Southwest University

About: Southwest University is a education organization based out in Chongqing, China. It is known for research contribution in the topics: Gene & Population. The organization has 29772 authors who have published 27755 publications receiving 409441 citations. The organization is also known as: Southwest University in Chongqing & SWU.

Multiplexed and Amplified Electronic Sensor for the Detection of MicroRNAs from Cancer Cells

Abstract: The detection of microRNA expression profiles plays an important role in early diagnosis of different cancers. On the basis of the employment of redox labels with distinct potential positions and duplex specific nuclease (DSN)-assisted target recycling signal amplifications, we have developed a multiplexed and convenient electronic sensor for highly sensitive detection of microRNA (miRNA)-141 and miRNA-21. The sensor is constructed by self-assembly of thiol-modified, redox species-labeled hairpin probes on the gold sensing electrode. The hybridizations between the target miRNAs and the surface-immobilized probes lead to the formation of RNA/DNA duplexes, and DSN subsequently cleaves the redox-labeled hairpin probes of the RNA/DNA duplexes to recycle the target miRNAs and to generate significantly amplified current suppression at different potentials for multiplexed detection of miRNA-141 and miRNA-21 down to 4.2 and 3.0 fM, respectively. The sensor is also highly selective toward the target miRNAs and can...

A Machine Learning Methodology for Diagnosing Chronic Kidney Disease

Abstract: Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. Since there are no obvious symptoms during the early stages of CKD, patients often fail to notice the disease. Early detection of CKD enables patients to receive timely treatment to ameliorate the progression of this disease. Machine learning models can effectively aid clinicians achieve this goal due to their fast and accurate recognition performance. In this study, we propose a machine learning methodology for diagnosing CKD. The CKD data set was obtained from the University of California Irvine (UCI) machine learning repository, which has a large number of missing values. KNN imputation was used to fill in the missing values, which selects several complete samples with the most similar measurements to process the missing data for each incomplete sample. Missing values are usually seen in real-life medical situations because patients may miss some measurements for various reasons. After effectively filling out the incomplete data set, six machine learning algorithms (logistic regression, random forest, support vector machine, k-nearest neighbor, naive Bayes classifier and feed forward neural network) were used to establish models. Among these machine learning models, random forest achieved the best performance with 99.75% diagnosis accuracy. By analyzing the misjudgments generated by the established models, we proposed an integrated model that combines logistic regression and random forest by using perceptron, which could achieve an average accuracy of 99.83% after ten times of simulation. Hence, we speculated that this methodology could be applicable to more complicated clinical data for disease diagnosis.

Active Targeting of Tumors through Conformational Epitope Imprinting

Abstract: Inspired by the knowledge that most antibodies recognize a conformational epitope because of the epitope’s specific three-dimensional shape rather than its linear structure, we combined scaffold-based peptide design and surface molecular imprinting to fabricate a novel nanocarrier harboring stable binding sites that captures a membrane protein. In this study, a disulfide-linked α-helix-containing peptide, apamin, was used to mimic the extracellular, structured N-terminal part of the protein p32 and then serve as an imprinting template for generating a sub-40 nm-sized polymeric nanoparticle that potently binds to the target protein, recognizes p32-positive tumor cells, and successfully mediates targeted photodynamic therapy in vivo. This could provide a promising alternative for currently used peptide-modified nanocarriers and may have a broad impact on the development of polymeric nanoparticle-based therapies for a wide range of human diseases.

The Mediating Roles of Upward Social Comparison and Self-esteem and the Moderating Role of Social Comparison Orientation in the Association between Social Networking Site Usage and Subjective Well-Being.

Abstract: The increased pervasiveness of social media use has raised questions about potential effects on users’ subjective well-being, with studies reaching contrasting conclusions. To reconcile these discrepancies and shed new light on this phenomenon, the current study examined: (1) whether upward social comparison and self-esteem mediate the association between social networking site (SNS) usage and users’ subjective well-being, and (2) whether the association between SNS usage and upward social comparison is moderated by users’ social comparison orientation. Data from 696 participants were collected. Structural equation modeling revealed that upward social comparison and self-esteem mediated the relationship between SNS usage and users’ subjective well-being. We found that social comparison orientation moderated the association between passive SNS usage and users’ upward social comparison. Specifically, social comparison orientation strengthened the association between passive SNS usage and upward social comparison. The results might suggest a process through which passive SNS usage is related to subjective well-being, and identify a context under which these associations may differ.