Expression of the mucin-like glycoprotein CD24 and its ligand siglec-10 in placentas with acute and post SARS-CoV-2 infection.

CD24 is a mucin-like glycoprotein expressed on trophoblast cells and endothelial tissue of first and third trimester placentas. As an immune suppressor, CD24 may contribute to maternal immune tolerance to the growing fetus. CD24 is known to interact with the sialic acid-binding immunoglobulin-type lectins (Siglecs), specifically siglec-10.

PD-L1 expression and characterization of its carrier macrophages in placentas with acute and specifically post-SARS-CoV-2 infection.

At the beginning of the coronavirus disease 2019 (COVID-19) pandemic, uncertainties about the virus and its dangers during pregnancy caused great uncertainty and fear, especially among pregnant women. New data suggest an increased risk of obstetric complications, including maternal complications, preterm labor, intrauterine growth restriction, hypertensive disorders, stillbirths, gestational diabetes and risk, of neonatal developmental disorders. In addition, preeclampsia (PE)-like syndromes were also induced by severe COVID-19 infection.

Idiopathic Granulomatous Mastitis as a Benign Condition Mimicking Inflammatory Breast Cancer: Current Status, Knowledge Gaps and Rationale for the GRAMAREG Study (EUBREAST-15).

Background: Idiopathic granulomatous mastitis (IGM) is a rare, benign inflammatory breast condition often mistaken for inflammatory breast cancer and, therefore, requires a biopsy for accurate diagnosis. Although not cancerous, IGM can cause emotional distress because of severe pain and ensuing breast deformity. Differentiating IGM from other breast inflammations caused by infections is essential.

Deep Learning Based Automatic Fibroglandular Tissue Segmentation in Breast Magnetic Resonance Imaging Screening.

In light of the global increase in breast cancer cases and the crucial importance of the density of fibroglandular tissue (FGT) in assessing risk and predicting the course of the disease, the accurate measurement of FGT emerges as a significant challenge in diagnostic imaging. The current study focuses on the automatic segmentation of breast glandular tissue in MRI scans using a deep learning model. The aim is to establish a solid foundation for the development of methods for the precise quantification of fibroglandular tissue.