PAG orchestrates T cell immune synapse function by binding to actin.

Unlabelled: Many immunotherapies impact T cell function by impacting the immune synapse. While immunotherapy is extremely successful in some patients, in many others, it fails to help or causes complications, including immune-related adverse events. Phosphoprotein Associated with Glycosphingolipid Rich Microdomains 1 (PAG) is a transmembrane scaffold protein with importance in T cell signaling.

A method combining LDA and neural networks for antitumor drug efficacy prediction.

Background: Personalized medicine has gained more attention for cancer precision treatment due to patient genetic heterogeneity in recent years. However, predicting the efficacy of antitumor drugs in advance remains a significant challenge to achieve this task.

Objective: This study aims to predict the efficacy of antitumor drugs in individual cancer patients based on clinical data.

Learning the cellular activity representation based on gene regulatory networks for prediction of tumor response to drugs.

Predicting the response of tumor cells to anti-tumor drugs is critical to realizing cancer precision medicine. Currently, most existing methods ignore the regulatory relationships between genes and thus have unsatisfactory predictive performance. In this paper, we propose to predict anti-tumor drug efficacy via learning the activity representation of tumor cells based on a priori knowledge of gene regulation networks (GRNs).

Personalized anti-tumor drug efficacy prediction based on clinical data.

Anti-tumor drug efficacy prediction poses an unprecedented challenge to realizing personalized medicine. This paper proposes to predict personalized anti-tumor drug efficacy based on clinical data. Specifically, we encode the clinical text as numeric vectors featured with hidden topics for patients using Latent Dirichlet Allocation model.

Effect of Torrefaction on the Physiochemical Characteristics and Pyrolysis of the Corn Stalk.

Torrefaction of biomass is one of the most promising pretreatment methods for deriving biofuels from biomass via thermochemical conversion processes. In this work, the changes in physicochemical properties and morphology features of the torrefied corn stalk, the changes in physicochemical properties and morphology features of the torrefied corn stalk were investigated. The results of this study showed that the elemental content and proximate analysis of the torrefied corn stalk significantly changed compared with those of the raw corn stalk.

Proteomics Analysis of Serum Proteins in Gestational Diabetes.

Objective: The purpose of this study was to screen serum proteins for biomarkers of gestational diabetes mellitus (GDM) and to investigate its pathogenesis by analyzing the differences in serum proteomics between pregnant women with GDM and healthy pregnant women.

Methods: Patients who were admitted to the First Affiliated Hospital of Fujian Medical University from June 2019 to January 2020 were included. According to the medical history and the results of the 75 g oral glucose tolerance test (OGTT), they were divided into the normal pregnant women group and GDM pregnant women group.

Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data.

Background: Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner.

Results: This paper proposes a global GRNs inference framework based on dictionary learning, named dlGRN.

miR-221 regulates proliferation and apoptosis of ovarian cancer cells by targeting BMF.

To observe the expression of microRNA-221 (miR-221) in ovarian cancer tissues and its effect and associated mechanism on proliferation and apoptosis in the ovarian cancer SKOV3 cell line. The expression of miR-221 and B-cell lymphoma 2 modifying factor (BMF) mRNA in ovarian cancer and para-carcinoma tissues was detected by reverse transcription-quantitative polymerase chain reaction, the expression of BMF was detected by western blot. MicroRNA.

Adaptively capturing the heterogeneity of expression for cancer biomarker identification.

Background: Identifying cancer biomarkers from transcriptomics data is of importance to cancer research. However, transcriptomics data are often complex and heterogeneous, which complicates the identification of cancer biomarkers in practice. Currently, the heterogeneity still remains a challenge for detecting subtle but consistent changes of gene expression in cancer cells.

DynSig: Modelling Dynamic Signaling Alterations along Gene Pathways for Identifying Differential Pathways.

Although a number of methods have been proposed for identifying differentially expressed pathways (DEPs), few efforts consider the dynamic components of pathway networks, i.e., gene links.

Clinical significance of Twist, E-cadherin, and N-cadherin protein expression in endometrioid adenocarcinoma.

Objective: The aim of the study was to investigate the expression of Twist, E-cadherin, and N-cadherin both in normal endometrium and in endometrioid adenocarcinoma tissues (NET and EAT), and further discuss the relationship between the proteins expression and the clinical parameters.

Methods: Seventy-six EAT and 50 NET were collected from endometrioid adenocarcinoma patients and patients who received hysterectomy. We used immunohistochemistry (two steps methods) to detect the expression of Twist, E-cadherin, and N-cadherin proteins in EAT and NET.

A regulation probability model-based meta-analysis of multiple transcriptomics data sets for cancer biomarker identification.

Background: Large-scale accumulation of omics data poses a pressing challenge of integrative analysis of multiple data sets in bioinformatics. An open question of such integrative analysis is how to pinpoint consistent but subtle gene activity patterns across studies. Study heterogeneity needs to be addressed carefully for this goal.

ctPath: Demixing pathway crosstalk effect from transcriptomics data for differential pathway identification.

Identifying differentially expressed pathways (DEPs) plays important roles in understanding tumor etiology and promoting clinical treatment of cancer or other diseases. By assuming gene expression to be a sparse non-negative linear combination of hidden pathway signals, we propose a pathway crosstalk-based transcriptomics data analysis method (ctPath) for identifying differentially expressed pathways. Biologically, pathways of different functions work in concert at the systematic level.