Machine learning for predicting opioid use disorder from healthcare data: A systematic review.

Introduction: The US opioid epidemic has been one of the leading causes of injury-related deaths according to the CDC Injury Center. The increasing availability of data and tools for machine learning (ML) resulted in more researchers creating datasets and models to help analyze and mitigate the crisis. This review investigates peer-reviewed journal papers that applied ML models to predict opioid use disorder (OUD).

Rapid deployment of inexpensive open-source orbital shakers in support of high-throughput screening.

Open-source projects continue to grow in popularity alongside open-source educational resources, software, and hardware tools. The impact of this increased availability of open-source technologies is that end users are empowered to have greater control over the tools that they work with. This trend extends in the life science laboratory space, where new open-source projects are routinely being published that allow users to build and modify scientific equipment specifically tailored to their needs, often at a reduced cost from equivalent commercial offerings.

Automating a Magnetic 3D Spheroid Model Technology for High-Throughput Screening.

Affordable and physiologically relevant three-dimensional (3D) cell-based assays used in high-throughput screening (HTS) are on the rise in early drug discovery. These technologies have been aided by the recent adaptation of novel microplate treatments and spheroid culturing techniques. One such technology involves the use of nanoparticle (NanoShuttle-PL) labeled cells and custom magnetic drives to assist in cell aggregation to ensure rapid 3D structure formation after the cells have been dispensed into microtiter plates.