Analysis of Differential Gel Electrophoresis of Paclitaxol Resistant and Sensitive Lung Adenocarcinoma Cells' Secretome

Background and objective Paclitaxol (PTX) resistance is one of main factors which affect the outcome of chemotherapy of lung adenocarcinoma. The aim of this study is to compare the secreted protein expression profiles between Paclitaxol (PTX) resistant and sensitive lung adenocarcinoma cells by proteomic research method, so as to provide evidence of choosing individual chemotherapy drugs in clinical treatment. Methods Total secreted proteins extracted from a PTX sensitive cell line A549 and a PTX resistant cell line A549-Taxol were separated by fluorscent differential gel electrophoresis (DIGE). High quality 2-DE profiles were obtained and analyzed by Decyder 6.5 analysis software to screen differentially expressed protein spots. Those spots were identified by mass spectrometry. Results 2-DE patterns of lung adenocarcinoma cells with high-resolution and reproducibility were obtained. 76 significantly differentially expressed protein spots were screened, 19 proteins were identified by mass spectrometry. The identified proteins could be classified into different catogories: metabolic enzyme, extracellular matrix (ECM) degradation enzyme, cytokine, signal transducer, cell adhesion, and so on. Conclusion Multiple secreted proteins related to chemoresistance of A549-Taxol cells were identified in this study for the first time. The results presented here would provide clues to identify new serologic chemoresistant biomarkers of NSCLC

Survey of Automatic Labeling Methods for Topic Models

Topic models are often used in modeling unstructured corpora and discrete data to extract the latent topic. As topics are generally expressed in the form of word lists, it is usually difficult for users to understand the meanings of topics, especially when users lack knowledge in the subject area. Although manually labeling topics can generate more explanatory and easily understandable topic labels, the cost is too high for the method to be feasible. Therefore, research on automatic labeling of topic discovered provides solutions to the problem. Firstly, the currently most popular technique, latent Dirichlet allocation (LDA), is elaborated and analyzed. According to the three different representations of topic labels, based on phrases, abstracts, and pictures, the topic labeling methods are classified into three types. Then, centered on improving the interpretability of topics, with different types of generated topic labels utilized, the relevant research in recent years is sorted out, analyzed, and summarized. The applicable scenarios and usability of different labels are also discussed. Meanwhile, methods are further categorized according to their different characteristics. The focus is placed on the quantitative and qualitative analysis of the abstract topic labels generated through lexical-based, submodular optimization, and graph-based methods. The differences between separate methods with respect to the learning types, technologies used, and data sources are then compared. Finally, the existing problems and trend of development of research on automatic topic labeling are discussed. Based on deep learning, integrating with sentiment analysis, and continuously expanding the applicable scenarios of topic labeling, will be the directions of future development

SUPRASPECIFIC TAXA OF THE BIVALVIA FIRST NAMED, DESCRIBED, AND PUBLISHED IN CHINA (1927–2007)

A total of 209 bivalve generic (subgeneric) and 19 familial (subfamilial) names first proposed by Chinese palaeontologists and published in China are treated herein as an annotated database. The present paper is designed especially for the Treatise on Invertebrate Paleontology Bivalvia revision project, because access to bivalve taxa published by Chinese authors in China has been difficult for non-Chinese researchers. The original diagnoses of these taxa, including the original descriptions and explanation of figures of all the type species, have been translated from Chinese into English, so that non-Chinese colleagues can more easily have access to them

GSK3 Inhibitor-BIO Regulates Proliferation of Immortalized Pancreatic Mesenchymal Stem Cells (iPMSCs)

