Publications by authors named "M Deloche"
Misfolding of the cellular PrP (PrP) protein causes prion disease, leading to neurodegenerative disorders in numerous mammalian species, including goats. A lack of PrP induces complete resistance to prion disease. The aim of this work was to engineer Alpine goats carrying knockout (KO) alleles of PRNP, the PrP-encoding gene, using CRISPR/Cas9-ribonucleoproteins and single-stranded donor oligonucleotides.
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- Dairy cattle breeds face recurrent recessive genetic defects that are often undetected due to conventional observation techniques missing various conditions, particularly those without clear symptoms.
- A new data mining framework has been developed to identify these hidden recessive defects in livestock by analyzing genomic data and comparing homozygote numbers in cattle with diverse life histories.
- This research uncovered 33 new genetic loci linked to increased juvenile mortality, offering insights into the genetic causes of inbreeding depression, which can enhance animal welfare and reduce industry losses.
Recently, a new genetically autosomal recessive color phenotype emerged in the red pied bovine Montbéliarde breed. It is characterized by a dilution of the red areas of the coat and was denominated 'milca'. A genome-wide homozygosity scan of 106 cases followed by haplotype analysis revealed a candidate region within BTA2 between positions 89.
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- - Pecorans, or higher ruminants like sheep and goats, have a variety of unique headgear, such as horns, which may have a common genetic origin, but the exact genetic mechanisms are not fully understood.
- - The study focuses on certain rare sheep and goat populations with polyceraty, meaning they have more than two horns, identifying specific genetic variations linked to a gene called HOXD1 that influences horn development.
- - Findings suggest that mutations in the HOXD1 gene lead to abnormal horn bud formation, emphasizing its crucial role in determining the number and arrangement of horns in these animals and shedding light on their evolutionary development.
Artificial intelligence solution to classify pulmonary nodules on CT.
Diagn Interv Imaging
December 2020
Purpose: The purpose of this study was to create an algorithm to detect and classify pulmonary nodules in two categories based on their volume greater than 100 mm or not, using machine learning and deep learning techniques.
Materials And Method: The dataset used to train the model was provided by the organization team of the SFR (French Radiological Society) Data Challenge 2019. An asynchronous and parallel 3-stages pipeline was developed to process all the data (a data "pre-processing" stage; a "nodule detection" stage; a "classifier" stage).