Background: Radiation-induced brain injury (RIBI) is the most serious complication of radiotherapy in patients with head and neck tumors, which seriously affects the quality of life. Currently, there is no effective treatment for patients with RIBI, and identifying new treatment that targets the pathological mechanisms of RIBI is urgently needed. Methods: Immunofluorescence staining, western blotting, quantitative real-time polymerase chain reaction (Q-PCR), co-culture of primary neurons and microglia, terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay, enzyme-linked immunosorbent assay (ELISA), and CRISPR–Cas9-mediated gene editing techniques were employed to investigate the protective effects and underlying mechanisms of pregabalin that ameliorate microglial activation and neuronal injury in the RIBI mouse model. Results: Our findings showed that pregabalin effectively repressed microglial activation, thereby reducing neuronal damage in the RIBI mouse model. Pregabalin mitigated inflammatory responses by directly inhibiting cytoplasmic translocation of high-mobility group box 1 (HMGB1), a pivotal protein released by irradiated neurons which induced subsequent activation of microglia and inflammatory cytokine expression. Knocking out neuronal HMGB1 or microglial TLR2/TLR4/RAGE by CRISPR/Cas9 technique significantly inhibited radiation-induced NF-κB activation and pro-inflammatory transition of microglia. Conclusions: Our findings indicate the protective mechanism of pregabalin in mitigating microglial activation and neuronal injury in RIBI. It also provides a therapeutic strategy by targeting HMGB1-TLR2/TLR4/RAGE signaling pathway in the microglia for the treatment of RIBI.

Background: Malnutrition’s prognostic impact in patients with severe stroke requiring ICU admission is not well known. This study aimed to assess the nutritional status of severe stroke patients using the geriatric nutritional risk index (GNRI) and examine the association of GNRI with mortality in that population. Methods: We identified 1145 severe stroke patients requiring ICU admission from the Medical Information Mart for Intensive Care (MIMIC-III) database and divided them into low GNRI (≤98) or high GNRI (>98) groups. We used a propensity score matching (PSM) method to reduce confounding. Cox proportional hazards regression and restricted cubic splines were used to elucidate the association between GNRI and mortality. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated. Results: A total of 397 (35%) patients were in the low GNRI group (at risk of malnutrition). After PSM, patients in the low GNRI group still suffered higher mortality compared with the high GNRI group at 28 days (27.9 vs. 20.8%), 90 days (35.5 vs. 25.7%), and 1 year (43.4 vs. 30.9%) (p < 0.05). A low GNRI was significantly associated with an increased mortality (HR: 1.38, 95% CI 1.03–1.86 in 28 days; HR: 1.45, 95% CI 1.11–1.89 in 90 days; HR: 1.51, 95% CI 1.19–1.92 in 1 year). Sensitivity analyses yielded consistent results. Restricted cubic splines showed a progressively decreasing risk of mortality with increasing GNRI scores up to 110, approximately. Conclusion: Severe stroke patients with malnutrition experienced an increased risk of death compared to those without malnutrition. GNRI, as a simple and practical nutritional screening tool, can be used as a routine approach to the nutritional status of stroke patients.

Malnutrition is related to worsened prognosis, but the association between nutritional risk status and overall survival in radiation-induced brain necrosis (RN) has never been studied. We included consecutive patients who had received radiotherapy for head and neck cancer (HNC) and subsequently developed RN from 8 January 2005 through to 19 January 2020. The primary outcome was overall survival. We utilized three commonly-used nutritional assessments: the Geriatric Nutritional Risk Index (GNRI), Prognostic Nutritional Index (PNI), and the COntrolling NUTritional Status (CONUT) measure, to quantify the baseline nutritional risk. A total of 398 eligible patients were included. During a median follow-up of 2.3 years, 42 (10.6%) patients died of any cause. Malnutrition at admission was associated with an increased risk of future death, as assessed by the GNRI (per 1-point decreased, HR 1.05, 95%CI 1.02–1.09, p = 0.001), the PNI (per 1-point decreased, HR 1.07, 95%CI 1.03–1.12, p = 0.002), and the CONUT (per 1-point increased, HR 1.22, 95%CI 1.08–1.37, p = 0.001). There were no nonlinear correlations between all three indices and post-RN survival. Among HNC survivors with RN, the assessment of nutritional risk by composite indices upon admission could help identify patients who might be at high risk of future death and deliver better nutritional management.

Background: Radiation-induced cystic brain necrosis (RCN) can progress rapidly to life-threatening cerebral herniation. This study aimed to develop a predictive model integrating radiomic features and clinical variables to assess the risk of cerebral herniation in RCN patients. Methods: A total of 130 patients diagnosed with RCN following radiotherapy for nasopharyngeal carcinoma were retrospectively enrolled and randomly assigned to training (n = 91) and testing (n = 39) cohorts in a 7:3 ratio. Radiomic features were extracted from baseline T2-weighted magnetic resonance imaging (MRI), and a radiomic signature was constructed using least absolute shrinkage and selection operator regression. A multivariate Cox regression model was then developed by incorporating the radiomic signature and clinical variables to predict cerebral herniation. The model’s discriminative ability, calibration, and clinical utility were evaluated. Results: The radiomic signature based on five selected radiomic features demonstrated good predictive performance. The radiomic model, which integrated the radiomic signature and ratios of perilesional enhancement, exhibited favorable performance in both the training cohort (C-index: 0.841) and testing cohort (C-index: 0.867). The model successfully stratified patients into high- and low-risk groups. The calibration curves showed good agreement and the decision curve confirmed the clinical utility of the model. Conclusions: The MRI-based radiomic model, which integrates radiomic features and clinical variables, demonstrates robust performance in predicting cerebral herniation in RCN patients, offering a practical and user-friendly tool to support clinical decision-making.