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Ischemic stroke with a mismatch between diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) or T2-weighted images indicates onset within 4.5 h, but the pathological substrates in the DWI-T2 mismatch and T2(+) areas remain elusive. In this study, proteomics was used to explore (1) the protein expression profiles in the T2(+), mismatch, and contralateral areas, and (2) the protein with the highest expression in the T2(+) area in the brains of male Sprague-Dawley rats within 4.5 h after middle cerebral artery occlusion (MCAO). The expression of the candidate protein was further validated in (1) rat brain subjected to MCAO, (2) rat primary cortical neuronal culture with oxygen-glucose deprivation (OGD), and (3) infarcted human brain tissues. This study showed that apoptosis was observed in the T2(+) and mismatch regions and necroptosis in the T2(+) region of rat brains after MCAO. We identified capping protein regulator and myosin 1 linker 3 (CARMIL3) as the candidate molecule in the T2(+) and mismatch areas, exclusively in neurons, predominantly in the cytoplasm, and most abundant in the mismatch area. The CARMIL3(+) neurons and neurites in the mismatch and T2(+) areas were larger than those in the control area, and associated with (1) increased expression of sulfonylurea receptor 1 (SUR1), indicating edema, (2) accumulation of p62, indicating impaired autophagy, and (3) increase in 8-hydroxy-2′-deoxyguanosine (8-OHdG), indicating oxidative stress. The increased expression of CARMIL3 was validated in a cell model of cortical neurons after OGD and in infarcted human brain tissues. In conclusion, this study shows that the mismatch and T2(+) areas within 4.5 h after ischemia are characterized by upregulated expression of CARMIL3 in neurons, particularly the mismatch area, which is associated with neuronal edema, impaired autophagy, and oxidative stress, indicating that CARMIL3 serves as a molecular signature of brain ischemia.

Background Experimental ischemic stroke models play a fundamental role in interpreting the mechanism of cerebral ischemia and appraising the development of pathological extent. An accurate and automatic skull stripping tool for rat brain image volumes with magnetic resonance imaging (MRI) are crucial in experimental stroke analysis. Due to the deficiency of reliable rat brain segmentation methods and motivated by the demand for preclinical studies, this paper develops a new skull stripping algorithm to extract the rat brain region in MR images after stroke, which is named Rat U-Net (RU-Net). Methods Based on a U-shape like deep learning architecture, the proposed framework integrates batch normalization with the residual network to achieve efficient end-to-end segmentation. A pooling index transmission mechanism between the encoder and decoder is exploited to reinforce the spatial correlation. Two different modalities of diffusion-weighted imaging (DWI) and T2-weighted MRI (T2WI) corresponding to two in-house datasets with each consisting of 55 subjects were employed to evaluate the performance of the proposed RU-Net. Results Extensive experiments indicated great segmentation accuracy across diversified rat brain MR images. It was suggested that our rat skull stripping network outperformed several state-of-the-art methods and achieved the highest average Dice scores of 98.04% (p < 0.001) and 97.67% (p < 0.001) in the DWI and T2WI image datasets, respectively. Conclusion The proposed RU-Net is believed to be potential for advancing preclinical stroke investigation and providing an efficient tool for pathological rat brain image extraction, where accurate segmentation of the rat brain region is fundamental.