ObjectiveTo investigate the effect of ADAM33 gene silencing in VSMCs on the proliferation and lumen formation of airway vascular endothelial cells (VECs) in a co-culture system and the possible regulatory mechanism. MethodsThe Human aortic smooth muscle cells (HASMCs) and human pulmonary microvascular endothelial cells (HPMECs) were used to construct a cell co-culture system. ADAM33 gene expression was silenced by lentivirus transfection technique, and the subjects were divided into endothelial cell blank group, co-culture group, co-culture +shRNA negative control group, and co-culture + ADAM33-SHRNA group. The expressions of sADAM33, VEGFA,VEGER2, ang-1 and ang-2 in co-culture system were detected by ELISA. The proliferation and lumen formation of HPMECs were observed by CCK-8 and Transwell experiments. The protein expression of Tie2, PI3K, Akt, and mTOR key molecules in Tie2/PI3K/Akt/mTOR signaling pathway and the phosphorylation levels of AKT and mTOR were detected by Western-blotting method. Results① Compared with the co-culture group (0.851±0.036) and the co-culture + shRNA negative control group (0.828±0.047), the OD value of the co-culture + ADAM33shRNA group (0.699±0.038) was significantly decreased (P<0.05). ② Compared with the co-culture group (159.169±15.740) and the co-culture +shRNA negative control group (157.357±21.612), the tube length of the co-culture +ADAM33shRNA group (120.812±2.791) was also significantly decreased (P<0.05). ③ After ADAM33 gene expression of HASMCs was silted in co-culture system, the expression levels of VEGFA, VEGFR2, ang-1 and ang-2 were significantly decreased (P<0.05), while the expression levels of Tie2, PI3K, P-Akt and P-mtor were decreased (P<0.05). ConclusionsSilencing the expression of the ADAM33 gene could reduce the release of sADAM33 from the membrane of the airway VSMCs, regulate the proliferation and lumen formation of airway VECs by reducing the expression of VEGF/VEGFR and inhibiting the activities of the Tie2/PI3K/Akt/mTOR signaling pathways,and then participate in airway vascular remodeling in asthma.
The diagnosis of pancreatic cancer is very important. The main method of diagnosis is based on pathological analysis of microscopic image of Pap smear slide. The accurate segmentation and classification of images are two important phases of the analysis. In this paper, we proposed a new automatic segmentation and classification method for microscopic images of pancreas. For the segmentation phase, firstly multi-features Mean-shift clustering algorithm (MFMS) was applied to localize regions of nuclei. Then, chain splitting model (CSM) containing flexible mathematical morphology and curvature scale space corner detection method was applied to split overlapped cells for better accuracy and robustness. For classification phase, 4 shape-based features and 138 textural features based on color spaces of cell nuclei were extracted. In order to achieve optimal feature set and classify different cells, chain-like agent genetic algorithm (CAGA) combined with support vector machine (SVM) was proposed. The proposed method was tested on 15 cytology images containing 461 cell nuclei. Experimental results showed that the proposed method could automatically segment and classify different types of microscopic images of pancreatic cell and had effective segmentation and classification results. The mean accuracy of segmentation is 93.46%±7.24%. The classification performance of normal and malignant cells can achieve 96.55%±0.99% for accuracy, 96.10%±3.08% for sensitivity and 96.80%±1.48% for specificity.
The innovative behavior of clinical nurses is of great significance for the professional development of nurses and the improvement of nursing service quality. This research topic has received continuous attention from domestic and foreign scholars. There is still significant room for improvement in the level of innovative behavior among clinical nurses in China. Constructing effective interventions to enhance innovative behavior among clinical nurses in China is an urgent requirement to promote the development of nursing informatization and nursing quality. This article reviews the intervention forms, theoretical support, effectiveness, and limitations of innovative behaviors among clinical nurses both domestically and internationally. It proposes prospects for future intervention plans, aiming to provide ideas and references for nursing managers to develop tailored, scientific, and effective intervention strategies.
