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Background: Heart disease, particularly coronary heart disease is a major cause of mortality among patients with diabetes mellitus. High level of hs-CRP is considered as a risk factor for heart diseases. Treatment with pioglitazone in patients with or without type II diabetes decreases serum concentrations of hs-CRP. The purpose of this study was to compare the effects of pioglitazone and metformin on hs-CRP level. Material and methods: A randomized clinical trial was performed on 40 patients with type 2 diabetes and defined inclusion criteria. Patients were divided into two groups receiving metformin and pioglitazone. Blood levels of cholesterol, triglycerides, fasting blood glucose, Alanine transaminase, Aspartat aminotransferase, HbA1C and hs-CRP were measured in all subjects before and after 3 month drug therapy. The average change in each group before and after drug therapy were analyzed by paired T-test, the mean change between groups were compared by T-test. Results: In both groups, hs-CRP level was significantly decreased after drug therapy. The mean change of hs-CRP, HbA1C, cholesterol was significantly higher in metformin treated group. Conclusion: Since the average reduction in the level of hs-CRP, cholesterol and HbA1C in diabetic patients treated with metformin is significantly higher than patients treated with pioglitazone treatment with metformin is recommended to reduce risk of heart disease

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Introduction: Appendicitis is considered as one of the major causes leading to acute abdomen and as one of the most prevalent cases in urgent need of surgery. Although diagnosis of acute appendicitis is based on the accounts, clinical examinations and laboratory results, but making any certain diagnosis often seems to be quite difficult, even for experienced surgeons. This problem is more appreciable/ discernible concerning some groups like pregnant women, children/infants, and aged people. So, there has been always a constant effort to achieve inexpensive and feasible/available methods for improving the process of diagnosis.

Materials & methods: All the patients who were suspected of having acute appendix, operated in the emergency of Be'sat Hospital in Hamadan during six months ago, have been checked. 5 cc peripheral blood has been obtained from each of those hospitalized patients whom were suspected of having acute appendicitis and supposed to have surgery afterwards. Serum D-Lactae level measured using Spectrophotometric method. Serum level of above 0.25 mg in deciliter has been considered for patients whom their test-result was positive.

Findings: In acute appendicitis diagnosis, the sensitivity of D-Lactae measurement was (79%) and specificity (58%) for all patients, positive predictive value (87%) and negative predictive value (43%) as the result of the test have been calculated. The accuracy of the test was 74%.

Discussion & Conclusions: Serum lactate test could be an influential aid in diagnosis of acute appendicitis. This test is quite inexpensive and widely available. Besides, the method of measurement is simple. Test sensitivity, in all age groups, is high; positive predictive value of the test, in comparison with other laboratory tests, is high. Accuracy of the test is also high and is comparable to other laboratory tests.

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Introduction: Most of the newborns’ diseases transfer to them through their mothers. Cytomegalovirus (CMV) is the most prevalence infections which transfer from mother to embryo. CMV is the cause of congenital infection and is known as spontaneous abortion in mothers. This virus can cross the placenta and cause clinical signs in embryo and infant. The aim of this study was to develop a PCR and Real Time PCR for diagnosis of Cytomegalovirus using molecular tests.

Materials & methods: In this study, out of 100 infants with clinical signs (under 3 weeks) who referred to Qom and were suspected to infection with CMV. The urine samples were studied for existence of DNA for using PCR and Real Time PCR methods and using specialized primers and probe.

Findings: Out of 100 newborn, DNA of CMV was excreted through urine. 58 newborns have CMV DNA in their urines. The results with 2 methods have similar PCR and Real Time PCR. The CMV DNA was not found in 19 newborns.

Discussion & conclusions: The results show that the molecular tests are influential for fast diagnosis especially tracking viral DNA using PCR method. Also on the strength of this research, the importance of this study and submitting solutions for fast and accurate diagnosis of this Virus in newborns immediately after delivery is necessary for treatment and preventing this Virus.

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Introduction: Urinary incontinence (UI) is a global problem that affects physical - emotional and social lives of women.The aim of this study was to evaluate the prevalence and demographic and obstetric predisposing factors of urinary incontinence in reproductive age women.
 
 Materials & Methods: This cross sectional study was conducted on 2000 reproductive age women. The women were selected randomly from all of the primary health care centers of Dezful. Data were collected by socio demographic and ICIQ questionnaires.
 
