Artificial intelligence and machine learning are rapidly expanding fields with increasing relevance in anesthesia and, in particular, airway management. The ability of artificial intelligence and machine learning algorithms to recognize patterns from large volumes of complex data makes them attractive for use in pediatric anesthesia airway management. The purpose of this review is to introduce artificial intelligence, machine learning, and deep learning to the pediatric anesthesiologist.
View Article and Find Full Text PDFBackground: Millions of patients are treated with opioid analgesics (OpAs) to relieve pain. Unfortunately, these medications are subject to abuse and/or unintended misuse. Abuse deterrent formulations (ADFs) represent an intervention strategy to decrease abuse/misuse without affecting patient access.
View Article and Find Full Text PDFBackground: In an effort to address the continuing problem of prescription opioid abuse, manufacturers are incorporating new technologies into formulations that are designed to deter product tampering and misuse. Standards for laboratory assessment of tamper deterrent properties of new formulations have not previously been developed.
Methods: Experimental designs were developed for the in vitro laboratory assessment of the tamper deterrent properties of reformulated oxycodone.
Early intracellular development in vitro of the cyst-forming protozoon Sarcocystis singaporensis and the influence of a monoclonal antibody on invasion, intracellular localization, and development of sporozoites were studied. As revealed by immunofluorescence using parasite-specific antibodies which labeled the parasitophorous vacuole membrane (PVM) and by ultrastructural analysis, sporozoites invaded pneumonocytes of the rat via formation of a parasitophorous vacuole (PV). About half of the sporozoites left this compartment within the first 8 h postinfection to enter the host cell cytosol.
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