AIM: To explore the effect of nuclear factor-?B (NF-?B) on the anti-apoptosis induced by brain ischemia preconditioning (IP). METHODS: Temporary middle cerebral artery occlusion for 20 min followed three days reperfusion before 6 hours middle cerebral artery occlusion (MCAO) trancranially was used as preconditioning in Wistar rats. The protective role was evaluated by analyzing the infarct volume. The status of neuronal apoptosis was observed by TUNEL. The expression of NF?B p65 protein, the assay of SOD activity and MDA concentration were analyzed by using the methods of immunohistochemistry and cytochemistry. RESULTS: Compared to the control group, 20 min ischemic preconditioning, which did not produce neuronal damage obviously, reduced the infarct volume significantly after MCAO 6 h and obviously decreased the number of neural cell apoptosis in penumbra (P

Objective To explore the present situation of self-care ability among elderly hypertensive patients in the community and its influential factors.Methods 310 elderly hypertensive patients in Hengyang community were studied with a demographic questionnaire,ESCA and SSRS.Results The total mean scores of self-care ability were (105.43±19.25).68.71％ of the elderly hypertensive patients were at medium level and 29.35％ of them were at high level.Statistically significant relations were found between self-care ability and the following condition factors:education level,marital status,monthly income,obtaining health education status,social support,subjective support,availability,objective support and the severity of hypertension.There were no significant relations with sex and occupations.The results of multiple regression showed that the main factors ranged from high to low including the severity of hypertension,education level,monthly income,subjective support,obtaining health education status and marital status.Conclusions Community health nurses should aim directly at the influential factors,take effective measures and improve their self-care ability.

Although deep learning plays an important role in cell nucleus segmentation, it still faces problems such as difficulty in extracting subtle features and blurring of nucleus edges in pathological diagnosis. Aiming at the above problems, a nuclear segmentation network combined with attention mechanism is proposed. The network uses UNet network as the basic structure and the depth separable residual (DSRC) module as the feature encoding to avoid losing the boundary information of the cell nucleus. The feature decoding uses the coordinate attention (CA) to enhance the long-range distance in the feature space and highlights the key information of the nuclear position. Finally, the semantics information fusion (SIF) module integrates the feature of deep and shallow layers to improve the segmentation effect. The experiments were performed on the 2018 data science bowl (DSB2018) dataset and the triple negative breast cancer (TNBC) dataset. For the two datasets, the accuracy of the proposed method was 92.01% and 89.80%, the sensitivity was 90.09% and 91.10%, and the mean intersection over union was 89.01% and 89.12%, respectively. The experimental results show that the proposed method can effectively segment the subtle regions of the nucleus, improve the segmentation accuracy, and provide a reliable basis for clinical diagnosis.
Cell Nucleus/pathology* ; Image Processing, Computer-Assisted/methods*

OBJECTIVE In the process of drug dispensing, using computer vision technology to identify drugs is vulnerable to the influence of lighting, angle, packaging and other factors, which will produce large identification errors. Therefore, this paper proposes an object detection algorithm for drug appearance recognition(YOLOv4-GhostNet-CMB). METHODS Firstly, the algorithm redesigned the backbone feature extraction network in YOLOv4 by using GhostNet. Secondly, the CA attention model was brought into the Ghost module, aggregate features along horizontal and vertical directions to enhance the precise positioning of drugs. Finally, Bi-FPN feature pyramid structure was introduced to connect with the new backbone, and added a feature graph output which could enhance feature extraction and improved the detection accuracy of drugs. RESULTS The experimental results show that the average detection accuracy of YOLOv4-GhostNet-CMB algorithm reached 92.24%, which was a significant improvement of 4.49% compared with YOLOv4 algorithm in term of detection accuracy. CONCLUSION The model size is only 150 MB, nd this algorithm can effectively identify drugs.