Ether bond-modified lipid nanoparticles for enhancing the treatment effect of hepatic fibrosis.

Lipid nanoparticle (LNP)-mediated RNA delivery holds significant potential for the treatment of various liver diseases. Ionizable lipids play a crucial role in the formulation of LNPs and directly influence their delivery efficiency. In this study, we introduced an innovative concept by incorporating an ether bond into the hydrophobic tail of ionizable lipids for the first time.

Robust collection and processing for label-free single voxel proteomics.

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk tissues. However, such bulk measurement lacks spatial resolution and obscures tissue heterogeneity, precluding proteome mapping of tissue microenvironment. Here we report an integrated wet collection of single microscale tissue voxels and Surfactant-assisted One-Pot voxel processing method termed wcSOP for robust label-free single voxel proteomics.

Association of Adverse Perinatal Outcomes with Blood Components Transfusion in Patients with Acute Fatty Liver of Pregnancy.

Purpose: To investigate the rare obstetric emergency with no specific treatments called acute fatty liver of pregnancy. The primary objective was to evaluate association of adverse perinatal outcomes with blood components transfusion. While the secondary objective focused on further establishing the predictive risk factors for adverse perinatal outcomes.

Bupivacaine multivesicular liposomes/meloxicam nanocrystals in a thermosensitive gel adapted to the "microenvironment" for long-term analgesia.

Current analgesics on the market exhibit a short duration of action and induce the production of inflammatory factors in tissues damaged by surgical procedures. Inflammatory factor production can create acidic environments, limiting drug delivery. In this study, we developed a novel injectable formulation comprising bupivacaine multivesicular liposomes of high osmotic pressure (H-MVL) and meloxicam nanocrystals (MLX) in a thermosensitive gel (H-MVL/MLX@GEL) adapted to the microenvironment for long-term postoperative analgesia.

Assessing the feasibility and external validity of natural language processing-extracted data for advanced lung cancer patients.

Background: Manual extraction of real-world clinical data for research can be time-consuming and prone to error. We assessed the feasibility of using natural language processing (NLP), an AI technique, to automate data extraction for patients with advanced lung cancer (aLC). We assessed the external validity of our NLP-extracted data by comparing our findings to those reported in the literature.