A Fatal Case of Isolated Renal Mucormycosis in an Immunocompetent Male.

Mucormycosis (MM) is an angio-invasive fungal infection that often presents in immunocompromised patients. Isolated renal MM is an uncommon presentation that has been documented as a life-threatening condition in immunocompetent patients due to its poor prognosis. Here, we present a rare case of isolated renal MM in a 27-year-old male who presented with left flank pain, nausea, and vomiting. Upon further investigation, a renal infarct was discovered, and he underwent a subsequent nephrectomy. A renal biopsy revealed MM. The patient's infection spread, and he ultimately succumbed to his illness. Isolated renal involvement of this pathogen is extremely rare in healthy individuals and has poor outcomes. The ubiquitous nature of MM increases the risk of exposure to humans. Comorbidities such as coronavirus disease 2019 and immunosuppressive states are risk factors for the deleterious outcomes of MM. It is unusual for an immunocompetent patient with no underlying conditions to die despite early diagnosis and prompt treatment. This example calls attention to the unpredictable clinical presentation of isolated renal MM. Our case highlights MM as a differential diagnosis in patients with unilateral flank pain and identifies the importance of a prompt clinical diagnosis and treatment due to the rapid progression and poor health outcomes associated with MM infection.

Use and validation of epithelial recognition and fields of view algorithms on virtual slides to guide TMA construction.

While tissue microarrays (TMAs) are a form of high-throughput screening, they presently still require manual construction and interpretation. Because of predicted increasing demand for TMAs, we investigated whether their construction could be automated. We created both epithelial recognition algorithms (ERAs) and field of view (FOV) algorithms that could analyze virtual slides and select the areas of highest cancer cell density in the tissue block for coring (algorithmic TMA) and compared these to the cores manually selected (manual TMA) from the same tissue blocks. We also constructed TMAs with TMAker, a robot guided by these algorithms (robotic TMA). We compared each of these TMAs to each other. Our imaging algorithms produced a grid of hundreds of FOVs, identified cancer cells in a stroma background and calculated the epithelial percentage (cancer cell density) in each FOV. Those with the highest percentages guided core selection and TMA construction. Algorithmic TMA and robotic TMA were overall approximately 50% greater in cancer cell density compared with Manual TMA. These observations held for breast, colon, and lung cancer TMAs. Our digital image algorithms were effective in automating TMA construction.