The SRS is easy to use, formulate and interpret, and provides an accurate prediction of death in general surgical patients across the entire risk spectrum.

No abstract

Objective. The major human Fc receptor, Fc␥RIIa, is the most widespread activating FcR. Our aim was to determine the role of Fc␥RIIa in a transgenic mouse model of immune complex-mediated autoimmunity and to characterize the development of spontaneous autoimmune disease. Methods. Arthritis was induced in normal and

The SRS accurately predicted mortality in higher-risk surgical patients. The accuracy of prediction equalled that of POSSUM and p-POSSUM.

Artificial intelligence is a rapidly evolving field, with modern technological advances and the growth of electronic health data opening new possibilities in diagnostic radiology. In recent years, the performance of deep learning (DL) algorithms on various medical image tasks have continually improved. DL algorithms have been proposed as a tool to detect various forms of intracranial hemorrhage on non-contrast computed tomography (NCCT) of the head. In subtle, acute cases, the capacity for DL algorithm image interpretation support might improve the diagnostic yield of CT for detection of this time-critical condition, potentially expediting treatment where appropriate and improving patient outcomes. However, there are multiple challenges to DL algorithm implementation, such as the relative scarcity of labeled datasets, the difficulties in developing algorithms capable of volumetric medical image analysis, and the complex practicalities of deployment into clinical practice. This review examines the literature and the approaches taken in the development of DL algorithms for the detection of intracranial hemorrhage on NCCT head studies. Considerations in crafting such algorithms will be discussed, as well as challenges which must be overcome to ensure effective, dependable implementations as automated tools in a clinical setting.