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Institution

Institute of Chartered Accountants of Nigeria

About: Institute of Chartered Accountants of Nigeria is a based out in . It is known for research contribution in the topics: Population & Adipose tissue. The organization has 528 authors who have published 579 publications receiving 18688 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors compared the prognostic value of Longitudinal strain measured from the 4 chambers view to LVEF and found that LVEFs appeared to be a better predictor of outcome than LS in different diseases.

2 citations

Journal ArticleDOI
TL;DR: La consommation moyenne annuelle de soins des patients diabetiques de type 2 (DT2) a ete estimee a 6 506 ± 9 955 € par patient dans une perspective collective limitee aux couts directs en 2013, soit environ 5% des depenses de sante pour l’annee 2013.

2 citations

Journal ArticleDOI
01 Sep 2015-PLOS ONE
TL;DR: This contribution proposes a framework based on a sparse zero-sum game which performs a stable functional feature selection based on feature subsets ranking by a thresholding stochastic bandit and provides a theoretical analysis of the introduced algorithm.
Abstract: In large-scale systems biology applications, features are structured in hidden functional categories whose predictive power is identical. Feature selection, therefore, can lead not only to a problem with a reduced dimensionality, but also reveal some knowledge on functional classes of variables. In this contribution, we propose a framework based on a sparse zero-sum game which performs a stable functional feature selection. In particular, the approach is based on feature subsets ranking by a thresholding stochastic bandit. We provide a theoretical analysis of the introduced algorithm. We illustrate by experiments on both synthetic and real complex data that the proposed method is competitive from the predictive and stability viewpoints.

2 citations

Journal ArticleDOI
TL;DR: A simple algorithm can classify patients referred for TTE as being at high or low pretest probability of having a normal examination, which might help to optimize management of requests in routine practice.

2 citations

Journal ArticleDOI
TL;DR: High Field ex vivo MRI was able to quantify fibrosis in human SAT samples with high agreement with histology and moreover to provide 3D SAT fibrosis quantification avoiding histological sampling errors.
Abstract: To determine whether magnetic resonance imaging (MRI) when used in an optimal ex vivo setting can help detecting and quantifying the 3D fibrosis fraction in human subcutaneous adipose tissue (SAT) samples, as compared to histology. This prospective observational study was approved by our institutional review board 3D MRI acquisitions were performed at 4.0 T (Bruker) on XX human SAT samples (around 1 cm3) collected from biopsy in morbidly obese patients. Such acquisitions included saturation-recovery T1 mapping (spatial resolution: 200 µm, acquisition time: ~16 minutes) and DIXON imaging (spatial resolution: 200 µm, acquisition time: ~20 minutes). After MRI, histological quantification of fibrosis was performed using picrosirius staining. T1 maps were clustered based on a k-means algorithm allowing quantification of fibrosis within the adipose tissue and percentage of fibrosis over the entire sample volume was calculated. Fat maps were computed from DIXON in-phase and out-of-phase images. The 3D MRI fibrosis percentage within the SAT samples were comprised between 6% and 15%. Excellent correlations and levels of agreement were observed between single slice MRI and histology (r=0.9, P=0.08) and between 3D MRI and histology in terms fibrosis percentages within SAT samples (r=0.9, P=0.01). High Field ex vivo MRI was able to quantify fibrosis in human SAT samples with high agreement with histology and moreover to provide 3D SAT fibrosis quantification avoiding histological sampling errors.

2 citations


Authors

Showing all 528 results

NameH-indexPapersCitations
Ronald M. Evans199708166722
Thierry Poynard11966864548
Heikki Joensuu10857150300
Gilles Montalescot10064158644
François Cambien9225136260
Antoine Danchin8048330219
Laurence Tiret7919425231
Karine Clément7827532185
Karine Clément7322814710
Pascal Ferré6924123969
Michael T. Osterholm6826022624
Vincent Jarlier6727817060
Florent Soubrier6722624486
Stephen H. Caldwell6630818527
Christian Funck-Brentano6426770432
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202168
202073
201950
201848
201793
201686