Investigation of Crystallization Growth Characteristics of Mg(OH)2 Crystals under Unconstrained Conditions.

Utilizing MgO as the precursor and deionized water as the solvent, this study synthesized nanoparticles of Mg(OH)2 via hydrothermal methods, aiming to control its purity, particle size, and morphology by understanding its growth under non-uniform nucleation. Characterization of crystal morphology and structure was conducted through scanning electron microscopy and X-ray diffraction, while laser particle size detection assessed the secondary particle size distribution. The study focused on how MgO's hydrothermal process conditions influence Mg(OH)2 crystal growth, particularly through ion concentration and release rate adjustments to direct crystal growth facets. These adjustments shifted the dominant growth plane, enhancing the peak intensity ratio I001/I101 from 1.03 to 2.14, thereby reducing surface polarity and secondary aggregation of crystals. The study of the physicochemical properties of the same sample at different times revealed the pattern of crystal dissolution and recrystallization. A 2 h hydrothermal reaction notably altered the particle size distribution, with a decrease in particles sized 0.2~0.4 µm and an increase in those sized 0.4~0.6 µm, alongside new particles over 1 µm, indicating a shift toward uniformity through dissolution and recrystallization. Optimal conditions (6% magnesium oxide concentration, 160 °C, 2 h) led to the synthesis of highly dispersed, uniformly sized magnesium hydroxide, showcasing a simple, eco-friendly, and high-yield process.

Construction and Evaluation of Prognosis Prediction Model for Patients with Brain Contusion and Laceration Based on Machine Learning.

Objective: Finding valuable risk factors for the prognosis of brain contusion and laceration can help patients understand the condition and improve the prognosis. This study is aimed at analyzing the risk factors of poor prognosis in patients with brain contusion after the operation. Methods: A total of 136 patients with cerebral contusion and laceration combined with cerebral hernia treated by neurosurgical craniotomy in our hospital were retrospectively selected and divided into a training set (n = 95) and a test set (n = 41) by the 10-fold crossover method. Logistic regression and back-propagation neural network prediction models were established to predict poor prognosis factors. The receiver operating characteristic curve (ROC) and the calibration curve were used to verify the differentiation and consistency of the prediction model. Results: Based on logistic regression and back-propagation neural network prediction models, GCS score ≤ 8 on admission, blood loss ≥ 30 ml, mannitol ≥ 2 weeks, anticoagulants before admission, and surgical treatment are the risk factors that affect the poor prognosis of patients with a cerebral contusion after the operation. The area under the ROC was 0.816 (95% CI 0.705~0.926) and 0.819 (95% CI 0.708~0.931), respectively. Conclusion: The prediction model based on the risk factors that affect the poor prognosis of patients with brain contusion and laceration has good discrimination and accuracy.

Blood Pressure Model Based on Hybrid Feature Convolution Neural Network in Promoting Rehabilitation of Patients with Hypertensive Intracerebral Hemorrhage.

OBJECTIVE: Accurate prediction of the rise of blood pressure is essential for the hypertensive intracerebral hemorrhage. This study uses the hybrid feature convolution neural network to establish the blood pressure model instead of the traditional method of pulse waves. METHODS: The pulse waves of 100 patients were collected, and the pulse wave was decomposed into three bell wave compound forms to obtain the accurate pulse wave propagation time. Then, the mixed feature convolution neural network model ABP-net was proposed, which combined the pulse wave propagation time characteristics with the pulse wave waveform characteristics automatically extracted by one-dimensional convolution to predict the arterial blood pressure. Finally, according to the prediction results, 20 patients were treated before the high blood pressure appeared (model group), and another 20 patients with a daily fixed treatment scheme were selected as the control group. RESULTS: In 80 training sets, compared with linear regression and the random forest method, the hybrid feature convolution neural network has higher accuracy in predicting blood pressure. In 20 test sets, the blood pressure error was eliminated within 5 mmHg. The total effective rate in the model group and the control group was 95.0% and 85.0%, respectively (P = 0.035). After treatment, the scores of self-care ability of daily life and limb motor function in the model group were higher than those in the control group (P < 0.05). There were 8 cases (13.6%) in the model group and 17 cases (28.3%) in the control group due to the recurrence of cerebrovascular accident (P = 0.043). CONCLUSION: Drug treatment guided by a blood pressure model based on a hybrid feature convolution neural network for patients with hypertensive cerebral hemorrhage can significantly and smoothly reduce blood pressure, promote the health recovery, and reduce the occurrence of cerebrovascular accidents.