Findings: Among participants, 57/7 % of the patients had urinary incontinence. There was a significant association between age, BMI, education level, mean of pregnancy, mean of deliveries, mode of delivery, abortion, neonate>4 kg, irregular menstruation, and UI(P<0/05). Multivariate analysis using logistic regression showed that age, C/S, and irregular menstruation were the factors affecting urinary incontinence (P<0/05).
 
Discussion & Conclusions: The prevalence of urinary incontinence in reproductive age women is high. It is suggested that the health care providers pay more attention to this issue in health care centers and train women in the prevention of urinary incontinence.

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Introduction: Some Azotobacter species can produce melanin. Melanin protects against UV. The purpose of this study was to isolate Azotobacter with the power of producing melanin and optimize its production and the effect of its combination with titanium oxide nanoparticles on the protection against ultraviolet light (UV).
 
Materials & Methods: Overall, 200 soil samples from different parts of Qom, Iran, were cultured on Ashby's mannitol agar medium using Bio-test method. The effect of optimization of conditions such as pH, temperature, glucose source, amino acid sources, and aeration on the amount of melanin production was assessed. Afterwards, melanin was extracted, conjugated with titanium nanosilicate compound, and then the effect of each of the compounds on Escherichia coli was examined under the influence of UV based on time.
 
Findings: Melanin-producing Azotobacter vinelandii species were isolated. The optimum pH was determined about 7.5. Glucose at different concentrations (1-6%) reduced the production of the product. Methionine amino acid had an inhibitory effect, while tyrosine amino acid increased the production rate. Tryptophan amino acid at 0.1-0.7% also increased the production rate, while cysteine and phenylalanine did not change the production rate. The combination of titanium oxide with melanin showed a significant protective effect against Escherichia coli bacterium under the influence of UV.
 
Discussion & Conclusions: Native isolates of Azotobacter isolated from agricultural soil with the power of producing melanin can be used as a source for its use in the industry.

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Introduction: Student academic failure is one of the shortcomings of higher education system. A system with the ability to predict and prevent the academic failure can play a decisive role in enhancing the quality of education. The utilization of educational data mining is one of the most effective strategies in monitoring and improving the quality of education. This study aimed to compare different data mining techniques in order to determine the best prediction model.
 
Materials & Methods: The study population consists of all undergraduate students studying Medical Laboratory Sciences, Health Information Technology Management, and Radiation Sciences Technology in Tehran University of Medical Sciences, Tehran, Iran in the academic year of 2018-2019. In total, 500 were selected to participate in this study. After administering and excluding the invalid questionnaires, 153, 85, and 73 questionnaires from the students of Medical Laboratory Sciences, Health Information Technology Management, and Radiation Sciences Technology were analyzed, respectively. The data were collected using students' academic failure questionnaire with a reliability Cronbach's alpha coefficient of 0.971 and confirmed content validity by experts. Furthermore, the data were analyzed in SPSS (version 16), SmartPLS 3, and SPSS-Modeler (Clementine) (version 18) software through logistic regression, decision tree, neural network, Bayesian network, and the support vector machine algorithms. Ethics code: IR.UT.PSYEDU.REC.1398.024
 
Findings: The results showed a significant difference among the majors in terms of the level of academic failure (P=0.046). Regarding the Medical Laboratory Sciences, the descending order of the accuracy of the data prediction model using each algorithm includes decision tree and Bayesian network, support vector machine, neural network, and logistic regression. In addition, considering the Health Information Technology Management and Radiation Sciences Technology, the best prediction models were Bayesian Network, Support Vector Machine, Neural Network, and logistic regression in a descending order. It is worth mentioning that the decision tree algorithm had no answer. Furthermore, the Chi-square test showed a statistically significant difference among the students of Medical Laboratory Sciences (0.88), Health Information Technology Management (0.001), and Radiation Science Technology (0.000) regarding the obtained algorithms. 
 
Discussion & Conclusions: The results indicated that the logistic regression model had less predictive power in real data, compared to the network models. Therefore, the appropriate data prediction algorithm must be explored in any dataset. Predictive algorithms are well capable to predict academic failure in all three majors at the departments of allied medical sciences. Moreover, it can be used for medical and allied medical science students to identify students at the risk of failing to take preventive measures.
 

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