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Background: To explore the effect of estrogen on human cerebral vascular smooth muscle cells (VSMCs) and to clarify the molecular mechanism of estrogen inhibition of VSMC proliferation, which could provide an important reference basis for the clinical treatment of hypertensive intracerebral hemorrhage. Method: Firstly, the effects of different concentrations of estradiol and estrogen receptor (ESR) blocker (tamoxifen) on the proliferation of human VSMCs and the expression of estrogen-related receptor gene (ESR: ESR1, ESR2, GPER), myocardin (MYOCD), serum reaction factor (SRF), and apoptosis gene caspase-3 were measured to discover the effect and mechanism of tamoxifen on the proliferation and apoptosis of VSMCs. Secondly, the effects of estradiol on human VSMCs treated with angiotensin II (Ang II) were observed by measuring the expression of vascular smooth muscle markers, α-smooth muscle actin (α-SMA), SM22α, FLN, MCP-1, and TLR4. Results: Estradiol inhibited the proliferation of VSMCs by upregulating the expression of ESR1, ESR2, and GPER and downregulating the expression of caspase-3, MYOCD, and SRF, thereby inhibiting the apoptosis of vascular smooth muscle. At the same time, tamoxifen had opposite effects. Angiotensin II decreased the expression of α-SMA and SM22α and promoted the expression of FLN, MCP-1, and TLR4 protein, while estrogen had the opposite effects.Conclusions: Estrogen suppresses apoptosis by inhibiting the proliferation of human VSMCs and preventing it from changing from contractile to synthetic. Estrogen can further prevents vascular damage and regulate peripheral inflammatory reaction, thereby producing a protective effect on cardiovascular and cerebrovascular.

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Subarachnoid hemorrhage (SAH) is one of the critical and severe neurological diseases with high morbidity and mortality. Head computed tomography (CT) is among the preferred methods for the diagnosis of SAH, which is confirmed by CT showing high-density shadow in the subarachnoid space. Analysis of these images through a deep learning-based subarachnoid hemorrhage will reduce the approximate rate of misdiagnosis in general and missed diagnosis by clinicians in particular. Deep learning-based detection of subarachnoid hemorrhage mainly includes two tasks, i.e., subarachnoid hemorrhage classification and subarachnoid hemorrhage region segmentation. However, it is difficult to effectively judge reliability of the model and classify bleeding which is based on limited predictive probability of convolutional neural network output. Moreover, deep learning-based bleeding area segmentation requires a large amount of training data to be marked in advance and the large number of network parameters makes the model training unable to reach the optimal. To resolve these problems associated with existing models, Bayesian deep learning and neural network-based hybrid model is presented in this paper to estimate uncertainty and efficiently classify subarachnoid hemorrhage. Uncertainty estimation of the proposed model helps in judging whether the model’s prediction is reliable or not. Additionally, it is used to guide clinicians to find the neglected subarachnoid hemorrhage area. In addition, a teacher-student mechanism deep learning model was designed to introduce observational uncertainty estimation for semisupervised learning of subarachnoid hemorrhage. Observation uncertainty estimation detects the uncertain bleeding areas in CT images and then selects areas with high reliability. Finally, it uses these unlabeled data for model training purposes as well.