Comparative Analysis of Polystyrene versus Zirconia Beads on Breakage Kinetics, Heat Generation, and Amorphous Formation during Wet Bead Milling.

This study determined process conditions under which polystyrene (CPS) and zirconia (YSZ) beads cause similar breakage kinetics and temperature rise during manufacturing of drug nanosuspensions via wet bead milling and explored relative advantages of CPS beads, particularly for stress-sensitive compounds. Besides temperature and particle size measurements, a microhydrodynamic-based kinetic model simulated the conditions for CPS to achieve breakage rates equivalent to those of YSZ. A power law correlation was applied to find conditions conducive to temperature equivalency.

Exploring the relationship between bulk Young's Modulus of materials and milling efficiency during wet bead milling of pharmaceutical compounds.

Wet bead milling (WBM) is one of the main approaches for manufacturing long acting injectable (LAI) suspensions, wherein the particle size of an Active Pharmaceutical Ingredient (API) is reduced in a liquid vehicle via grinding. A common challenge observed during WBM is long milling time to achieve target particle size, resulting in poor milling efficiency. The objective of this work was to identify potential API attributes predictive of milling efficiency during WBM.

Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images.

Microscopy-based spatially resolved omic methods are transforming the life sciences. However, these methods rely on high numerical aperture objectives and cannot resolve crowded molecular targets, limiting the amount of extractable biological information. To overcome these limitations, here we develop Deconwolf, an open-source, user-friendly software for high-performance deconvolution of widefield fluorescence microscopy images, which efficiently runs on laptop computers.

Development of a Semi-Mechanistic Modeling Framework for Wet Bead Milling of Pharmaceutical Nanosuspensions.

This study aimed to develop a practical semi-mechanistic modeling framework to predict particle size evolution during wet bead milling of pharmaceutical nanosuspensions over a wide range of process conditions and milling scales. The model incorporates process parameters, formulation parameters, and equipment-specific parameters such as rotor speed, bead type, bead size, bead loading, active pharmaceutical ingredient (API) mass, temperature, API loading, maximum bead volume, blade diameter, distance between blade and wall, and an efficiency parameter. The characteristic particle size quantiles, i.

Using Artificial Intelligence to Stratify Normal versus Abnormal Chest X-rays: External Validation of a Deep Learning Algorithm at East Kent Hospitals University NHS Foundation Trust.

The chest radiograph (CXR) is the most frequently performed radiological examination worldwide. The increasing volume of CXRs performed in hospitals causes reporting backlogs and increased waiting times for patients, potentially compromising timely clinical intervention and patient safety. Implementing computer-aided detection (CAD) artificial intelligence (AI) algorithms capable of accurate and rapid CXR reporting could help address such limitations.