BackgroundTo evaluate the efficacy of microvascular decompression (MVD) in reducing hypertension (HTN) in hypertensive patients with trigeminal neuralgia (TN).MethodsThe clinical data of 58 cases of neurogenic HTN with TN treated in our hospital were retrospectively reviewed. Preoperative MR revealed abnormal blood pressure in the left rostral ventrolateral medulla (RVLM) and the posterior cranial nerve root entry zone (REZ). The patients were divided into control group: only trigeminal nerve was treated with MVD; experimental group: trigeminal nerve, RVLM and REZ were treated with MVD at the same time. The patients were followed up for 6 months to 1 year to observe the changes of blood pressure.ResultsThere was no significant difference in gender, age, course of TN, course of HTN, grade of HTN and preoperative blood pressure between the two groups. After operation, the effective rate of HTN improvement with MVD was 32.1% in the control group. There was no significant difference in the preoperative and post operative blood pressure. (P△SBP = 0.131; P△BDP = 0.078). In the experimental group, the effective rate was 83.3%. The postoperative blood pressure was significantly lower than preoperative values. (P△SBP < 0.001; P△DBP < 0.001).ConclusionsMVD is an effective treatment for neurogenic HTN. However, the criteria for selecting hypertensive patients who need MVD to control their HTN still needs to be further determined. Possible indications may include: left trigeminal neuralgia, neurogenic HTN; abnormal blood pressure compression in the left RVLM and REZ areas on MR; and blood pressure in these patients can not be effectively controlled by drugs.

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Thermodynamic parameters play a crucial role in determining polar sea ice thickness (SIT); however, modeling their relationship is difficult due to the complexity of the influencing mechanisms. In this study, we propose a self-attention convolutional neural network (SAC-Net), which aims to model the relationship between thermodynamic parameters and SIT more parsimoniously, allowing us to estimate SIT directly from these parameters. SAC-Net uses a fully convolutional network as a baseline model to detect the spatial information of the thermodynamic parameters. Furthermore, a self-attention block is introduced to enhance the correlation among features. SAC-Net was trained on a dataset of SIT observations and thermodynamic data from the 2012–2019 freeze-up period, including surface upward sensible heat flux, surface upward latent heat flux, 2 m temperature, skin temperature, and surface snow temperature. The results show that our neural network model outperforms two thermodynamic-based SIT products in terms of accuracy and can provide reliable estimates of SIT. This study demonstrates the potential of the neural network to provide accurate and automated predictions of Arctic winter SIT from thermodynamic data, and, thus, the network can be used to support decision-making in certain fields, such as polar shipping, environmental protection, and climate science.