Effect of Kang Fu Yan capsule on phenol mucilage-induced intrauterine adhesion injury in female rats

Purpose: To investigate the effect of Kang fu yan capsule (KFYC) on phenol mucilage-induced intrauterine adhesion (IUA) in a rat model, and the underlying mechanisms. Methods: An IUA model was established by injecting 0.06 mL of 25 % phenol mucilage into the uterus of female Sprague-Dawley rats. The IUA model rats (n=59) were randomly divided into 5 groups: IUA group, fuke qianjin tablet group (FKQJT, 0.22 mg/kg), and 3 KFYC groups given different doses of the drug i.e. 0.13, 0.39and 1.17 mg/kg. A group of 10 healthy female rats served as control. After 19 days treatment, blood samples were collected for determination of IL-2 and IL-10 by ELISA, while uterine tissues were subjected to histological examination using hematoxylin and eosin staining (H&E) and Masson staining. Expressions of Notch1, recombination signal binding protein-JK (RBP-JK), a disintegrin and metalloprotease (ADAM)-12, ADAM-15, matrix metalloprotein-9 (MMP-9), and inhibitor of NF-κB (IĸB) in uterine tissues were determined using RT-qPCR and western blot analysis. Results: Compared to IUA group, histological results showed reduced inflammatory cell infiltration in rat uterine tissue of KFYC group. Moreover, KFYC significantly reversed uterine fibrosis (p < 0.05). Serum concentrations of IL-2 significantly decreased in KFYC groups (p < 0.05 or p < 0.01), and there was significant increases the serum concentrations of IL-10 in KFYC groups (p < 0.05 or < 0.01), when compared to IUA group. The mRNA and protein expressions of Notch1, RBP-JK, ADAM-12, ADAM-15, MMP-9 were also significantly down-regulated (p < 0.05), while protein expression of IĸB was upregulated in KFYC group, when compared to IUA group. Conclusion: KFYC exerts an anti-IUA effect via amelioration of uterine inflammation and fibrosis, probably via a mechanism involving regulation of Notch1/ADAM pathway

Implications of Canonical Gauge Coupling Unification in High-Scale Supersymmetry Breaking

We systematically construct two kinds of models with canonical gauge coupling unification and universal high-scale supersymmetry breaking. In the first we introduce standard vector-like particles while in the second we also include non-standard vector-like particles. We require that the gauge coupling unification scale is from 5 x 10^{15} GeV to the Planck scale, that the universal supersymmetry breaking scale is from 10 TeV to the unification scale, and that the masses of the vector-like particles (M_V) are universal and in the range from 200 GeV to 1 TeV. Using two-loop renormalization group equation (RGE) running for the gauge couplings and one-loop RGE running for Yukawa couplings and the Higgs quartic coupling, we calculate the supersymmetry breaking scales, the gauge coupling unification scales, and the corresponding Higgs mass ranges. When the vector-like particle masses are less than 1 TeV, these models can be tested at the LHC.Comment: 25 pages, 4 figure

Antipsychotics-induced improvement of cool executive function in individuals living with schizophrenia

Cool executive dysfunction is a crucial feature in people living with schizophrenia which is related to cognition impairment and the severity of the clinical symptoms. Based on electroencephalogram (EEG), our current study explored the change of brain network under the cool executive tasks in individuals living with schizophrenia before and after atypical antipsychotic treatment (before_TR vs. after_TR). 21 patients with schizophrenia and 24 healthy controls completed the cool executive tasks, involving the Tower of Hanoi Task (THT) and Trail-Marking Test A-B (TMT A-B). The results of this study uncovered that the reaction time of the after_TR group was much shorter than that of the before_TR group in the TMT-A and TMT-B. And the after_TR group showed fewer error numbers in the TMT-B than those of the before_TR group. Concerning the functional network, stronger DMN-like linkages were found in the before_TR group compared to the control group. Finally, we adopted a multiple linear regression model based on the change network properties to predict the patient’s PANSS change ratio. Together, the findings deepened our understanding of cool executive function in individuals living with schizophrenia and might provide physiological information to reliably predict the clinical efficacy of schizophrenia after atypical antipsychotic treatment

Identification the genetic influence of SARS-CoV-2 infections on IgA nephropathy based on bioinformatics method.

