目的 建立重组人内皮抑素（恩度）联合顺铂一线治疗肿瘤进展的小鼠模型，继续应用内皮抑素联合紫杉醇二线治疗，研究内皮抑素协同紫杉醇抗肿瘤的作用及其机制。 方法 建立小鼠Lewis 肺癌移植瘤动物模型，内皮抑素联合顺铂治疗后观察肿瘤生长情况，遴选出肿瘤进展小鼠16只，随机留取1只，余15只小鼠随机分成紫杉醇组和联合用药组治疗，观察疗效。另取上述肿瘤进展小鼠1只，剥离肿瘤组织，重新接种，将成瘤小鼠随机分成生理盐水组，紫杉醇组及联合用药组治疗，观察疗效。治疗结束后24 h处死所有小鼠，采用免疫组织化学CD31单克隆抗体标记检测微血管密度（MVD），采用原位末端转移酶（TUNEL）检测细胞凋亡。 结果 只肿瘤进展小鼠中，联合用药组相比紫杉醇组生存时间无明显延长，但肿瘤体积增长较慢；而在重新接种成瘤的小鼠中，联合用药组较其余各组微血管密度明显减低（P＜0.05），凋亡指数明显增加（P＜0.05），肿瘤体积抑制明显。 结论 在内皮抑素联合顺铂治疗肿瘤进展的小鼠模型中，继续应用内皮抑素治疗与紫杉醇有较明显的协同抗肿瘤作用。
Real-time continuous glucose monitoring can help diabetics to control blood sugar levels within the normal range. However, in the process of practical monitoring, the output of real-time continuous glucose monitoring system is susceptible to glucose sensor and environment noise, which will influence the measurement accuracy of the system. Aiming at this problem, a dual-calibration algorithm for the moving-window double-layer filtering algorithm combined with real-time self-compensation calibration algorithm is proposed in this paper, which can realize the signal drift compensation for current data. And a real-time continuous glucose monitoring instrument based on this study was designed. This real-time continuous glucose monitoring instrument consisted of an adjustable excitation voltage module, a current-voltage converter module, a microprocessor and a wireless transceiver module. For portability, the size of the device was only 40 mm × 30 mm × 5 mm and its weight was only 30 g. In addition, a communication command code algorithm was designed to ensure the security and integrity of data transmission in this study. Results of experiments in vitro showed that current detection of the device worked effectively. A 5-hour monitoring of blood glucose level in vivo showed that the device could continuously monitor blood glucose in real time. The relative error of monitoring results of the designed device ranged from 2.22% to 7.17% when comparing to a portable blood meter.
Objective To evaluate the predictive effect of three machine learning methods, namely support vector machine (SVM), K-nearest neighbor (KNN) and decision tree, on the daily number of new patients with ischemic stroke in Chengdu. Methods The numbers of daily new ischemic stroke patients from January 1st, 2019 to March 28th, 2021 were extracted from the Third People’s Hospital of Chengdu. The weather and meteorological data and air quality data of Chengdu came from China Weather Network in the same period. Correlation analyses, multinominal logistic regression, and principal component analysis were used to explore the influencing factors for the level of daily number of new ischemic stroke patients in this hospital. Then, using R 4.1.2 software, the data were randomly divided in a ratio of 7∶3 (70% into train set and 30% into validation set), and were respectively used to train and certify the three machine learning methods, SVM, KNN and decision tree, and logistic regression model was used as the benchmark model. F1 score, the area under the receiver operating characteristic curve (AUC) and accuracy of each model were calculated. The data dividing, training and validation were repeated for three times, and the average F1 scores, AUCs and accuracies of the three times were used to compare the prediction effects of the four models. Results According to the accuracies from high to low, the prediction effects of the four models were ranked as SVM (88.9%), logistic regression model (87.5%), decision tree (85.9%), and KNN (85.1%); according to the F1 scores, the models were ranked as SVM (66.9%), KNN (62.7%), decision tree (59.1%), and logistic regression model (57.7%); according to the AUCs, the order from high to low was SVM (88.5%), logistic regression model (87.7%), KNN (84.7%), and decision tree (71.5%). Conclusion The prediction result of SVM is better than the traditional logistic regression model and the other two machine learning models.