Although post-ischemic inflammation induced by the innate immune response is considered an essential step in the progression of cerebral ischemia injury, the role of triggering receptor expressed on myeloid cells 2 (TREM2) in the pathogenesis of ischemic stroke remains to be elucidated. Here, we found that the transcriptional and post-transcriptional levels of TREM2 were increased in cultured primary microglia after oxygen-glucose deprivation and reoxygenation and in the ischemic penumbra of the cerebral cortex after middle cerebral artery occlusion (MCAO) and reperfusion in mice. TREM2 was mainly expressed in microglia, but not in astrocytes, neurons, or oligodendrocytes in mice subjected to MCAO. Manipulating TREM2 expression levels in vitro and in vivo significantly regulated the production of pro-and anti-inflammatory mediators after ischemic stroke. TREM2 overexpression markedly suppressed the inflammatory response and neuronal apoptosis. By contrast, TREM2 gene silencing intensified the inflammatory response, increased neuronal apoptosis and infarct volume, and further exacerbated neurological dysfunction. Our study demonstrated that TREM2 protects against cerebral ischemia/ reperfusion injury through the aspect of post-ischemic inflammatory response and neuronal apoptosis. Pharmacological targeting of TREM2 to suppress the inflammatory response may provide a new approach for developing therapeutic strategies in the treatment of ischemic stroke and other cerebrovascular diseases.

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Purpose Neuropathic pain is an unavoidable treatment-related adverse event among patients with head and neck cancer who are undergoing radiotherapy. We aimed to test the efficacy and safety of pregabalin versus placebo in the treatment of radiotherapy-related neuropathic pain. Patients and Methods This randomized, double-blind, placebo-controlled trial was conducted in four centers in China. Eligible patients with a mean pain intensity score of 4 or more on an 11-point numeric rating scale were randomly assigned to receive either active treatment with a flexible dose of pregabalin or placebo for 16 weeks. The primary efficacy outcome was pain reduction measured on the numeric rating scale. Result There were 128 patients who received treatment as randomly assigned. Pain intensity reduction was 2.44 in the pregabalin arm and 1.58 in the placebo arm at week 16, yielding an adjusted mean difference of 0.87 (95% CI, 0.30 to 1.44; P = .003). In the pregabalin arm, 38 patients (59.4%) achieved at least 30% pain relief versus 21 (32.8%) in the placebo arm ( P = .006). Nineteen patients (29.7%) in the pregabalin group and five (7.8%) in the placebo group achieved 50% or greater pain relief ( P = .003). Total scores on the Profile of Mood States-Short Form, pain severity and functional interference of Brief Pain Inventory-Short Form, as well as the physiology and psychology domain of the WHO Quality of Life-BREF all were reduced significantly at week 16 in patients who received pregabalin compared with those who received placebo. There was no significant difference ( P = .29) in the incidence of experiencing at least one adverse event in the pregabalin arm (n = 35; 54.7%) versus the placebo arm (n = 29; 45.3%). Conclusion Patients treated with pregabalin with radiotherapy-related neuropathic pain had greater pain alleviation, better mood states, and higher quality of life compared with patients in the placebo group, with a good tolerability.

◥Purpose: Bevacizumab is considered a promising therapy for brain necrosis after radiotherapy, while some patients fail to derive benefit or even worsen. Hence, we developed and validated a radiomics model for predicting the response to bevacizumab in patients with brain necrosis after radiotherapy.Experimental Design: A total of 149 patients (with 194 brain lesions; 101, 51, and 42 in the training, internal, and external validation sets, respectively) receiving bevacizumab were enrolled. In total, 1,301 radiomic features were extracted from the pretreatment MRI images of each lesion. In the training set, a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm. Multivariable logistic regression analysis was then used to develop a radiomics model incorporated in the radiomics signature and independent clinical predictors. The performance of the model was assessed by its discrimination, calibration, and clinical usefulness with internal and external validation.Results: The radiomics signature consisted of 18 selected features and showed good discrimination performance. The model, which integrates the radiomics signature, the interval between radiotherapy and diagnosis of brain necrosis, and the interval between diagnosis of brain necrosis and treatment with bevacizumab, showed favorable calibration and discrimination in the training set (AUC 0.916). These findings were confirmed in the validation sets (AUC 0.912 and 0.827, respectively). Decision curve analysis confirmed the clinical utility of the model.Conclusions: The presented radiomics model, available as an online calculator, can serve as a user-friendly tool for individualized prediction of the response to bevacizumab in patients with brain necrosis after radiotherapy.