Introduction: Coronavirus disease-2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. It was initially detected in Wuhan, China, in December 2019. In March 2020, the World Health Organization (WHO) declared COVID-19 a global pandemic. Compared to healthy individuals, patients with IgA nephropathy (IgAN) are at a higher risk of SARS-CoV-2 infection. However, the potential mechanisms remain unclear. This study explores the underlying molecular mechanisms and therapeutic agents for the management of IgAN and COVID-19 using the bioinformatics and system biology way. Methods: We first downloaded GSE73953 and GSE164805 from the Gene Expression Omnibus (GEO) database to obtain common differentially expressed genes (DEGs). Then, we performed the functional enrichment analysis, pathway analysis, protein-protein interaction (PPI) analysis, gene regulatory networks analysis, and potential drug analysis on these common DEGs. Results: We acquired 312 common DEGs from the IgAN and COVID-19 datasets and used various bioinformatics tools and statistical analyses to construct the PPI network to extract hub genes. Besides, we performed gene ontology (GO) and pathway analyses to reveal the common correlation between IgAN and COVID-19. Finally, on the basis of common DEGs, we determined the interactions between DEGs-miRNAs, the transcription factor-genes (TFs-genes), protein-drug, and gene-disease networks. Discussion/Conclusion: We successfully identified hub genes that may act as biomarkers of COVID-19 and IgAN and also screened out some potential drugs to provide new ideas for COVID-19 and IgAN treatment

Hypothetical Protein Cpn0308 Is Localized in the Chlamydia pneumoniae Inclusion Membrane

The hypothetical protein encoded by Chlamydia pneumoniae open reading frame cpn0308 was detected in inclusion membranes of C. pneumoniae-infected cells using antibodies raised with Cpn0308 fusion proteins. The anti-Cpn0308 antibodies did not cross-react with IncA, a known C. pneumoniae inclusion membrane protein, although the anti-Cpn0308 antibody staining overlapped with the anti-IncA antibody labeling. The labeling of the inclusion membrane by the anti-Cpn0308 antibody was specifically blocked by the Cpn0308 but not IncA fusion proteins. The Cpn0308 antigen was detectable 24 h after infection and remained in the inclusion membrane throughout the infection course

Re-perceive 3D printing with Artificial Intelligence

How can machine learning be combined with intelligent construction, materialtesting and other related topics to develop a new method of fabrication? Thispaper presents a set of experiments on the dynamic control of the heat deflectionof thermoplastics in searching for a new 3D printing method with the dynamicbehaviour of PLA and with a comprehensive workflow utilizing mechanicautomation, computer vision, and artificial intelligence. Additionally, this paperwill discuss in-depth the performance of different types of neural networks used inthe research and conclude with solid data on the potential connection between thestructure of neural networks and the dynamic, complex material performance weare attempting to capture

Identification and Portrait of Urban Functional Zones Based on Multisource Heterogeneous Data and Ensemble Learning

Urban functional zones are important space carriers for urban economic and social function. The accurate and rapid identification of urban functional zones is of great significance to urban planning and resource allocation. However, the factors considered in the existing functional zone identification methods are not comprehensive enough, and the recognition of functional zones stops at their categories. This paper proposes a framework that combines multisource heterogeneous data to identify the categories of functional zones and draw the portraits of functional zones. The framework comprehensively describes the features of functional zones from four aspects: building-level metrics, landscape metrics, semantic metrics, and human activity metrics, and uses a combination of ensemble learning and active learning to balance the identification accuracy of functional zones and the labeling cost during large-scale generalization. Furthermore, sentiment analysis, word cloud analysis, and land cover proportion maps are added to the portraits of typical functional zones to make the image of functional zones vivid. The experiment carried out within the Fifth Ring Road, Haidian District, Beijing, shows that the overall accuracy of the method reached 82.37% and the portraits of the four typical functional zones are clear. The method in this paper has good repeatability and generalization, which is helpful to carry out quantitative and objective research on urban functional zones