Chitosan-Functionalized Fluorescent Calcium Carbonate Nanoparticle Loaded with Methotrexate: Future Theranostics for Triple Negative Breast Cancer.

Herein, fluorescent calcium carbonate nanoclusters encapsulated with methotrexate (Mtx) and surface functionalized with chitosan (25 nm) (@Calmat) have been developed for the imaging and treatment of triple-negative breast cancer (TNBC). These biocompatible, pH-sensitive nanoparticles demonstrate significant potential for targeted therapy and diagnostic applications. The efficacy of nanoparticles (NPs) was evaluated in MDA-MB-231 TNBC cell lines.

Examining priorities and investments made through the Global Financing Facility for maternal and newborn health: a sub-analysis on quality.

Improving quality of care could avert most of the 4.5 million maternal and neonatal deaths and stillbirths that occur each year. The Global Financing Facility (GFF) aims to catalyse the national scale-up of maternal and newborn health (MNH) interventions through focused investments.

Simultaneous Unbinned Differential Cross-Section Measurement of Twenty-Four Z+jets Kinematic Observables with the ATLAS Detector.

Z boson events at the Large Hadron Collider can be selected with high purity and are sensitive to a diverse range of QCD phenomena. As a result, these events are often used to probe the nature of the strong force, improve Monte Carlo event generators, and search for deviations from standard model predictions. All previous measurements of Z boson production characterize the event properties using a small number of observables and present the results as differential cross sections in predetermined bins.

Exploring Pyridine-Based Schemes: A Comprehensive Review on their Synthesis and Therapeutic Applications.

Pyridine and its derivatives are six-membered aromatic rings containing nitrogen, which are abundant in nature and indispensable in studying heterocyclic chemistry. They constitute significant chemical substances with numerous applications. The application of pyridine derivatives by incorporating metals in modern medicine is growing in relevance.

Generalizable deep learning approach for 3D particle imaging using holographic microscopy (HM).

Despite its potential for label-free particle diagnostics, holographic microscopy is limited by specialized processing methods that struggle to generalize across diverse settings. We introduce a deep learning architecture leveraging human perception of longitudinal variation of diffracted patterns of particles, which enables highly generalizable analysis of 3D particle information with orders of magnitude improvement in processing speed. Trained with minimal synthetic and real holograms of simple particles, our method demonstrates exceptional performance across various challenging cases, including high particle concentrations, significant noise, and a wide range of particle sizes, complex shapes, and optical properties, exceeding the diversity of training datasets.