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The impact of several environmental and genetic factors on diabetes and its complications is well documented but there is an urgent need to understand more about genetic risk factors associated with this disease. The present study was aimed at examining the two single nucleotide polymorphisms (SNP) in intron 8 and exon 9 of the vitamin D receptor (VDR) gene in nephropathic and non-nephropathic type-2 diabetic patients. In this clinical study, peripheral blood samples were obtained from 100 type-2 diabetic patients, 100 nephropathic type-2 diabetic patients and 100 healthy controls. DNA was extracted and polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was performed to examine two SNP polymorphisms within the VDR gene. Our results showed a significant difference in the Taq-1 evaluated genotypes of exon 9 in the VDR gene of diabetic individuals with (P=0.012) and without (P ≤ 0.001) nephropathy. Analysis of the Taq-1 evaluated alleles of nephropathic (P=0.917) and non-nephropathic (P=1.000) did not show a significant difference. We also evaluated the intron 8 Apa-1 alleles in patients with (P=0.480) and without nephropathy (P=0.543) and determined there were no differences between these groups. Our results also showed that the frequency of Apa-1 genotypes did not differ in nephropathic (P=0.224) and non-nephropathic (P=0.236) diabetic patients. Based on our results, it can be concluded that VDR and its functional polymorphism in exon 9 may play an important role in pathogenesis of type-2 diabetes and more investigations are required to clarify their role in nephropathy.

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Flyrock is one of the most hazardous events in blasting operation of surface mines. There are several empirical methods to predict flyrock. Low performance of such models is due to complexity of flyrock analysis. Existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict and control flyrock in blasting operation of Sangan iron mine, Iran incorporating rock properties and blast design parameters using artificial neural network (ANN) method. A three-layer feedforward back-propagation neural network having 13 hidden neurons with nine input parameters and one output parameter were trained using 192 experimental blast datasets. It was also observed that in ascending order, blastability index, charge per delay, hole diameter, stemming length, powder factor are the most effective parameters on the flyrock. Reducing charge per delay caused significant reduction in the flyrock from 165 to 25 m in the Sangan iron mine.

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Blasting operation should be performed satisfying some criteria, such as fragmentation, flyrock, and cost. To reach the most appropriate alternative among previous performed blast designs, all the criteria should be simultaneously considered in the analysis. To do so, rather new emerging approaches such as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a branch of multi-criteria decision-making techniques could be applied. Using TOPSIS method, the present study tries to investigate the blasting operation in the Tajareh limestone mine and select the most appropriate blasting pattern. According to the obtained results, alternative ten with hole diameter of 64 mm and staggered pattern designed by Ash formula, was selected to be the best decision. Application of this alternative comparatively satisfies both fragmentation and flyrock.