Mechanism of miRNAs and miRNA-mRNA Regulatory Networks in Modulating Drug Resistance in HER2-Positive Breast Cancer: An Integrative Bioinformatics Approach.

Background: HER2-positive breast cancer is an aggressive subtype where innate/acquired resistance to targeted drugs remains a challenge. This study aims to uncover the underlying mechanisms of HER2 drug resistance through miRNA analysis and target identification.

Methods: MiRNA datasets were systematically retrieved from the GEO database, and differential expression analysis was conducted for both miRNA and mRNA datasets.

Piezoelectric GaGeX (X = N, P, and As) semiconductors with Raman activity and high carrier mobility for multifunctional applications: a first-principles simulation.

In the present work, we propose GaGeX (X = N, P, As) monolayers and explore their structural, vibrational, piezoelectric, electronic, and transport characteristics for multifunctional applications based on first-principles simulations. Our analyses of cohesive energy, phonon dispersion spectra, and molecular dynamics simulations indicate that the three proposed structures have good energetic, dynamic, and thermodynamic stabilities. The GaGeX are found as piezoelectric materials with high piezoelectric coefficient of -1.

Comtrifosides A and B, two new triterpenoid saponins from the leaves of .

The phytochemical composition of the leaves was studied for the first time. Two new triterpenoid saponins, named comtrifoside A () and comtrifoside B (), together with two other saponins () were purified by variously chromatographic techniques. For the first time, compound was informed from the genus, as well as all of the isolated compounds () were reported from .

miRNAs as biomarkers of therapeutic response to HER2-targeted treatment in breast cancer: A systematic review.

Breast cancer is the most common type of lethal cancer in women globally. Women have a 1 in 8 chance of developing breast cancer in their lifetime. Among the four primary molecular subtypes (luminal A, luminal B, HER2+, and triple-negative), HER2+ accounts for 20-25 % of all breast cancer and is rather aggressive.

Omics-based deep learning approaches for lung cancer decision-making and therapeutics development.

Lung cancer has been the most common and the leading cause of cancer deaths globally. Besides clinicopathological observations and traditional molecular tests, the advent of robust and scalable techniques for nucleic acid analysis has revolutionized biological research and medicinal practice in lung cancer treatment. In response to the demands for minimally invasive procedures and technology development over the past decade, many types of multi-omics data at various genome levels have been generated.

ALDH2 as a potential stem cell-related biomarker in lung adenocarcinoma: Comprehensive multi-omics analysis.

Lung adenocarcinoma (LUAD) is the most prevalent lung cancer and one of the leading causes of death. Previous research found a link between LUAD and Aldehyde Dehydrogenase 2 (ALDH2), a member of aldehyde dehydrogenase gene (ALDH) superfamily. In this study, we identified additional useful prognostic markers for early LUAD identification and targeting LUAD therapy by analyzing the expression level, epigenetic mechanism, and signaling activities of ALDH2 in LUAD patients.

The Distribution of Autoantibodies by Age Group in Children with Type 1 Diabetes versus Type 2 Diabetes in Southern Vietnam.

Asian children are increasingly being diagnosed with type 1 diabetes (T1D) or type 2 diabetes (T2D), and the presence of coexisting islet autoimmune antibodies complicate diagnosis. Here, we aimed to determine the prevalence of islet cell autoantibodies (ICAs) and glutamic acid decarboxylase 65 autoantibodies (GADAs) in children with T1D versus T2D living in Vietnam. This cross-sectional study included 145 pediatric patients aged 10.

On the road to explainable AI in drug-drug interactions prediction: A systematic review.

Over the past decade, polypharmacy instances have been common in multi-diseases treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected adverse drug events (ADEs) in multiple regimens therapy remain a significant issue. Since artificial intelligence (AI) is ubiquitous today, many AI prediction models have been developed to predict DDIs to support clinicians in pharmacotherapy-related decisions.