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COVID-19 pneumonia is a newly recognized lung infection. Initially, CT imaging was demonstrated to be one of the most sensitive tests for the detection of infection. Currently, with broader availability of polymerase chain reaction for disease diagnosis, CT is mainly used for the identification of complications and other defined clinical indications in hospitalized patients. Nonetheless, radiologists are interpreting lung imaging in unsuspected patients as well as in suspected patients with imaging obtained to rule out other relevant clinical indications. The knowledge of pathological findings is also crucial for imagers to better interpret various imaging findings. Identification of the imaging findings that are commonly seen with the disease is important to diagnose and suggest confirmatory testing in unsuspected cases. Proper precautionary measures will be important in such unsuspected patients to prevent further spread. In addition to understanding the imaging findings for the diagnosis of the disease, it is important to understand the growing set of tools provided by artificial intelligence. The goal of this review is to highlight common imaging findings using illustrative examples, describe the evolution of disease over time, discuss differences in imaging appearance of adult and pediatric patients and review the available literature on quantitative CT for COVID-19. We briefly address the known pathological findings of the COVID-19 lung disease that may help better understand the imaging appearance, and we provide a demonstration of novel display methodologies and artificial intelligence applications serving to support clinical observations. [ABSTRACT FROM AUTHOR]

The stomach may be involved by a myriad of pathologies ranging from benign aetiologies like inflammation to malignant aetiologies like carcinoma or lymphoma. Multidetector CT (MDCT) of the stomach is the first-line imaging for patients with suspected gastric pathologies. Conventionally, CT imaging had the advantage of simultaneous detection of the mural and extramural disease extent, but advances in MDCT have allowed mucosal assessment by virtual endoscopy (VE). Also, better three-dimensional (3D) post-processing techniques have enabled more robust and accurate pre-operative planning in patients undergoing gastrectomy and even predict the response to surgery for patients undergoing laparoscopic sleeve gastrectomy for weight loss. The ability of CT to obtain stomach volume (for bariatric surgery patients) and 3D VE images depends on various patient and protocol factors that are important for a radiologist to understand. We review the appropriate CT imaging protocol in the patients with suspected gastric pathologies and highlight the imaging pearls of various gastric pathologies on CT and VE. [ABSTRACT FROM AUTHOR]

The article discusses the medical case of a 61-year-old man with left-side neck pain and swelling, dizziness and night sweats. Bone marrow aspiration and biopsy showed 90% blasts with a peculiar cup-shaped nuclear morphology, with molecular diagnostics revealed cooccurring mutations of the nucleophosmin (NPM1) and Fms-like tyrosine kinase (FLT3) genes. The patient was diagnosed with acute myeloid leukemia and started on induction chemotherapy with cytarabine and idarubicin.

Clinical chemistry analysis of body fluids from non-blood or urine sources presents a technical challenge for clinical laboratories. Examples of body fluids include biliary secretions, cerebrospinal fluid, cyst contents, dialysate, gastric aspirates, peritoneal fluid, pleural fluid, stool, surgical drain fluid, synovial fluid, and wound exudates. The heterogeneous nature of these body fluids presents technical difficulties for analysis. For example, body fluid specimens may have presence of hemolysis, icterus, or lipemia ('interference indices') that can interfere with clinical chemistry analysis. In the related research article, we analyzed the distribution of these interference indices and body fluid samples submitted for analysis at an academic medical center central clinical laboratory and compared this to data from serum/plasma specimens. The data in this article provide the body fluid type, clinical chemistry testing ordered, interference indices, and whether the indices exceeded the manufacturer's recommendations in the package insert for serum/blood specimens. The analyzed data are provided in the supplementary tables included in this article. The dataset reported is related to the research article entitled "Review of interference indices in body fluids specimens admitted for clinical chemistry analyses" [1]., (© 2020 The Authors.)