Publications by authors named "M Eveillard"
We aimed to describe the characteristics of teachers' engagement and persistence in an innovative multidisciplinary health programme. Participants in this descriptive and comprehensive study consisted of teachers in higher education (veterinary medicine, human medicine, pharmacy, engineers in husbandry) who were involved in the conception and the implementation of an international Master programme called MAN-IMAL. This programme was characterized by interculturality, multidisciplinarity, using technology, active teaching, and learning.
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- VEXAS syndrome, identified in 2020, is caused by mutations in the UBA1 gene and shows a variety of clinical and hematological features, making it challenging to distinguish from other inflammatory conditions. !* -
- This study collected a dataset of 9,514 images of polymorphonuclear cells (PMNs) and used a convolutional neural network (CNN) to automate the detection of specific dysplastic features unique to VEXAS, achieving a high level of accuracy (AUC of 0.85-0.97). !* -
- Results indicate that automated analysis can effectively support hematologists in identifying potential VEXAS cases, suggesting a screening score for UBA1 mutational
We identified a high prevalence (46.4%) of wound colonization with methicillin-resistant Staphylococcus aureus (MRSA) in patients hospitalized in a center devoted to the treatment of cutaneous tropical diseases in Benin. The proportion of MRSA among S aureus isolates was 54.
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- Acute leukaemias are serious blood cancers that require quick and accurate diagnosis of their three main types (ALL, AML, APL) to ensure proper treatment and reduce mortality risk, but expert diagnosis can be challenging due to resource limitations.
- Researchers aimed to develop a machine-learning model using routine laboratory data to identify leukaemia subtypes and improve diagnostic accuracy without the need for extensive cytological expertise.
- The study involved data from six French hospitals and successfully built the Artificial Intelligence Prediction of Acute Leukemia (AI-PAL) tool, which demonstrated strong performance in predicting leukaemia subtypes based on 19 selected laboratory parameters from the patient datasets.