Neurobiology of L-proline: From molecules to behavior.

L-proline is an amino acid with a unique cyclic structure, involvement in various physiological processes, such as protein synthesis, collagen production, and neurotransmission. This review explores the complex roles of proline in the central nervous system (CNS), where it contributes to both excitatory and inhibitory neurotransmission. Additionally, L-proline has distinct metabolic functions attributed to its structural properties.

Agathisflavone Inhibits Viability and Modulates the Expression of miR-125b, miR-155, IL-6, and Arginase in Glioblastoma Cells and Microglia/Macrophage Activation.

Glioblastomas (GBM) are malignant tumours with poor prognosis. Treatment involves chemotherapy and/or radiotherapy; however, there is currently no standard treatment for recurrence, and prognosis remains unfavourable. Inflammatory mediators and microRNAs (miRNAs) influence the aggressiveness of GBM, being involved in the communication with the cells of the tumour parenchyma, including microglia/macrophages, and maintaining an immunosuppressive microenvironment.

Injectable biocompatible hydrogels with tunable strength based on crosslinked supramolecular polymer nanofibers.

Hydrogels based on supramolecular assemblies offer attractive features for biomedical applications including injectability or versatile combinations of various building blocks. We here investigate a system combining benzenetrispeptides (BTP), which forms supramolecular fibers, with polymer polyethylene oxide (PEO) forming a dense hydrophilic shell around the fibers. Hydrogels are created through the addition of a bifunctional crosslinker (CL).

Assessing Patient Health Dynamics by Comparative CT Analysis: An Automatic Approach to Organ and Body Feature Evaluation.

The integration of machine learning into the domain of radiomics has revolutionized the approach to personalized medicine, particularly in oncology. Our research presents RadTA (RADiomics Trend Analysis), a novel framework developed to facilitate the automatic analysis of quantitative imaging biomarkers (QIBs) from time-series CT volumes. RadTA is designed to bridge a technical gap for medical experts and enable sophisticated radiomic analyses without deep learning expertise.