Volume 23, Issue 4, July 2018, Pages 419–423
Abdelouahed Ait Ider1, Mustapha El Bir2, Constant TCHEKA3, Mohamed Maaouni4, Abdelkrim Merbouha5, and Mohamed Mbarki6
1 Department of chemistry and environment, Sultan Moulay Slimane University, Faculty of Science and Technology, Transdisciplinary Team of Analytical Science for Sustainable Development, PB 523, Beni Mellal, Morocco
2 University of Sultan Moulay Slimane, Faculty of Science and Technology, P.B 523, Beni Mellal, Morocco
3 Laboratory of Physical and Theoretical Chemistry, Department of Inorganic Chemistry, Faculty of Science, P.O. Box 812, Yaoundé, Cameroon
4 Transdisciplinary Team of Analytical Sciences for Sustainable Development, Faculty of Science and Technologies, Sultan Moulay Slimane University, 23000 - Beni Mellal, Morocco
5 Faculty of Sciences and Technology, Sultan Moulay Slimane University, Beni Mellal, Morocco
6 Department of chemistry and environment, Sultan Moulay Slimane University, Faculty of Science and Technology, Transdisciplinary Team of Analytical Science for Sustainable Development, PB 523, Beni Mellal, Morocco
Original language: English
Copyright © 2018 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The majority of the analyzed calculi from patients are composed of calcium oxalate (CaOx) monohydrate whewellite (Wh) and CaOx dihydrate wedellite (Wd). The urinary calculi were identified by chemical and morphological analysis based on106 urine samples from human voluntary. The Crystalluria made by an optical polarized light microscopy. The oxaluria and urinary calcium were determined by conventional volumetric assays. The aim of this paper was to develop a simple system to predict and classify the type of crystalluria using Artificial Neural Networks (ANNs) algorithm.
Author Keywords: calcium oxalate, urinary calculi, Artificial Neural Networks.
Abdelouahed Ait Ider1, Mustapha El Bir2, Constant TCHEKA3, Mohamed Maaouni4, Abdelkrim Merbouha5, and Mohamed Mbarki6
1 Department of chemistry and environment, Sultan Moulay Slimane University, Faculty of Science and Technology, Transdisciplinary Team of Analytical Science for Sustainable Development, PB 523, Beni Mellal, Morocco
2 University of Sultan Moulay Slimane, Faculty of Science and Technology, P.B 523, Beni Mellal, Morocco
3 Laboratory of Physical and Theoretical Chemistry, Department of Inorganic Chemistry, Faculty of Science, P.O. Box 812, Yaoundé, Cameroon
4 Transdisciplinary Team of Analytical Sciences for Sustainable Development, Faculty of Science and Technologies, Sultan Moulay Slimane University, 23000 - Beni Mellal, Morocco
5 Faculty of Sciences and Technology, Sultan Moulay Slimane University, Beni Mellal, Morocco
6 Department of chemistry and environment, Sultan Moulay Slimane University, Faculty of Science and Technology, Transdisciplinary Team of Analytical Science for Sustainable Development, PB 523, Beni Mellal, Morocco
Original language: English
Copyright © 2018 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The majority of the analyzed calculi from patients are composed of calcium oxalate (CaOx) monohydrate whewellite (Wh) and CaOx dihydrate wedellite (Wd). The urinary calculi were identified by chemical and morphological analysis based on106 urine samples from human voluntary. The Crystalluria made by an optical polarized light microscopy. The oxaluria and urinary calcium were determined by conventional volumetric assays. The aim of this paper was to develop a simple system to predict and classify the type of crystalluria using Artificial Neural Networks (ANNs) algorithm.
Author Keywords: calcium oxalate, urinary calculi, Artificial Neural Networks.
How to Cite this Article
Abdelouahed Ait Ider, Mustapha El Bir, Constant TCHEKA, Mohamed Maaouni, Abdelkrim Merbouha, and Mohamed Mbarki, “Using concentrations of calcium and oxalates to predict crystalluria type,” International Journal of Innovation and Applied Studies, vol. 23, no. 4, pp. 419–423, July 2018.