Amyloid β-protein (Aβ) deposition is an important prevention and treatment target for Alzheimer’s disease (AD), and early detection of Aβ deposition in the brain is the key to early diagnosis of AD. Magnetic resonance imaging (MRI) is the perfect imaging technology for the clinical diagnosis of AD, but it cannot display the plaque deposition directly. In this paper, based on two feature selection modes-filter and wrapper, chain-like agent genetic algorithm (CAGA), principal component analysis (PCA), support vector machine (SVM) and random forest (RF), we designed six kinds of feature learning classification algorithms to detect the information (distribution) of Aβ deposition through magnetic resonance image pixels selection. Firstly, we segmented the brain region from brain MR images. Secondly, we extracted the pixels in the segmented brain region as a feature vector (features) according to rows. Thirdly, we conducted feature learning on the extracted features, and obtained the final optimal feature subset by voting mechanism. Finally, using the final optimal selected features, we could find and mark the corresponding pixels on the MR images to show the information about Aβ plaque deposition by elastic mapping. According to the experimental results, the proposed pixel features learning methods in this paper could extract and reflect Aβ plaque deposition, and the best classification accuracy could be as high as 80%, thereby showing the effectiveness of the methods. The proposed methods can precisely detect the information of the Aβ plaque deposition, thereby being helpful for improving classification accuracy of diagnosis of AD.
Methods for achieving diagnosis of Parkinson’s disease (PD) based on speech data mining have been proven effective in recent years. However, due to factors such as the degree of disease of the data collection subjects and the collection equipment and environment, there are different categories of sample aliasing in the sample space of the acquired data set. Samples in the aliased area are difficult to be identified effectively, which seriously affects the classification accuracy of the algorithm. In order to solve this problem, a partition bagging ensemble learning is proposed in this article, which measures the aliasing degree of the sample by designing the the ratio of sample centroid distance metrics and divides the training set into multiple subsets. And then the method of transfer training of misclassified samples is used to adjust the results of subset partitioning. Finally, the optimized weights of each sub-classifier are used to integrate the test results. The experimental results show that the classification accuracy of the proposed method is significantly improved on two public datasets and the increasement of mean accuracy is up to 25.44%. This method not only effectively improves the classification accuracy of PD speech dataset, but also increases the sample utilization rate, providing a new idea for the diagnosis of PD.
ObjectiveTo compare the clinical effectiveness of Chinese medicine and integrated Chinese medicine and antimicrobial drugs in the treatment of pneumonia. MethodsThe electronic medical record (EMR) of patients with pneumonia who admitted to the Classical Department of Chinese Medicine of Guangdong Hospital of Traditional Chinese Medicine from November 29, 2012 to June 17, 2022 were retrospectively collected. The patients were divided into two groups according to whether they were treated with antimicrobial drugs on the basis of Chinese medicine treatment. The non-exposed group was the traditional Chinese medicine group, and the exposed group was the integrated Chinese medicine and antimicrobial drugs group. Propensity score matching method was used to balance possible confounding factors. COX regression analysis was performed on the matched cohort to compare death rates among the groups, and Kaplan-Meier curve was drawn to evaluate the survival probability during hospitalization. The proportion of maximum oxygen concentration and duration of fever remission were compared between the two groups. ResultsThis study included a total of 898 cases, with the majority (over 95%) falling within the range of mild to moderate severity. After propensity score matching,180 patients were remained in each group, among which the baseline characteristics were comparable. The primary outcome indicators showed that the risk of death during hospitalization was same in the integrated Chinese medicine and antimicrobial drugs group and in the Chinese medicine group (HR=1.52, 95%CI 0.36 to 6.39, P=0.566), the subgroup analysis is consistent with the overall trend of the results, and the differences were not statistically significant. The results indicate that during the hospitalization, the overall and subgroup mortality rates were similar between the two groups. The treatment effectiveness on the disappearance of major symptoms such as fever, cough, sputum production, fatigue, shortness of breath, and chest pain were comparable in both groups. The secondary outcome indicators showed that there was no statistical significance in the comparison of the proportion of maximum oxygen therapy concentration and the stable duration of fever remission between the two groups. ConclusionIn the treatment of patients with mainly mild to moderate pneumonia, the effectiveness of the Chinese medicine group and the integrated Chinese medicine and antimicrobial drugs group in the hospitalization mortality, the disappearance of major symptoms, the proportion of maximum oxygen therapy concentration and the stable duration of fever remission are similar. Chinese medicine has a positive significance in reducing the use of antimicrobials in patients with pneumonia.
Traditional Chinese medicine has been used for the treatment of many diseases including acute infections often associated with public health emergencies for thousands of years. However, clinical evidence supporting the use of these treatments is insufficient, and the mechanism for using Chinese medicine therapy in the public health setting has not been fully established. In this report, the Evidence-based Traditional and Integrative Chinese medicine Responding to Public Health Emergencies Working Group proposed five recommendations to facilitate the inclusion of Chinese medicine as part of our responses to public health emergencies. It is expected that the Working Group’s proposals may promote the investigation and practice of Chinese Medicine in public health settings.