Intelligent body behavior feature extraction based on convolution neural network in patients with craniocerebral injury.

Patients with craniocerebral injury are in serious condition and inconvenient to take care of. This paper proposes a method of extracting the patient's body behavior feature based on convolution neural network, in order to reduce nursing workload and save hospital costs. The algorithm adopts double network model design, including the patient detection network model and the patient's body behavior feature extraction model. The algorithm is applied to the patient's body behavior detection system, so as to realize the recognition and monitoring of patients and improve the level of intelligent medical care for craniocerebral injury. Finally, the open source framework platform is used to test the patient behavior detection system. The experimental results show that the larger the test data set is, the higher the accuracy of patient body behavior feature extraction is. The average recognition rate of patient body behavior category is 97.8%, which verifies the effectiveness and correctness of the system. The application of convolution neural network connects image recognition with intelligent medical nursing, which provides reference and experience for intelligent medical nursing of patients with craniocerebral injury.

Effect of Lactobacillus paracasei N1115 and fructooligosaccharides in nonalcoholic fatty liver disease.

INTRODUCTION: Nonalcoholic fatty liver disease (NAFLD) is a growing health concern worldwide. Administration of probiotics and prebiotics has been proposed as a convenient and effective treatment. Our study aims to evaluate the therapeutic benefits of Lactobacillus paracasei N1115 (N1115) and fructooligosaccharides (FOS) by examining the histopathogenesis and underlying molecular events of NAFLD. MATERIAL AND METHODS: An NAFLD mouse model was established by feeding C57BL/6 mice with a high-fat diet (HFD). N1115, FOS and synbiotics were administered for 16 weeks. RESULTS: N1115, FOS and synbiotics alleviated HFD-induced hepatic steato-sis and release of tumor necrosis factor-α, and slowed the progression of cirrhosis. Compared to the HFD group, these dietary supplements reduced serum total triglyceride and cholesterol, and appeared to decrease the fasting blood glucose and insulin. Intraperitoneal glucose tolerance tests, homeostatic model assessment of insulin resistance and real-time PCR showed that the regimens could overcome insulin resistance. These findings were associated with the transcriptional repression of inflammatory factors such as lipopolysaccharides, Toll-like receptor 4 and nuclear factor-κB. Lastly, N1115, FOS, and synbiotics improved the intestinal barrier functions and histologic integrity. This was accompanied by the restoration of the p38 MAPK pathway and in-creased expression of the tight junction components occludin-1 and claudin-1. CONCLUSIONS: N1115, FOS and synbiotics are effective in the prevention and treatment of NAFLD. Our data support the translation of these agents into clinical evaluation in human subjects with NAFLD and/or associated risk factors.

Mechanism of Magnesium Oxide Hydration Based on the Multi-Rate Model.

The hydration of different active MgO under an unforced and ultrasonic condition was conducted in this paper to investigate the chemical kinetics model of the apparent reaction and discuss the mechanism combined with the product morphology. The dynamics fitting result shows that both the first-order and multi-rate model describe the hydration process under ultrasound well, while only the multi-rate model was right for the hydration process under an unforced condition. It indicated that the rate order of hydration was different in the hydration process under an unforced condition. The XRD and SEM show that the MgO hydration was a process of dissolution and crystallization. Part of the magnesium ions produced by dissolution of MgO did not diffuse into the solution in time, and adhered to the magnesium oxide surface and grew in situ instead. As a result, the difference in the hydration rate of the remaining MgO particles becomes wider and not in the same order (order of magnitude). The ultrasonic cavitation could prevent the in-situ growth of Mg(OH)2 crystal nuclei on the surface of MgO. It not only greatly improved the hydration rate of MgO and produced monodisperse Mg(OH)2 particles, but also made the first-order kinetics model fit the hydration process of MgO well.