Astragaloside IV promotes exosome secretion of endothelial progenitor cells to regulate PI3KR2/SPRED1 signaling and inhibit pyroptosis of diabetic endothelial cells.

BACKGROUND AIMS: Treating chronic non-healing diabetic wounds and achieving complete skin regeneration has always been a critical clinical challenge. METHODS: In order to address this issue, researchers conducted a study aiming to investigate the role of miR-126-3p in regulating the downstream gene PIK3R2 and promoting diabetic wound repair in endothelial progenitor cell (EPC)-derived extracellular vesicles. The study involved culturing EPCs with astragaloside IV, transfecting them with miR-126-3p inhibitor or mock plasmid, interfering with high glucose-induced damage in human umbilical vein endothelial cells (HUVECs) and treating diabetic skin wounds in rats. RESULTS: The healing of rat skin wounds was observed through histological staining. The results revealed that treatment with miR-126-3p-overexpressing EPC-derived extracellular vesicles accelerated the healing of rat skin wounds and resulted in better tissue repair with slower scar formation. In addition, the transfer of EPC-derived extracellular vesicles with high expression of miR-126-3p to high glucose-damaged HUVECs increased their proliferation and invasion, reduced necrotic and apoptotic cell numbers and improved tube formation. In this process, the expression of angiogenic factors vascular endothelial growth factor (VEGF)A, VEGFB, VEGFC, basic fibroblast growth factor and Ang-1 significantly increased, whereas the expression of caspase-1, NRLP3, interleukin-1ß, inteleukin-18, PIK3R2 and SPRED1 was suppressed. Furthermore, miR-126-3p was able to target and inhibit the expression of the PIK3R2 gene, thereby restoring the proliferation and migration ability of high glucose-damaged HUVEC. CONCLUSIONS: In summary, these research findings demonstrate the important role of miR-126-3p in regulating downstream genes and promoting diabetic wound repair, providing a new approach for treating chronic non-healing diabetic wounds.

A Sensitive Liquid Chromatography-Mass Spectrometry Method for Determination of Toosendanin in Rat Plasma and its Application to Pharmacokinetic Study.

A simple, rapid and sensitive analytical method was developed for the determination of toosendanin in rat plasma using liquid chromatography tandem mass spectrometry (LC-MS/MS). Andrographolide was selected as the internal standard, and the plasma samples were extracted by liquid-liquid extraction with diethyl ether. Chromatographic separation was performed on a Dikma Spursil C18, 3.5 µm (150 × 2.1 mm i.d) analytical column with 85% methanol:water (v/v) containing 0.025% formic acid (pH = 3.9) as mobile phase. The flow rate was 0.25 mL/min, and the total run time was 3 min. Detection was performed with a triple-quadrupole tandem mass spectrometer using negative ion mode electrospray ionization (ESI) in the multiple reaction monitoring (MRM) mode. The MS/MS ion transitions monitored were m/z 573.1 â†’ 531.1 and 349.0 â†’ 287.0 for toosendanin and andrographolide, respectively. Good linearity was observed over the concentration range of 3.125-500 ng/mL in 100 µL of rat plasma with a correlation coefficient ˃0.9997. Intra- and inter-assay variabilities were ˂8.5% in plasma. The recovery and the matrix effect were in the range 71.8-73.5% and 96.4-103.8%, respectively. The analyte was stable under various conditions (at room temperature, during freeze-thaw settings, in the autosampler, and under deep-freeze conditions). The method was successfully applied to a pharmacokinetic study of toosendanin after its oral administration in rats at a dose of 10 mg/kg.

Extracting Concepts for Precision Oncology from the Biomedical Literature.

This paper describes an initial dataset and automatic natural language processing (NLP) method for extracting concepts related to precision oncology from biomedical research articles. We extract five concept types: Cancer, Mutation, Population, Treatment, Outcome. A corpus of 250 biomedical abstracts were annotated with these concepts following standard double-annotation procedures. We then experiment with BERT-based models for concept extraction. The best-performing model achieved a precision of 63.8%, a recall of 71.9%, and an F1 of 67.1. Finally, we propose additional directions for research for improving extraction performance and utilizing the NLP system in downstream precision oncology applications.

A Frame-Based NLP System for Cancer-Related Information Extraction.

We propose a frame-based natural language processing (NLP) method that extracts cancer-related information from clinical narratives. We focus on three frames: cancer diagnosis, cancer therapeutic procedure, and tumor description. We utilize a deep learning-based approach, bidirectional Long Short-term Memory (LSTM) Conditional Random Field (CRF), which uses both character and word embeddings. The system consists of two constituent sequence classifiers: a frame identification (lexical unit) classifier and a frame element classifier. The classifier achieves an F1 of 93.70 for cancer diagnosis, 96.33 for therapeutic procedure, and 87.18 for tumor description. These represent improvements of 10.72, 0.85, and 8.04 over a baseline heuristic, respectively. Additionally, we demonstrate that the combination of both GloVe and MIMIC-III embeddings has the best representational effect. Overall, this study demonstrates the effectiveness of deep learning methods to extract frame semantic information from clinical narratives.