Cerebral haemorrhage is a serious subtype of stroke, with most patients experiencing short-term haematoma enlargement leading to worsening neurological symptoms and death. The main hemostatic agents currently used for cerebral haemorrhage are antifibrinolytics and recombinant coagulation factor VIIa. However, there is no clinical evidence that patients with cerebral haemorrhage can benefit from hemostatic treatment. We provide an overview of the mechanisms of haematoma expansion in cerebral haemorrhage and the progress of research on commonly used hemostatic drugs. To improve the semantic segmentation accuracy of cerebral haemorrhage, a segmentation method based on RGB-D images is proposed. Firstly, the parallax map was obtained based on a semiglobal stereo matching algorithm and fused with RGB images to form a four-channel RGB-D image to build a sample library. Secondly, the networks were trained with 2 different learning rate adjustment strategies for 2 different structures of convolutional neural networks. Finally, the trained networks were tested and compared for analysis. The 146 head CT images from the Chinese intracranial haemorrhage image database were divided into a training set and a test set using the random number table method. The validation set was divided into four methods: manual segmentation, algorithmic segmentation, the exact Tada formula, and the traditional Tada formula to measure the haematoma volume. The manual segmentation was used as the “gold standard,” and the other three algorithms were tested for consistency. The results showed that the algorithmic segmentation had the lowest percentage error of 15.54 (8.41, 23.18) % compared to the Tada formula method.
This paper mainly studies the clinical efficacy of sodium nitroprusside and urapidil in the treatment of acute hypertensive intracerebral hemorrhage and analyzes the brain CT image detection based on a deep learning algorithm. A total of 132 cases of acute hypertension admitted to XXX hospital from XX 2019 to XX 2020 were retrospectively analyzed. The diseases of all patients were clinically confirmed, and patients were divided into groups according to the differences in treatment methods. Urapidil was used for group 1; sodium nitroprusside was used for group 2; and urapidil combined with sodium nitroprusside was used for group 3. A convolutional neural network in deep learning is used to construct intelligent processing to classify brain CT images of patients. The network performance of AlexNet, GoogLeNet, and CNN3 is predicted. The results show that GoogLeNet has the highest prediction accuracy of 0.83, followed by AlexNet with 0.80 and CNN3 with 0.74. The results of the performance parameter curve show that the GoogLeNet has the highest performance parameter of 0.89, followed by AlexNet and CNN3 network. The performance parameter curve of machine learning is above 0.80. After five weeks of drug treatment, the hematoma volume was (3.8 ± 2.6) mL in group1, (7.6 ± 2.8) mL in group 2, and (2.8 ± 1.5) mL in group 3. After 5 days of treatment, the patients’ heart rate changed compared with before treatment. Compared with group 2, there were significant differences between groups 1 and 3 ( P < 0.01 ), indicating that the therapeutic effect of the combination group was significantly better than that of the other groups alone. In summary, the combination of sodium nitroprusside and urapidil has a significantly better effect than that of urapidil alone. A convolutional neural network based on deep learning improves the recognition accuracy of medical images.
High environmental hydrogen peroxide (H2O2) has been demonstrated to be toxic for fish. However, the response mechanism of fish to chronic H2O2 exposure is not yet well understood. Therefore, this study aimed to investigate the alteration in ion transport in gills and analyzed the potential response mechanism after chronic H2O2 exposure. The common carps were exposed to 0, 0.25, 0.50, and 1.00 mM of H2O2 for 14 days. The histopathological evaluation results indicated that H2O2 exposure caused incomplete gill filament structure. In the plasma, H2O2 exposure suppressed the potassium (K+) concentration but increased sodium (Na+) concentration. In the gills, the calcium (Ca2+) level was raised, but the K+ and chlorine (Cl−) levels were decreased after H2O2 exposure. After 14 days of exposure, H2O2 prompted the activities of Ca2+/Mg2+-ATPase and H+/K+-ATPase but suppressed Na+/K+-ATPase activity in the gills. Gene transcription analysis showed that the ion-regulation-related genes including nkaa and rhbg were downregulated after H2O2 exposure. In addition, H2O2 exposure upregulated the mRNA levels of cam and camk II, indicating that the Ca2+ singling pathway was activated. In conclusion, our data showed that chronic H2O2 exposure altered gill structure and disturbed ion transport, which further negatively affected the equilibrium of ions and osmotic pressure.
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