In coronavirus disease 2019 (COVID-19) patients, various dermatological conditions have been observed. Varicella zoster virus (VZV) and herpes simplex virus must be ruled out before considering vesicular exanthems linked to COVID-19. The immunological status of the host has an impact on the natural history of herpes zoster (HZ). Age is a major risk factor for most of the cases of HZ. Reactivation of VZV can be triggered by iatrogenic immunosuppression or disease-related immunocompromised state or age-related immunosenescence. Rarely, dermatological symptoms have been reported in recovered COVID-19 patients. We hereby present a rare case of HZ in a recovered patient from symptomatic reinfection of COVID-19.
The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools.
Optical coherence tomography (OCT) is an advanced imaging modality to detect Barrett’s esophagus (BE) dysplasia, providing widefield, cross-sectional imaging and microscopic resolution. BE dysplasia is characterized under OCT by the presence and number of glandular structures with atypical morphology. Accurate detection and interpretation of BE glands under OCT is essential to detect dysplastic lesions. Object Detection using deep learning has the potential to identify glands from OCT images. We developed a YOLO model to identify the presence of glands in BE tissue. The YOLOv4 object detector was trained on a custom BE dataset of 30 patients with confirmed BE who underwent OCT imaging, of which 222 OCT images included at least one gland. Our model identified glands with a high average precision of 88.79% on the test dataset. We showed that the developed model is robust to rotation, brightness, and blur in images. We have implemented an object detection model to identify glands from OCT images with promising results accurately. This model has the potential to improve the diagnosis and surveillance of BE by eliminating human error and missed dysplastic lesions adaptable for capsule endoscopy applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.