Prevention of alloimmune rejection using XBP1-deleted bone marrow-derived dendritic cells in heart transplantation

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Skin3D: Detection and longitudinal tracking of pigmented skin lesions in 3D total-body textured meshes

We present an automated approach to detect and longitudinally track skin lesions on 3D total-body skin surface scans. The acquired 3D mesh of the subject is unwrapped to a 2D texture image, where a trained objected detection model, Faster R-CNN, localizes the lesions within the 2D domain. These detected skin lesions are mapped back to the 3D surface of the subject and, for subjects imaged multiple times, we construct a graph-based matching procedure to longitudinally track lesions that considers the anatomical correspondences among pairs of meshes and the geodesic proximity of corresponding lesions and the inter-lesion geodesic distances.We evaluated the proposed approach using 3DBodyTex, a publicly available dataset composed of 3D scans imaging the coloured skin (textured meshes) of 200 human subjects. We manually annotated locations that appeared to the human eye to contain a pigmented skin lesion as well as tracked a subset of lesions occurring on the same subject imaged in different poses. Our results, when compared to three human annotators, suggest that the trained Faster R-CNN detects lesions at a similar performance level as the human annotators. Our lesion tracking algorithm achieves an average matching accuracy of 88% on a set of detected corresponding pairs of prominent lesions of subjects imaged in different poses, and an average longitudinal accuracy of 71% when encompassing additional errors due to lesion detection. As there currently is no other large-scale publicly available dataset of 3D total-body skin lesions, we publicly release over 25,000 3DBodyTex manual annotations, which we hope will further research on total-body skin lesion analysis.     

A comprehensive method for calculating total body irradiation

PurposeTo provide means for calculating the dose received by various tissues of the patient, calculate lung shield, and verify received dose using a phantom as a tool for quality assurance for a planned Total Body Irradiation (TBI) procedure in radiotherapy.MethodUsing Microsoft Visual Basic, MATLAB, and Python, a program for Total Body Irradiation Calculation in Radiotherapy (TBICR) is constructed. It uses patient translation and beam zone method for total body irradiation calculations to compute the proper dose received by the patient and determine the lung shield thickness. There are three main user-friendly interfaces in the application. The first one allows the user to upload the TBI topography and estimate the distances needed for TBI calculations. The second one enables the user to count the number of beam zones needed for each point and estimate the effective area (A_eff) for each level. The third interface estimates the velocity required to deliver the relative dose depending on patient separation, Monitor Units (MU), couch speed and travel distance. It allows the user to compute the required lung shield thickness, read any patient's CT DICOM file and acquire dose in any distinct location using machine learning model to predict the dose.ResultsThe TBICR software has been successfully validated by reproducing all of the manual calculations in an exact and timely manner. TBICR generated more accurate results and confirmed the absorbed dose to patient through measurements on Anderson phantom.ConclusionsA computer program for the calculation of total body irradiation (TBI) is described in full. The dose received at each point on the patient, the calculation of lung shield and the determination of the velocity and time required for the couch movement are all made possible using the software. The ease of use, precision, data storage and printing are some important features of the present software.     

Ivermectin administration is associated with lower gastrointestinal complications and greater ventilator-free days in ventilated patients with COVID-19: A propensity score analysis

IntroductionCOVID-19 patients have been reported to have digestive symptoms with poor outcome. Ivermectin, an antiparasitic drug, has been used in COVID-19 patients. The objective of this study was to evaluate whether ivermectin has effects on gastrointestinal complications and ventilator-free days in ventilated patients with COVID-19.MethodsCOVID-19 patients who were mechanically ventilated in the ICU were included in this study. The ventilated patients who received ivermectin within 3 days after admission were assigned to the Ivermectin group, and the others were assigned to the Control group. Patients in the Ivermectin group received ivermectin 200 μg/kg via nasal tube. The incidence of gastrointestinal complications and ventilator-free days within 4 weeks from admission were evaluated as clinical outcomes using a propensity score with the inverse probability weighting method.ResultsWe included 88 patients in this study, of whom 39 patients were classified into the Ivermectin group, and 49 patients were classified into the Control group. The hazard ratio for gastrointestinal complications in the Ivermectin group as compared with the Control group was 0.221 (95% confidence interval [CI], 0.057 to 0.855; p = 0.029) in a Cox proportional-hazard regression model. The odds ratio for ventilator-free days as compared with the Control group was 1.920 (95% CI, 1.076 to 3.425; p = 0.027) in a proportional odds logistic regression model.ConclusionsIvermectin improved gastrointestinal complications and the number of ventilator-free days in severe COVID-19 patients undergoing mechanical ventilation. Prevention of gastrointestinal symptoms by SARS-Cov-2 might be associated with COVID-19 outcome.