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Nathalie Bednarek

Researcher at University of Reims Champagne-Ardenne

Publications -  89
Citations -  3028

Nathalie Bednarek is an academic researcher from University of Reims Champagne-Ardenne. The author has contributed to research in topics: Medicine & Pregnancy. The author has an hindex of 22, co-authored 78 publications receiving 2407 citations. Previous affiliations of Nathalie Bednarek include French Institute of Health and Medical Research & University of Paris.

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Survival and morbidity of preterm children born at 22 through 34 weeks' gestation in France in 2011: results of the EPIPAGE-2 cohort study.

Pierre-Yves Ancel, +67 more
- 01 Mar 2015 - 
TL;DR: A substantial improvement in survival in France for newborns born at 25 through 31 weeks' gestation was accompanied by an important reduction in severe morbidity, but survival remained rare before 25 weeks, and improvement at extremely low gestational age may be possible.
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Mirror extreme BMI phenotypes associated with gene dosage at the chromosome 16p11.2 locus

Sébastien Jacquemont, +182 more
- 06 Oct 2011 - 
TL;DR: In this article, the reciprocal duplication is associated with being clinically underweight, which is the main sign of a series of heterogeneous clinical conditions including failure to thrive, feeding and eating disorder and/or anorexia nervosa.

Mirror extreme BMI phenotypes associated with gene dosage at the chromosome 16p11.2 locus

Sébastien Jacquemont, +180 more
TL;DR: The reciprocal impact of these 16p11.2 copy-number variants indicates that severe obesity and being underweight could have mirror aetiologies, possibly through contrasting effects on energy balance.
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Clinical features and prognostic factors of listeriosis: the MONALISA national prospective cohort study.

Caroline Charlier, +1778 more
- 01 May 2017 - 
TL;DR: Evidence of a significantly reduced survival in patients with neurolisteriosis treated with adjunctive dexamethasone is found, and the time window for fetal losses is determined, which is higher than reported elsewhere.
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Multiscale brain MRI super-resolution using deep 3D convolutional networks.

Chi-Hieu Pham, +6 more
- 01 Oct 2019 - 
TL;DR: This work delves into the relevance of several factors in the performance of the purely convolutional neural network-based techniques for the monomodal super-resolution, and highlights that one single network can efficiently handle multiple arbitrary scaling factors based on a multiscale training approach.