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This paper summarized the integrated traditional Chinese and Western medicine nursing of a case of severe diabetic ketoacidosis (DKA) with acute intestinal obstruction complicated with pneumonia secondary to gastric adenocarcinoma treated with anti-PD-1. The main points of nursing included: disease monitoring, nausea and vomiting nursing, lack of knowledge and other general nursing measures. The key and difficult points of nursing for this case were complicated with intestinal obstruction and ICPis related diabetes. Therefore, integrated traditional Chinese and western medicine nursing and corresponding nursing measures were taken to relieve the clinical symptoms and improve the rehabilitation of the patient. (本文总结1例基于胃癌免疫治疗引起重度糖尿病酮症酸中毒(DKA)伴肠梗阻并发肺炎患者的中西医结合护理经验。患者病情复杂, 合并症多, 在加强病情监测、心理护理、恶心呕吐护理、健康宣教等常规护理的基础上, 针对并发症采取肠梗阻护理干预、DKA急性发作期护理干预, 改善患者症状, 促进患者早期康复。)

The changes of yeast count, ethanol, reducing sugar, pH, titratable acidity and total phenols as well as antioxidant properties such as hydroxyl and superoxide anion radical scavenging activities and reducing power during Suaeda salsa fermentation by yeast were studied. The SGompertz, DoseResp, Logistic and Boltzmann models were used to investigate the fermentation kinetics. Meanwhile, a comprehensive evaluation index (CEI) system of S. salsa fermentation was developed by correlation analysis (CA) and principal component analysis (PCA). The growth kinetic model of yeast was established using the DoseResp and Boltzmann equations, the kinetic model of ethanol production was established using the SGompertz equation, and the kinetic model of reducing sugar consumption was established using the Logistic equation. The fitting coefficients (R2) of these models were 0.977, 0.995 and 0.982, respectively, indicating excellent goodness of fit. CA showed that total phenols and organic acid were the major contributors to the antioxidant capacity of fermented S. salsa. PCA showed that the CEI value increased first, then leveled off, and finally decreased. The fermentation endpoint was on the eighth day in overall consideration. The results of this study provide a theoretical basis for the precision preparation of fermented S. salsa.

The potentiostat is critical in the development of electrochemical systems; however, its cumbersome detection and high cost considerably limit its large-scale application. To provide an affordable alternative to developing countries and resource-constrained areas, this study designs an electrochemical detection system based on smartphones, which uses Bluetooth Low Energy to convert open-source potentiostat data based on PSoC-5LP. The WeChat application on the smartphone provides an interface for entering experimental parameters and visualizing the results in real time. The smartphone-based electrochemical detection system has a simple design and reduces the size (10×3×0.3 cm3) and the cost of the hardware ($ 18). The system performs the most commonly used cyclic voltammetry for electrochemical detection, with results that are comparable to those obtained using a commercial potentiostat and an error rate of 1.3 %. In the classical teaching experiment of electrochemical determination of ascorbic acid in orange juice samples, the measured value of the system is 0.367±0.012 mg/mL, compared with the standard reference value of 0.37 mg/mL, which is obviously a convincing value. Therefore, this system is a low-cost, reliable alternative to a potentiostat for research, education or product integration development.

In brain imaging segmentation, precise tumor delineation is crucial for diagnosis and treatment planning. Traditional approaches include convolutional neural networks (CNNs), which struggle with processing sequential data, and transformer models that face limitations in maintaining computational efficiency with large-scale data. This study introduces MambaBTS: a model that synergizes the strengths of CNNs and transformers, is inspired by the Mamba architecture, and integrates cascade residual multi-scale convolutional kernels. The model employs a mixed loss function that blends dice loss with cross-entropy to refine segmentation accuracy effectively. This novel approach reduces computational complexity, enhances the receptive field, and demonstrates superior performance for accurately segmenting brain tumors in MRI images. Experiments on the MICCAI BraTS 2019 dataset show that MambaBTS achieves dice coefficients of 0.8450 for the whole tumor (WT), 0.8606 for the tumor core (TC), and 0.7796 for the enhancing tumor (ET) and outperforms existing models in terms of accuracy, computational efficiency, and parameter efficiency. These results underscore the model's potential to offer a balanced, efficient, and effective segmentation method, overcoming the constraints of existing models and promising significant improvements in clinical diagnostics and planning. [ABSTRACT FROM AUTHOR]