<div><h3>Background</h3><p>The small molecule 6-bromoindirubin-30-oxime (BIO), a glycogen synthase kinase 3 (GSK3) inhibitor, is a pharmacological agent known to maintain self-renewal in human and mouse embryonic stem cells (ESCs). However, the precise role of GSK3 in immortalized pancreatic mesenchymal stem cells (iPMSCs) growth and survival is not completely understood at present.</p> <h3>Results</h3><p>To determine whether this molecule is involved in controlling the proliferation of iPMSCs, we examined the effect of BIO on iPMSCs. We found that the inactivation of GSK3 by BIO can robustly stimulate iPMSCs proliferation and mass formation as shown by QRT-PCR, western blotting, 5-Bromo-2-deoxyuridine (BrdU) immunostaining assay and tunel assay. However, we did not find the related roles of BIO on β cell differentiation by immunostaining, QRT-PCR assay, glucose-stimulated insulin release and C-peptide content analysis.</p> <h3>Conclusions</h3><p>These results suggest that BIO plays a key role in the regulation of cell mass proliferation and maintenance of the undifferentiated state of iPMSCs.</p> </div

Metabolic reprogramming in nodules, roots and leaves of symbiotic soybean in response to iron deficiency

To elucidate the mechanism of adaptation of leguminous plants to iron (Fe)‐deficient environment, comprehensive analyses of soybean (Glycine max) plants (sampled at anthesis) were conducted under Fe‐sufficient control and Fe‐deficient treatment using metabolomic and physiological approach. Our results show that soybeans grown under Fe‐deficient conditions showed lower nitrogen (N) fixation efficiency, however, ureides increased in different tissues, indicating potential N feedback inhibition. N assimilation was inhibited as observed in the repressed amino acids biosynthesis and reduced proteins in roots and nodules. In Fe‐deficient leaves, many amino acids increased, accompanied by the reduction of malate, fumarate, succinate and α‐ketoglutarate, which implies the N reprogramming was stimulated by the anaplerotic pathway. Accordingly, many organic acids increased in roots and nodules, however, enzymes involved in the related metabolic pathway (e.g., Krebs cycle) showed opposite activity between roots and nodules, indicative of different mechanisms. Sugars increased or maintained at constant level in different tissues under Fe deficiency, which probably relates to oxidative stress, cell wall damage and feedback regulation. Increased ascorbate, nicotinate, raffinose, galactinol and proline in different tissues possibly helped resist the oxidative stress induced by Fe deficiency. Overall, Fe deficiency induced the coordinated metabolic reprogramming in different tissues of symbiotic soybean plants

Establishment and verification a nomogram for predicting portal vein thrombosis presence among admitted cirrhotic patients

BackgroundPortal vein thrombosis (PVT) is an increasingly recognized complication of cirrhosis and possibly associated with mortality. This study aims to evaluate provoking factors for PVT, then establish a concise and efficient nomogram for predicting PVT presence among admitted cirrhotic patients.Materials and methodsAll cirrhotic patients admitted in Hunan Provincial People's Hospital between January 2010 and September 2020 were retrospectively reviewed, the clinical and laboratory data were collected. Multivariate logistic regression analysis and the least absolute shrinkage and selection operator regression method were used for screening the independent predictors and constructing the nomogram. The calibration curve was plotted to evaluate the consistent degree between observed outcomes and predicted probabilities. The area under the receiver operating characteristics curve was used to assess the discriminant performance. The decision curve analysis (DCA) was carried out to evaluate the benefits of nomogram.ResultsA total of 4,479 patients with cirrhosis were enrolled and 281 patients were identified with PVT. Smoking history, splenomegaly, esophagogastric varices, surgical history, red blood cell transfusion, and D-dimer were independent risk factors for PVT in cirrhosis. A nomogram was established with a good discrimination capacity and predictive efficiency with an the area under the curve (AUC) of 0.704 (95% CI: 0.664–0.745) in the training set and 0.685 (95% CI: 0.615–0.754) in the validation set. DCA suggested the net benefit of nomogram had a superior risk threshold probability.ConclusionA concise and efficient nomogram was established with good performance, which may aid clinical decision making and guide best treatment measures