TMP269 is a selective class IIA histone deacetylase inhibitor that has a protective effect on the central nervous system, whose specific mechanism of action is unclear. We aimed to reveal the optimal concentration of TMP269 for protecting against cerebral ischemia/reperfusion injury and its neuroprotective mechanism. Male Sprague-Dawley rats were randomly divided into sham, ischemia/reperfusion, and 1, 4, 10 and 16 mg/kg TMP269 groups. Cerebral ischemia/reperfusion injury was induced by middle cerebral artery occlusion. TMP269 was intraperitoneally administered at different doses 0.5 hours before ischemia induction. Western blot assay and immunohistochemistry were used to detect effects of TMP269 on histone 2 acetylation. The results showed that the level of histone 2 acetylation was increased 24 hours after TMP269 injection. 2,3,5-Triphenyltetrazolium chloride staining was utilized to examine effect of TMP269 on infarct volume. The results found that different doses of TMP269 could reduce the infarct volume. Western blot assay, immunohistochemistry and Evans blue staining were employed to measure the effect of TMP269 on blood-brain barrier. The results showed that TMP269 counteracted the abnormal endothelial cell permeability changes caused by cerebral ischemia/reperfusion. Western blot assay and immunohistochemistry were used to determine the effect of TMP269 on tissue kallikrein. The results found that TMP269 up-regulated the expression of tissue kallikrein. Western blot assay further determined the optimal concentration to be 4 mg/kg. In conclusion, TMP269 plays a neuroprotective role by up-regulating the level of histone 2 acetylation, alleviating endothelial cell injury after cerebral ischemia/reperfusion, and up-regulating the expression of tissue kallikrein. The experimental protocol was approved in 2014 by the Department of Laboratory Animal Science, Fudan University, China (approval No. 20140143C001).

Objectives: Insomnia was associated with cerebral structural changes and Alzheimer’s disease. However, associations among cerebral perfusion, insomnia with cerebral small vessel disease (CSVD), and cognitive performance were little investigated.Methods: This cross-sectional study included 89 patients with CSVDs and white matter hyperintensities (WMHs). They were dichotomized into the normal sleep and poor sleep group, according to Pittsburgh sleep quality index (PSQI). Baseline characteristics, cognitive performance, and cerebral blood flow (CBF) were measured and compared between the two groups. The association or correlation between cerebral perfusion, cognition, and insomnia was analyzed using binary logistic regression.Results: Our study found that declined MoCA score (P = .0317) was more prevalent in those with poor sleep. There was a statistical difference in the recall (P = .0342) of MMSE, the delayed recall (P = .0289) of MoCA between the two groups. Logistic regression analysis showed educational background (P < .001) and insomnia severity index (ISI) score (P = .039) were independently correlated with MoCA scores. Arterial spin labeling demonstrated that left hippocampal gray matter perfusion was significantly reduced (P = .0384) in the group with poor sleep. And, negative correlation was found between left hippocampal perfusion and PSQI scores.Conclusions: In the patients with CSVDs, insomnia severity was associated with cognitive decline. Left hippocampal gray matter perfusion was correlated with PSQI scores in CSVDs.

Aicardi-Goutières syndrome (AGS) is a rare, single-gene disorder, characterized by neurologic and skin involvement with an increased level of interferon-α (IFN-α) in the CSF. We describe the case of a young patient presenting with recurrent ischemic stroke. Evaluation revealed the presence of chilblains, white matter abnormalities, cerebral atrophy, and raised IFN-α in the CSF. Compound heterozygous variants of TREX1 were detected, confirming a diagnosis of AGS. After excluding other causes, we attributed the stroke to AGS. Tofacitinib, a Janus kinase inhibitor, was administered to our patient in addition to antiplatelet drugs. There was no recurrence of stroke during 3-month follow-up. This is a rare case of recurrent stroke in TREX1-mutated AGS. Small vessel involvement has been previously demonstrated to play a significant role in the pathogenesis of AGS. This microvascular mechanism might explain the occurrence of ischemic stroke in our patient. For young patients with stroke and multiple system involvement, genetic disorders including AGS should be considered.

Atrial fibrillation (AF) represents a major risk factor of ischemic stroke recurrence with serious management implications. However, it often remains undiagnosed due to lack of standard or prolonged cardiac rhythm monitoring. We aim to create a novel end-to-end artificial intelligence (AI) model that uses MRI data to rapidly identify high AF risk in patients who suffer from an acute ischemic stroke. This study comprises an internal retrospective cohort and a prospective cohort from Shanghai sixth people's hospital to train and validate an MRI-based AI model. Between January 1, 2018 and December 31, 2021, 510 patients were retrospectively enrolled for algorithm development and performance was measured using fivefold cross-validation. Patients from this trial were registered with http://www.chictr.org.cn, ChiCTR2200056385. Between September 1, 2022 and July 31, 2023, 73 patients were prospectively enrolled for algorithm test. An external cohort of 175 patients from Huashan Hospital, Minhang Hospital, and Shanghai Tenth People's Hospital was also enrolled retrospectively for further model validation. A combined classifier leveraging pre-defined radiomics features and de novo features extracted by convolutional neural network (CNN) was proposed to identify underlying AF in acute ischemic stroke patients. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were calculated for model evaluation. The top-performing combined classifier achieved an AUC of 0.94 (95% CI, 0.90–0.98) in the internal retrospective validation group, 0.85 (95% CI, 0.79–0.91) in the external validation group, and 0.87 (95% CI, 0.90–0.98) in the prospective test group. Based on subgroup analysis, the AI model performed well in female patients, patients with NIHSS > 4 or CHA₂DS₂-VASc ≤ 3, with the AUC of 0.91, 0.94, and 0.90, respectively. More importantly, our proposed model identified all the AF patients that were diagnosed with Holter monitoring during index stroke admission. Our work suggested a potential association between brain ischemic lesion pattern on MR images and underlying AF. Furthermore, with additional validation, the AI model we developed may serve as a rapid screening tool for AF in clinical practice of stroke units. This work was supported by grants from the 10.13039/501100001809 National Natural Science Foundation of China (NSFC, Grant Number: 81871102 and 82172068); 10.13039/501100008233 Shanghai Jiao Tong University School of Medicine , Two-Hundred Talent Program as Research Doctor (Grant Number: SBR202204); Municipal Science and Technology Commission Medical Innovation Project of Shanghai, (Grant/Award Number: 20Y11910200); 10.13039/501100010167 Research Physician Program of Shanghai Shen Kang Hospital Development Center (Grant Number: SHD2022CRD039) to Dr. Dong Huang and the SJTU Trans-med Awards Research (No. 20220101) to Dahong Qian.