The Society for Laboratory Automation and Screening (SLAS) recently held their second Sample Management Symposium in Boston, MA. I was there on behalf of GA International to cover some of the new trends in sample management being implemented in biotech and pharmaceutical companies across North America.
Working in the lab can be both creatively inspiring, allowing you the ultimate freedom to plan, execute, and test your own experimental theories, and extraordinarily restrictive. Lab space is always an issue, especially for small labs that use large equipment, such as HPLC machines and mass spectrometers.
Artificial intelligence (AI) is one of the fastest growing new technologies of the last couple of years. As computing power has increased, so has the potential for using AI in the clinic, with researchers focusing on its role in solving complex biological problems and making healthcare more effective and efficient. AI isn’t an entirely new technology in histology and pathology; dating back to 1992, attempts have been made at introducing some basic form of AI, such as the PATHFINDER system for hematopathology diagnosis.1
Many labs are inundated with a high volume of samples on a regular basis. Pharmaceutical and biotechnology labs can process thousands of samples daily, while medical labs are regularly bombarded with patient specimens. Because tube labeling can take up a significant portion of your day, having a system in place that can streamline the process is a necessity. For large labs, that includes having an automated labeling system.
Histology has evolved considerably since its beginnings in the 17th century, with advances in both specimen processing and analysis. Consequently, histology departments now face increasingly larger workloads. To adapt, they have integrated automated systems, which save time and allow histology professionals to work on other skill-based tasks, while maintaining enough flexibility to process and stain according to the needs of the medical or research lab. Here, we’ll explore how automation has been integrated into histology to speed up the workflow of both medical technicians and researchers.