In the context of sustainable development and promoting natural resources, some forgotten fruits such as the Zizyphus Lotus can be developed as local products. In addition, the frequency of Urolithiasis multiplied increasingly rapidly in the world. The Urolithiasis involves the formation of crystalline aggregates called "urinary stones" developed in the urinary tract, usually in the kidneys or ureters, but may also affect the bladder or urethra. The objective of this study is to study in vitro the crystallization of calcium oxalate as a lithogenic species and to investigate the inhibitory effect of aqueous jujube extracts on the crystallization of calcium oxalate in order to exploit it. Six jujube fruit samples were taken from six geographical zones from Beni Mellal-Khenifra region. These fruit - seed samples were separated from their seeds. The pulps were subjected to a grinding mortar so as to have fine powder. The samples have been subsequently, submitted a cold maceration during 48 hours. The study of the crystallization of calcium oxalate is carried out by the optical microscope with polarized light (MLP). Some aqueous extracts have an anti-lithiasic effect on the aqueous solution of the lithogenous species studied.

The goal of this study is to develop a simple intelligent urolithiasis diagnosis system. The accuracy of the system was determined by comparing the recognition rates of the Artificial Neural Networks (ANNs)-, k-nearest Neighbor (kNN)-, and Support Vector Machines (SVM) algorithms. The results showed that the ANN model was superior to SVM and KNN models in prediction. We aimed through this work to classify the subjects in three classes according to the chemical concentrations of variables (Ca, Ox, pCaOx, Ca/ Ox) using and according to their clinical status. The ANN model, used to determine the first class that contains the subjects presenting their urine a calcium oxalate monohydrate (CaC2O4,H2O : whewellite (Wh)) crystal type. This ANN model reached a correct prediction rate of 85.3%. Using SVM- and KNN model the correct prediction rate reached 82.6% and 65.55% respectively. The second class contains the subjects presenting a calcium oxalate dihydrate (CaC2O4,2H2O wedellite (Wd)) crystal type. The ANN-, SVM- and KNN model reached a 93.4%-, 94.2%- and 77.25% correct prediction rate, respectively. In third class that corresponds to the subjects who have negative crystalluria (NC), ANN-, SVM- and KNN model reached a 91.7%-, 87.8%- and 69.77% correct prediction rate, respectively. Compared to SVM- and KNN models, the developed system using ANN model has allowed us to discriminate the subjects. This system is important in clinical laboratories since it could be a helpful tool for provide information about the development, formation of urinary stones crystals and the determination of their crystal type.

The present work reports a comparative study of spontaneous crystalluria for non- and goitrous patients with the aim to determine its correlation with parathyroid gland activity and goiter etiology. The crystalluria was accessed based on optical polarized light microscopy (OPLM). Goiter presents high woman predominance with an average age of 35.6 years. The frequency of majority constituents in crystalluria is age dependent and amorphous complex carbonated phosphates (ACCP) and uric acid (UA) are the frequent chemical species. The observed hyperphosphaturia can be explained by the estrogen's activity on parathyroid cells proliferation. The presence of oxalo-calcic crystalluria confirms hyperparathyroidism as one of the hypercalciuric kidney stone etiology. Clinical goiter diagnostic and treatment could be followed and confirmed by a simple fluctuations follow-up of crystalluria composition according to phosphate and calcium species.