Background Neuromuscular blocking agents are crucial for anesthesia but can cause reversible paralysis, leading to risks like postoperative residual dysfunction. Undetected paralysis in the post-anesthesia care unit (PACU) jeopardizes patient safety by impairing airway function and increasing complications. Effective reversal, assessed clinically or via nerve stimulation, is critical to prevent residual postoperative curarization (RPOC), which is linked to significant morbidity and mortality.
View Article and Find Full Text PDFBackground Spinal dysraphism, characterized by incomplete closure of neural and bone spinal structures, manifests as congenital fusion abnormalities along the dorsal midline, involving the skin, subcutaneous tissue, meninges, vertebrae, and neural tissue. Magnetic resonance imaging (MRI), the preferred imaging modality for assessing spinal dysraphism across all age groups, provides direct visualization of the spinal cord without the need for contrast or ionizing radiation while also eliminating bone artifacts and allowing multiplanar imaging. The objective of this study was to evaluate the range of spinal dysraphism lesions and assess the significance of MRI in their evaluation.
View Article and Find Full Text PDFBackground A major development in noninvasive imaging modalities, computed tomographic enterography (CTE) has a number of benefits over conventional computed tomography (CT) and capsule endoscopy. Through the utilization of multidetector computed tomography (MDCT) technology, CTE expedites the assessment of small bowel diseases, especially in those segments that are not accessible through traditional endoscopy. This study's main goal is to thoroughly evaluate CTE's diagnostic accuracy for a range of small intestinal conditions.
View Article and Find Full Text PDFBackground: The use of tools that allow estimation of the probability of progression of chronic kidney disease (CKD) to advanced stages has not yet achieved significant practical importance in clinical setting. This study aimed to develop and validate a machine learning-based model for predicting the need for renal replacement therapy (RRT) and disease progression for patients with stage 3-5 CKD.
Methods: This was a retrospective, closed cohort, observational study.