Objective To evaluate the radioprotective effects of metformin on radiation-induced pulmonary injury in rats. Method A total of 30 Sprague-Dawley ( SD) rats were randomly divided into three groups:control, radiation (20 Gy) and radiation (20 Gy) with metformin, with 10 rats in each group. The right lungs of rats were irradiated to 20 Gy with 6 MV X-rays. Computed tomography (CT) was performed and Hounsfield Units ( HU) were determined during the observation period. The tissue samples of lung were extracted to perform the histological analysis, measurement of hydroxyproline content, fibrosis score and evaluation of fibrosis/inflammatory markers by Western blot at 12 weeks post-irradiation. CCK-8 method was used to explore the effects of metformin on non-small cell lung cancer( NSCLC) cells A549 and H460. Results Metformin reduced radiologic and histologic signs of fibrosis, lung density(6. 52 ± 0. 43 vs. 3. 31 ± 0. 57, t=6. 37, P<0. 01) and hydroxyproline content(32. 58 ± 1. 59 vs. 23. 47 ± 2. 46,t=12. 72, P<0. 01) which had been increased due to irradiation. Meanwhile, it significantly decreased the expressions of Col1, p-AMPKα and TGF-β, while inhibited the expressions of p-Smad2 and p-Smad3 compared to the irradiation alone group. Moreover, metformin reduced A549 and H460 cells growth. Conclusions Metformin exerted a protective effect on normal tissues in radiation-induced pulmonary fibrosis. Thus, it might act as a promising radioprotective agent in the treatment of lung cancer.

Objective:To investigate the effects of different small monitor unit (MU) beam deletion optimization method in the CyberKnife treatment planning system on the calculated planned dose to brain tumors.Methods:A total of 17 patients with brain metastases treated in our hospital from June, 2021 to February, 2022 were selected for this study. A treatment plan was designed for each patient using the multiPlan system in the CyberKnife VSI system as the group without optimization. To improve the efficiency, the generated original plans should be optimized first by deleting some small MUs, forming an experience group and an optimization group for each patient. For the experience group, beams below 30 MU were deleted according to experience. For the optimization group, beams below the MU value calculated based on the second derivative method were deleted. Finally, the parameters of the two groups were statistically compared. The main evaluation parameters included the node number, the beam number, the total number of MUs, the estimated treatment duration, doses to 2% and 95% planning target volumes (PTV D2 and PTV D95), average dose to PTV ( Dmean), average dose to brain tissue ( Dmean-Brain), conformity index (CI), new conformity index (nCI), gradient index (GI), coverage, and the maximum doses to the brainstem and left and right lens ( Dmax-BS, Dmax-LL, and Dmax-RL), and the average doses to the dose shells 20 mm and 40 mm away from PTV (Shell20 and Shell40). Results:The two optimization method met the requirements for the prescription dose delivery to more than 98% PTV. There were statistical differences in the node number ( H = 7.97, P< 0.05) and estimated treatment duration ( H = 6.60, P < 0.05) among the group without MP optimization, the experience group, and the optimization group, with the estimated treatment duration and node number of the optimization group less than those of the group without MP optimization ( P < 0.05). There were no statistically significant differences in other parameters among the three groups ( P > 0.05). The PTV was moderately positively correlated with the treatment duration ( r=0.79, P < 0.01) and beam number ( r=0.78, P < 0.01) of the experience group, and was also moderately positively correlated with the treatment duration ( r=0.69, P < 0.01) and beam number ( r=0.71, P < 0.01) of the optimization group. Conclusions:For the CyberKnife planning of heads, the small MU beam deletion optimization method based on the second derivative can further shorten the treatment duration while ensuring no significant differences in the distribution of doses to organs at risk and targets. Moreover, this method is more effective in optimizing the plans for a large PTV volume.