Glucose-insulin-potassium (GIK) is protective following cardiac myocyte ischaemia-reperfusion (IR) injury, however the role of GIK in protecting skeletal muscle from IR injury has not been evaluated. Given the similar mechanisms by which cardiac and skeletal muscle sustain an IR injury, we hypothesized that GIK would similarly protect skeletal muscle viability. A total of 20 C57BL/6 male mice (10 control, 10 GIK) sustained a hindlimb IR injury using a 2.5-hour rubber band tourniquet. Immediately prior to tourniquet placement, a subcutaneous osmotic pump was placed which infused control mice with saline (0.9% sodium chloride) and treated mice with GIK (40% glucose, 50 U/l insulin, 80 mEq/L KCl, pH 4.5) at a rate of 16 µl/hr for 26.5 hours. At 24 hours following tourniquet removal, bilateral (tourniqueted and non-tourniqueted) gastrocnemius muscles were triphenyltetrazolium chloride (TTC)-stained to quantify percentage muscle viability. Bilateral peroneal muscles were used for gene expression analysis, serum creatinine and creatine kinase activity were measured, and a validated murine ethogram was used to quantify pain before euthanasia.Aims
Methods
Orthopaedic surgery uses many varied instruments with high-speed, high-impact, thermal energy and sometimes heavy instruments, all of which potentially result in aerosolization of contaminated blood, tissue, and bone, raising concerns for clinicians’ health. This study quantifies the aerosol exposure by measuring the number and size distribution of the particles reaching the lead surgeon during key orthopaedic operations. The aerosol yield from 17 orthopaedic open surgeries (on the knee, hip, and shoulder) was recorded at the position of the lead surgeon using an Aerodynamic Particle Sizer (APS; 0.5 to 20 μm diameter particles) sampling at 1 s time resolution. Through timestamping, detected aerosol was attributed to specific procedures.Aims
Methods
The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction. Cite this article:
