Analysis of the transplacental transmission of SARS CoV-2 virus and antibody transfer according to the gestational age at maternal infection.

To quantify transplacental transmission of SARS-CoV-2 virus and antibody transfer in pregnant women and their newborns according to the gestational age at maternal infection. A prospective observational multicenter study including pregnant women with a positive RT-PCR or a positive serology for SARS-CoV-2 and compatible symptoms, from April to December 2020, in 11 French maternities. The study was designed to obtain a systematic collection of mother-infant dyad's samples at birth. SARS-CoV-2 viral load was measured by RT-PCR. IgG and IgM antibodies against the SARS-CoV-2 spike protein were measured by enzyme-linked immunosorbent assay. Antibody concentrations and transplacental transfer ratios were analyzed according to the gestational age at maternal infection. The primary outcome was the rate of SARS CoV-2 materno-fetal transmission at birth. The secondary outcome was the quantification of materno-fetal antibody transfer. Maternal and neonatal outcomes at birth were additionally assessed. Among 165 dyads enrolled, one congenital infection was confirmed {n = 1 (0.63%) IC95% [0.02%; 3.48%]}. The average placental IgG antibody transfer ratio was 1.27 (IC 95% [0.69-2.89]). The transfer ratio increased with increasing time between the onset of maternal infection and delivery (P Value = 0.0001). Maternal and neonatal outcomes were reassuring. We confirmed the very low rate of SARS-CoV-2 transplacental transmission (< 1%). Maternal antibody transfer to the fetus was more efficient when the infection occurred during the first and second trimester of pregnancy.

Idiopathic Granulomatous Mastitis as an Unusual Cause of Erythema Nodosum in a Malagasy Woman.

Introduction: Idiopathic granulomatous mastitis (IGM) is a rare chronic inflammatory disease. Neoplastic and infectious etiologies must be ruled out. IGM is a diagnostic challenge for countries with high tuberculosis endemicity like Madagascar since it may clinically and radiologically mimic breast tuberculosis. We report a case of IGM associated with erythema nodosum in a Malagasy. Case Report: A 29-year-old primiparous woman came to a dermatological consultation for typical erythema nodosum lesions that appeared one month after a breast swelling. She had no particular medical history. Examination revealed typical erythema nodosum lesions on the legs, voluminous tender mass in the right breast. Bacteriological samples and tuberculosis test were negative. Imaging showed mastitis on the right breast with no evidence of malignancy. Histology revealed a non-caseating granulomas on the lobule of the right breast. As part of an etiological work-up, COVID-19 serology was performed with a positive IgG antibody. The diagnosis of IGM associated with erythema nodosum was evocated. The evolution was favorable under systemic corticosteroid therapy. Discussion: The cause of this uncommon lesion remains obscure. The extramammary localizations such as erythema nodosum and arthralgia suggest an autoimmune origin. This pathogenesis is also reinforced by a good response to systemic immunosuppression. In our patient, the etiological assessment of the mastitis revealed a chronic infection with SARS-CoV-2. Histopathology is the gold standard for the IGM diagnosis which demonstrates a lobulocentric granulomas without caseous necrosis. Oral corticosteroid therapy is the initial choice of treatment. Conclusion: Now, with several cases of concomitant IGM and EN reported, dermatologists should be aware that erythema nodosum can be one of the presenting signs of IGM, since the two conditions appear to be associated. The particularity of our case lies in the incidental discovery of SARS-CoV-2 infection. Is a chronic granulomatous disease associated with SARS-CoV-2 infection, a coincidence?

Détection précoce du cancer du sein : étude de l'impact sociotechnique d'un soutien-gorge «â€ˆintelligent ¼.

INTRODUCTION: The SBra (Smart Bra) project aims to develop an intelligent bra, combining sensors for measuring skin temperature and the electrical impedance of breast tissue, which could be used for breast cancer screening. The objective of this study is to anticipate both the hindrances to usage and acceptability of SBra with respect to the breast cancer screening practices of healthcare professionals and patients, and then to propose ways to modify the shape and functions of the device to facilitate its potential insertion into the healthcare system. METHODS: A qualitative survey was conducted between September 2019 and December 2020, consisting of a series of interviews conducted with hospital and private healthcare professionals (N = 22) working in Burgundy-Franche-Comté and related to breast cancer, and with women aged 38 to 74 years old living in Burgundy-Franche-Comté and Auvergne-Rhône-Alpes (N = 21) who have or have not had breast cancer, and who either practice or refuse screening. RESULTS: If patients say they are ready to use such a device, at most once a year, and subject to its usability, the majority of them prefer an examination in the office, performed by a gynecologist or a general practitioner. Health professionals point out that this option generates institutional (remuneration and cost of the procedure) and organizational needs, which are both material and human. DISCUSSION: The study highlights the need to pluralize the system in order to respond to the multiplicity of local situations.

Prediction of Oncotype DX recurrence score using deep multi-layer perceptrons in estrogen receptor-positive, HER2-negative breast cancer.

Oncotype DX (ODX) is a multi-gene expression signature designed for estrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer patients to predict the recurrence score (RS) and chemotherapy (CT) benefit. The aim of our study is to develop a prediction tool for the three RS's categories based on deep multi-layer perceptrons (DMLP) and using only the morphoimmunohistological variables. We performed a retrospective cohort of 320 patients who underwent ODX testing from three French hospitals. Clinico-pathological characteristics were recorded. We built a supervised machine learning classification model using Matlab software with 152 cases for the training and 168 cases for the testing. Three classifiers were used to learn the three risk categories of the ODX, namely the low, intermediate, and high risk. Experimental results provide the area under the curve (AUC), respectively, for the three risk categories: 0.63 [95% confidence interval: (0.5446, 0.7154), p < 0.001], 0.59 [95% confidence interval: (0.5031, 0.6769), p < 0.001], 0.75 [95% confidence interval: (0.6184, 0.8816), p < 0.001]. Concordance rate between actual RS and predicted RS ranged from 53 to 56% for each class between DMLP and ODX. The concordance rate of low and intermediate combined risk group was 85%.We developed a predictive machine learning model that could help to define patient's RS. Moreover, we integrated histopathological data and DMLP results to select tumor for ODX testing. Thus, this process allows more relevant use of histopathological data, and optimizes and enhances this information.

[Neural network: A future in pathology?] / Intelligence artificielle : quel avenir en anatomie pathologique ?

Artificial Intelligence, in particular deep neural networks are the most used machine learning technics in the biomedical field. Artificial neural networks are inspired by the biological neurons; they are interconnected and follow mathematical models. Two phases are required: a learning and a using phase. The two main applications are classification and regression Computer tools such as GPU computational accelerators or some development tools such as MATLAB libraries are used. Their application field is vast and allows the management of big data in genomics and molecular biology as well as the automated analysis of histological slides. The Whole Slide Image scanner can acquire and store slides in the form of digital images. This scanning associated with deep learning algorithms allows automatic recognition of lesions through the automatic recognition of regions of interest previously validated by the pathologist. These computer aided diagnosis techniques are tested in particular in mammary pathology and dermatopathology. They will allow an efficient and a more comprehensive vision, and will provide diagnosis assistance in pathology by correlating several biomedical data such as clinical, radiological and molecular biology data.