Ischemia-reperfusion (I/R) injury following skin flap transplantation is a critical factor leading to flap necrosis and transplant failure. Antagonizing inflammatory responses and oxidative stress are regarded as crucial targets for mitigating reperfusion injury and enhancing flap survival. In this study, caffeic acid-vanadium metal polyphenol nanoparticles (CA-V NPs) were prepared for the treatment of skin flap ischemia and reperfusion. This study was conducted using a one-step method to prepare new types of CA-V NPs with uniform sizes and stable structures. In vitro, the CA-V NPs exhibited CAT-like and SOD-like activities and could effectively scavenge ROS, generate oxygen, and alleviate oxidative stress. In the H₂O₂-induced cellular oxidative stress model, CA-V NPs effectively reduced ROS levels and inhibited apoptosis through the XIAP/Caspase-3 pathway. In the cellular inflammation model induced by LPS combined with IFN-γ, CA-V NPs reprogrammed macrophage polarization toward the M2 phenotype and reduced inflammatory responses by reducing the expression of the chemokines CCL4 and CXCL2. In addition, animal experiments have shown that CA-V NPs can alleviate oxidative stress in skin flap tissues, inhibit apoptosis, promote angiogenesis, and ultimately improve the survival rate of skin flaps. CA-V NPs provide a new target and strategy for the treatment of flap I/R injury.

Objective : To evaluate the prognostic value of a radiomics model based on the peritumoral region of gli- oma． Methods : 138 patients with glioma were retrospectively analyzed ，medical imaging interaction toolkit ( MITK) software was used to obtain the magnetic resonance imaging (MRI) images of peritumoral area 5 mm，10 mm and 20 mm from the tumor edge and extract texture features．The texture features were screened the radiomics model was established and the radiomic score was calculated．A clinical prediction model and a combined predic- tion model along with Rad-score and clinical risk factors were established．The combined prediction model was dis- played as a nomogram，and the predictive performance of the model for survival in glioma patients was evaluated. Results : In the validation set，the C-index value of the radiomics model based on the peritumoral region 10 mm a- way from the tumor edge based on T2 weighted image (T2WI) images was 0. 663 (95% CI = 0. 72－0. 78) ，resul- ting in the best prediction performance．On the training set and validation set，the C-index of the nomogram was 0. 770 and 0. 730，respectively，indicating that the prediction performance of nomogram was better than those of the radiomics model and clinical prediction model．The model had the highest prediction effect on the 3-year survival rate of glioma patients (training set area under curve (AUC) = 0. 93，95% CI = 0. 83 － 0. 98 ; validation set AUC = 0. 88，95% CI = 0. 76 －0. 99) ．The calibration curve showed that the joint prediction nomogram in both the training set and the validation set had good performance． Conclusion The combined prediction model based on the preoperative T2WI images in the peritumoral region 10 mm from the tumor edge and the clinicopathological risk factors can accurately predict the prognosis of glioma，providing the best effect of prediction on the 3-year survival rate of glioma.