Effectiveness of second-opinion radiology consultations to reassess the cervical spine CT scans: a study on trauma patients referred to a tertiary-care hospital

PURPOSEA second opinion is a valuable resource in confirming proper medical diagnosis and treatment. This study evaluates the effectiveness of second-opinion radiology consultations to reassess the cervical spine computed tomography (CT) scans of the trauma patients referred to our hospital.METHODSCervical spine CT scans of 301 consecutive adult trauma patients, who were referred to our hospital from outside institutions, were analyzed. The emergency radiologists at our institution completed the over-read reports on the CT images obtained at the outside facilities. A single radiologist compared the outside- and over-read reports and determined the discrepancy of the radiologic reports.RESULTSBased on the outside reports, 31% of the CT scans had cervical traumatic injury. In 92% of patients, the first-read and the over-read reports had consistent radiologic findings. About 90% of the positive, and 93% of the negative radiologic findings, were reported consistently in the over-read reports. Our analysis showed that the over-read reporting resulted in reassurance of negative findings in 63%; confirmation of positive findings in 29%; clearing a false diagnosis in 3%; and detection of a missed diagnosis in 5%. A rescan was done in 80% of patients with inconsistent and 20% of patients with consistent findings (P < 0.05). The most common missed radiologic findings in the first-reports were transverse and spinous process fractures and the most common misdiagnoses were dens fractures.CONCLUSIONFor a service offering second-opinion consultations on cervical spine trauma, review of outside CT studies improves diagnosis and benefits patient care

Optimized peptide-MHC multimer protocols for detection and isolation of autoimmune T-cells

<p>Peptide–MHC (pMHC) multimers have become the “gold standard” for the detection and isolation of antigen-specific T-cells but recent evidence shows that normal use of these reagents can miss fully functional T-cells that bear T-cell receptors (TCRs) with low affinity for cognate antigen. This issue is particularly pronounced for anticancer and autoimmune T-cells as self-reactive T-cell populations are enriched for low-affinity TCRs due to the removal of cells with higher affinity receptors by immune tolerance mechanisms. Here, we stained a wide variety of self-reactive human T-cells using regular pMHC staining and an optimized technique that included: (i) protein kinase inhibitor (PKI), to prevent TCR triggering and internalization, and (ii) anti-fluorochrome antibody, to reduce reagent dissociation during washing steps. Lymphocytes derived from the peripheral blood of type 1 diabetes patients were stained with pMHC multimers made with epitopes from preproinsulin (PPI), insulin-β chain, glutamic acid decarboxylase 65 (GAD65), or glucose-6-phospate catalytic subunit-related protein (IGRP) presented by disease-risk allelles HLA A*02:01 or HLA*24:02. Samples from ankylosing spondylitis patients were stained with a multimerized epitope from vasoactive intestinal polypeptide receptor 1 (VIPR1) presented by HLA B*27:05. Optimized procedures stained an average of 40.5-fold (p = 0.01, range between 1.4 and 198) more cells than could be detected without the inclusion of PKI and cross-linking anti-fluorochrome antibody. Higher order pMHC dextramers recovered more cells than pMHC tetramers in parallel assays, and standard staining protocols with pMHC tetramers routinely recovered less cells than functional assays. HLA A*02:01-restricted PPI-specific and HLA B*27:05-restricted VIPR1-specific T-cell clones generated using the optimized procedure could not be stained by standard pMHC tetramer staining. However, these clones responded well to exogenously supplied peptide and endogenously processed and presented epitopes. We also showed that anti-fluorochrome antibody-conjugated magnetic beads enhanced staining of self-reactive T-cells that could not be stained using standard protocols, thus enabling rapid ex vivo isolation of autoimmune T-cells. We, therefore, conclude that regular pMHC tetramer staining is generally unsuitable for recovering self-reactive T-cells from clinical samples and recommend the use of the optimized protocols described herein.</p

Optimized Peptide–MHC Multimer Protocols for Detection and Isolation of Autoimmune T-Cells

Peptide–MHC (pMHC) multimers have become the “gold standard” for the detection and isolation of antigen-specific T-cells but recent evidence shows that normal use of these reagents can miss fully functional T-cells that bear T-cell receptors (TCRs) with low affinity for cognate antigen. This issue is particularly pronounced for anticancer and autoimmune T-cells as self-reactive T-cell populations are enriched for low-affinity TCRs due to the removal of cells with higher affinity receptors by immune tolerance mechanisms. Here, we stained a wide variety of self-reactive human T-cells using regular pMHC staining and an optimized technique that included: (i) protein kinase inhibitor (PKI), to prevent TCR triggering and internalization, and (ii) anti-fluorochrome antibody, to reduce reagent dissociation during washing steps. Lymphocytes derived from the peripheral blood of type 1 diabetes patients were stained with pMHC multimers made with epitopes from preproinsulin (PPI), insulin-β chain, glutamic acid decarboxylase 65 (GAD65), or glucose-6-phospate catalytic subunit-related protein (IGRP) presented by disease-risk allelles HLA A*02:01 or HLA*24:02. Samples from ankylosing spondylitis patients were stained with a multimerized epitope from vasoactive intestinal polypeptide receptor 1 (VIPR1) presented by HLA B*27:05. Optimized procedures stained an average of 40.5-fold (p = 0.01, range between 1.4 and 198) more cells than could be detected without the inclusion of PKI and cross-linking anti-fluorochrome antibody. Higher order pMHC dextramers recovered more cells than pMHC tetramers in parallel assays, and standard staining protocols with pMHC tetramers routinely recovered less cells than functional assays. HLA A*02:01-restricted PPI-specific and HLA B*27:05-restricted VIPR1-specific T-cell clones generated using the optimized procedure could not be stained by standard pMHC tetramer staining. However, these clones responded well to exogenously supplied peptide and endogenously processed and presented epitopes. We also showed that anti-fluorochrome antibody-conjugated magnetic beads enhanced staining of self-reactive T-cells that could not be stained using standard protocols, thus enabling rapid ex vivo isolation of autoimmune T-cells. We, therefore, conclude that regular pMHC tetramer staining is generally unsuitable for recovering self-reactive T-cells from clinical samples and recommend the use of the optimized protocols described herein

Fully Automated Deep Learning System for Bone Age Assessment

Skeletal maturity progresses through discrete phases, a fact that is used routinely in pediatrics where bone age assessments (BAAs) are compared to chronological age in the evaluation of endocrine and metabolic disorders. While central to many disease evaluations, little has changed to improve the tedious process since its introduction in 1950. In this study, we propose a fully automated deep learning pipeline to segment a region of interest, standardize and preprocess input radiographs, and perform BAA. Our models use an ImageNet pretrained, fine-tuned convolutional neural network (CNN) to achieve 57.32 and 61.40% accuracies for the female and male cohorts on our held-out test images. Female test radiographs were assigned a BAA within 1 year 90.39% and within 2 years 98.11% of the time. Male test radiographs were assigned 94.18% within 1 year and 99.00% within 2 years. Using the input occlusion method, attention maps were created which reveal what features the trained model uses to perform BAA. These correspond to what human experts look at when manually performing BAA. Finally, the fully automated BAA system was deployed in the clinical environment as a decision supporting system for more accurate and efficient BAAs at much faster interpretation time (<2 s) than the conventional method