Showing 4 results for Nasli esfahani
Maryam Peimani, Camelia Rambod, Robabeh Ghodsi, Ensieh Nasli Esfahani,
Volume 15, Issue 4 (5-2016)
Abstract
Background: The objective of the current study is to assess the effectiveness of Mobile Short Message Service (SMS) intervention on education of basic self-care skills in patients with type 2 diabetes. Moreover, we aimed to determine whether delivering individually-tailored educational messages can be more effective than general educational messages.
Methods: A total of 150 patients with diabetes type 2 were randomized into three groups: tailored SMS group, non-tailored SMS group, and the control group. Biochemical parameters including HbA1c, FBS, lipid profile were evaluated for the three groups at baseline and after 12 weeks. Moreover, self-care Inventory (SCI), Diabetes Management Self-Efficacy Scale (DMSES) and Diabetes Self -Care Barriers assessment scale for Older Adults (DSCB-OA) were completed. In the tailored SMS group, each person received 75% of their messages based on the top two barriers to adherence that they had experienced and reported in their scale. In the non-tailored SMS group, random messages were sent to every patient.
Results: After12 weeks, although HgA1c levels did not significantly change, significant decline was observed in FBS and mean BMI in both intervention groups. Mean SCI-R scores significantly increased and mean DSCB and DMSES scores significantly decreased in both tailored and non-tailored SMS groups. In the control group, mean SCI-R scores decreased and mean DSCB and DMSES scores significantly increased (P< 0.001).
Conclusion: Sending short text messages as a method of education in conjunction with conventional diabetes treatment can improve glycemic control and positively influence other aspects of diabetes self-care. According to our findings, sending SMS regularly in particular times appears to be as effective as sending individually tailored messages.
Saeedeh Asgarbeik, Mahsa Mohammad Amoli, Seyed Abdolhamid Angaji, Farideh Razi, Ensieh Nasli Esfahani,
Volume 16, Issue 3 (3-2017)
Abstract
Background: Diabetic Nephropathy is one of the main microvascular complications of diabetic mellitus. Methylenetetrahydrofolate Reductase (MTHFR) is one of the candidate genes of diabetic nephropathy. MTHFR (C677T) polymorphism reduces catalytic activity of MTHFR and leads to increase level of plasma homocysteine. The aim of this study was to evaluate the association of C677T polymorphism with diabetic nephropathy.
Methods: In this case control study, 300 individuals, including type 2 diabetes mellitus with diabetic nephropathy (N=104), diabetes mellitus patients without diabetic nephropathy (N=100) and controls (N=96) participated. The MTHFR genotype was determined using PCR-RFLP technique and biochemical parameters were measured.
Results: Genotype frequencies were significantly different between patients with diabetic nephropathy and diabetes mellitus without nephropathy (TT+CT vs CC; P=0.02,OR:0.5,CI:0.3-0.9).The allele frequency was also significantly different between diabetic nephropathy and diabetics mellitus without nephropathy(P=0.013,OR:1.754,CI:1.123-2.740).
Conclusion: These findings suggest that there is an association between C677T polymorphism and nephropathy in patients with type 2 diabetes. Allele C increase the risk of nephropathy, and T allele has a protective role in susceptibility to disease.
Narges Shafaei Bajestani, Maryam Aradmehr, Ensieh Nasli Esfahani, Behrooz Khiabani Tanha,
Volume 18, Issue 2 (2-2019)
Abstract
Background: Diabetes is one of the most dangerous and common diseases of the modern world. Since medical research usually has limited data available and medical data is very ambiguous, it seems appropriate to use the fuzzy model to find out the relationship between input and output in medical data. None of the previous articles of fuzzy regression have been used to predict complications of diabetes, including nephropathy. Therefore, in this study, a fuzzy regression model was used to predict nephropathy in a diabetic patient.
Methods: In the present study, GFR results of previous patient experiments were used to predict a deeper horizons of GFR and ultimately to predict renal disease. Chronic kidney disease has been stratified based on the amount of GFR, that fuzzy data has been constructed based on these levels. The GFR prediction was performed in the following steps. Step 1: Define fuzzy sets based on the GFR level, which is considered for each level of a fuzzy set. Step 2: Fuzzify patient data Based on fuzzy sets. Step 3: GFR prediction with fuzzy regression model. Step 4: Defuzzifying the predictions. Step 5: Evaluating the model efficiency. The RMSE error is used to compare the performance of the model.
Results: The results of GFR prediction showed that comparison RMSE was 10.09 with using simple linear regression model and 4.24 in fuzzy model.
Conclusions: fuzzy regression model can predict nephropathy in diabetic patients.
Esmail Shekari, Seyed Kianoosh Hosseini, Farideh Razi, Ensieh Nasli Esfahani, Mostafa Qorbani, Bagher Larijani,
Volume 19, Issue 4 (4-2020)
Abstract
Background: Diabetes mellitus is one of the most common endocrine diseases. Cardiovascular disease (CVD) is one of the leading causes of death in patients with type 2 diabetes. The aim of this study was to investigate the metabolic profile of plasma amino acids in diabetic patients with cardiovascular disease.
Methods: The present study is a descriptive-analytical cross-sectional study on 140 patients including 35 patients with type 2 diabetes and cardiovascular disease (CVD.DM), 35 patients with type 2 diabetes and non-cardiovascular disease (DM). 35 non-diabetic patients with cardiovascular disease (CVD.nDM) and 35 non-diabetic patients with non-cardiovascular disease (HS) were referred to Diabetes Clinic No. 1 of Tehran University of Medical Sciences.
Results: 76 (54.3%) were male and 64 (45.7%) were female. The highest concentrations of glutamine and isoleucine were observed in DM.CVD, asparagine, serine, arginine, threonine, alanine, tyrosine, valine in DM.nCVD and methionine in CVD.nDM. The lowest concentrations of tyrosine and tryptophan in DM.CVD has been detected , and methionine has been detected in DM.nCVD. The amino acids alanine, glutamine, tyrosine, valine, methionine, leucine, lysine and arginine significantly increased the chances of developing DM.nCVD. For each increase in Z-score per plasma concentration of isoleucine, the chances of developing cardiovascular disease without diabetes were significantly increased.
Conclusion: The amino acids alanine, glutamine, tyrosine, valine, methionine, leucine, lysine and arginine are involved in predicting the risk of DM.nCVD and isoleucine and methionine are involved in predicting the risk of CVD.nDM.
