Membrane protein degradation is a cutting-edge field in targeted protein degradation (TPD). Herein, we developed glypican-3 (GPC3)-mediated lysosome-targeting chimeras (GLTACs) as a novel strategy for the targeted degradation of tumor-specific membrane proteins. GLTACs utilize tumor-specific expression and endocytosis properties of GPC3 to degrade membrane proteins. By conjugating a GPC3-targeting peptide with the ligand of protein of interest (POI), GLTACs induce the formation of a ternary complex that is internalized into lysosomes, leading to the degradation of the POI. The effectiveness and specificity of GLTACs were validated by designing PD-L1, c-Met, and FGFR1 degraders. In particular, GLTAC WP0 potently degraded PD-L1 and induced T-cell-mediated tumor killing against HepG2 cells, highlighting the potential therapeutic applications. The development of GLTAC technology expands the scope of TPD strategies and opens new avenues for discovering novel therapeutic modalities against challenging protein targets.

In this study, araucarene diterpenes, characterized by a pimarene skeleton with a variably oxidized side chain at C-13, were investigated. A total of 16 araucarene diterpenoids and their derivatives were isolated from the woods of Agathis dammara, including 11 previously unreported compounds: dammaradione (1), dammarones D-G (2, 5, 14, 15), dammaric acids B-F (8-12), and dammarol (16). The structures of these new compounds were elucidated using high-resolution electrospray ionization mass spectroscopy (HR-ESI-MS) and one-dimensional/two-dimensional (1D/2D) nuclear magnetic resonance (NMR), while their absolute configurations were determined through the electronic circular dichroism (ECD) exciton chirality method and Snatzke's method. The hypoglycemic activity of all isolated compounds was evaluated using a transgenic zebrafish model, and a structure-activity relationship (SAR) analysis was conducted. Araucarone (3) and dammaric acid C (9), serving as representative compounds, demonstrated significant hypoglycemic effects on zebrafish. The primary mechanism involves the promotion of pancreatic β cell regeneration and glucose uptake. Specifically, these compounds enhance the differentiation of pancreatic endocrine precursor cells (PEP cells) into β cells in zebrafish.
Zebrafish ; Animals ; Diterpenes/isolation & purification* ; Insulin-Secreting Cells/cytology* ; Glucose/metabolism* ; Hypoglycemic Agents/isolation & purification* ; Molecular Structure ; Structure-Activity Relationship ; Plant Extracts/pharmacology* ; Regeneration/drug effects*

OBJECTIVE To compare the contents of schizandrin A, schizandrin B, schizandrin C, schisandrol A, schisandrol B, schisantherin A, anwuligan, and schisanhenol in Schisandrae Sphenantherae Fructus on market from 12 habitats. METHODS The samples were pre-treated by 96-well fitration plates. The assay was performed on ACE EXCEL 1.7 C18-AMIDE(100 mm×2.1 mm, 1.7 μm) column with 0.1% formic acid-water(A) and methanol(B), gradient elution, flow speed was 0.4 mL·min–1. Ion source was electric spray ion source, positive ion monitoring mode, multireaction monitoring mode for quantitative analysis. Linear, instrument precision, stability, repeatability, average recovery were investigated. RESULTS The content of schisantherin A in 10 of 12 producing areas reached the standard of ≥0.2% of Schisandrae Sphenantherae Fructus in 2020 Edition of Chinese Pharmacopoeia. CONCLUSION UHPLC-MS/MS is suitable for simultaneous determination of multiple components in Schisandrae Sphenantherae Fructus. The Schisandrae Sphenantherae Fructus in the market basically meet the national legal standards.

Artificial Intelligence (AI) is an interdisciplinary subject developed on the basis of computer technology, cybernetics, mathematics, philosophy and brain science. The purpose of AI is to study new ways to extend the intelligence of human brain in various fields. In recent years, the rapid development of AI technology has brought innovation to medical science and health care. During the pandemic of coronavirus disease 2019 (COVID-19) AI has been widely used in epidemiological investigation and outbreak prediction, clinical diagnosis and treatment, hospital management, research and development of new drugs and vaccines. The application of AI has reduced the clinical workload and the consumption of medical resources, greatly assisted the battle against COVID-19.This article introduces the progresses on the applications of AI technology to provide information for its further application in the fighting against COVID-19.