目的 建立重組人內皮抑素（恩度）聯合順鉑一線治療腫瘤進展的小鼠模型，繼續應用內皮抑素聯合紫杉醇二線治療，研究內皮抑素協同紫杉醇抗腫瘤的作用及其機制。 方法 建立小鼠Lewis 肺癌移植瘤動物模型，內皮抑素聯合順鉑治療后觀察腫瘤生長情況，遴選出腫瘤進展小鼠16只，隨機留取1只，余15只小鼠隨機分成紫杉醇組和聯合用藥組治療，觀察療效。另取上述腫瘤進展小鼠1只，剝離腫瘤組織，重新接種，將成瘤小鼠隨機分成生理鹽水組，紫杉醇組及聯合用藥組治療，觀察療效。治療結束后24 h處死所有小鼠，采用免疫組織化學CD31單克隆抗體標記檢測微血管密度（MVD），采用原位末端轉移酶（TUNEL）檢測細胞凋亡。 結果 只腫瘤進展小鼠中，聯合用藥組相比紫杉醇組生存時間無明顯延長，但腫瘤體積增長較慢；而在重新接種成瘤的小鼠中，聯合用藥組較其余各組微血管密度明顯減低（P＜0.05），凋亡指數明顯增加（P＜0.05），腫瘤體積抑制明顯。 結論 在內皮抑素聯合順鉑治療腫瘤進展的小鼠模型中，繼續應用內皮抑素治療與紫杉醇有較明顯的協同抗腫瘤作用。
ObjectiveTo review the role and research progress of mechanotransduction signaling pathway in distraction osteogenesis, so as to provide theoretical basis and reference for clinical treatment. MethodsThe role and research progress of mechanotransduction signaling pathway in distraction osteogenesis were summarized by extensive review of relevant literature at home and abroad. ResultsThe mechanotransduction signaling pathway plays a central role of “sensation-transformation-execution” in distraction osteogenesis, and activates a series of molecular mechanisms to promote the regeneration and remodeling of bone tissue by integrating external mechanical signals. Mechanical stimuli are converted into mechanotransduction signals through the perception of integrins, Piezo1 ion channels and bone cell networks. Activate downstream molecules are transduce through signal pathways such as Wnt/β-catenin, transforming growth factor β/bone morphogenetic protein-Smad, mitogen-activated protein kinase, protein kinase Hippo-Yes-associated protein/transcriptional coactivator with PDZ-binding motif, and phosphatidylinositol 3-kinase/ protein kinase B, so as to achieve the effects of promoting osteoblasts proliferation, accelerating endochondral ossification, regulating bone resorption and the like, thereby promoting the regeneration of new bone in the distraction area. The study of mechanotransduction signaling pathways in distraction osteogenesis is expected to optimize the mechanical parameters of distraction osteogenesis and provide targeted intervention strategies for accelerating new bone regeneration and mineralization in the distraction zone. However, the specific mechanism of mechanotransduction signaling pathway in distraction osteogenesis remains to be further elucidated, and artificial intelligence and multi-omics analysis may be the future development direction of mechanotransduction signaling pathway. ConclusionIn distraction osteogenesis, mechanotransduction signal transduction is the core mechanism of bone regeneration in the distraction zone, which regulates cell behavior and tissue regeneration by converting mechanical stimulation into biochemical signals.
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.