accuracies were 84.4%, 65.6%, 75.0%, 65.6%, 71.9%, 68.8%, and 71.9%, respectively. The validation cohort further confirmed the superiority of the LR model, which was selected as the optimal model. SHAP analysis revealed that Delta-radiomics made a substantial positive contribution to the model. Conclusion: The interpretable ML model based on radiomics provides a non-invasive tool for predicting RIHT in patients with HCC, demonstrating satisfactory discriminative performance.

The integration of immunotherapy with neoadjuvant therapy for proficient mismatch repair (pMMR) locally advanced rectal cancer (LARC) is a promising strate