Publications by authors named "T R Sanford"
The human microbiota, a community of microorganisms in our bodies, is crucial for our health. This paper explores its development from birth through old age, highlighting some of the unique roles at key life stages-infancy, adulthood, and in the elderly years. Understanding the significant health impacts and consequences of changes in the microbiota offers insights for both the public and clinicians.
View Article and Find Full Text PDFBackground: The 2022 study on diagnostic error in the emergency department (ED) published by the Agency for Healthcare Research and Quality (AHRQ) reported that one in every 18 ED patients is misdiagnosed. The report was methodologically critiqued by emergency physicians and researchers. However, little is known about public perception of error in the ED.
View Article and Find Full Text PDFCholesterol-dependent cytolysins (CDCs) comprise a large family of pore-forming toxins produced by Gram-positive bacteria, which are used to attack eukaryotic cells. Here, we functionally characterize a family of 2-component CDC-like (CDCL) toxins produced by the Gram-negative Bacteroidota that form pores by a mechanism only described for the mammalian complement membrane attack complex (MAC). We further show that the Bacteroides CDCLs are not eukaryotic cell toxins like the CDCs, but instead bind to and are proteolytically activated on the surface of closely related species, resulting in pore formation and cell death.
View Article and Find Full Text PDFPurpose Of Review: Many cholesterol-dependent cytolysin (CDC)-producing pathogens pose a significant threat to human health. Herein, we review the pore-dependent and -independent properties CDCs possess to assist pathogens in evading the host immune response.
Recent Findings: Within the last 5 years, exciting new research suggests CDCs can act to inhibit important immune functions, disrupt critical cell signaling pathways, and have tissue-specific effects.
Article Synopsis
- This study assesses the effectiveness of three AI algorithms for segmenting prostate regions in MRI scans of patients with complex medical backgrounds and varied anatomical features.
- The researchers analyzed data from 683 MRI scans, ensuring that they included criteria such as previous treatments and different scanner qualities, and compared the AI’s segmentation against expert radiologist assessments.
- Results showed that deep learning models significantly outperformed other methods, especially in cases with smaller prostate volumes and better image quality, highlighting the challenges presented by variances in anatomy and scan conditions.