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1.
Article | IMSEAR | ID: sea-184393

ABSTRACT

Background: - It is important to estimate in females the level of serum thyroid hormones throughout infertility to avoid its existence later on. The aim in this study, was to estimate thyroid function tests in infertile patients with menstrual irregularities. Methodology: - This case control study was performed in 90 infertile patients with menstrual irregularities and 50 healthy matched women. Biochemical parameters were measured was serum hormones triiodothyronine (T3), tetraiodothyronine (T4) and serum thyroid stimulating hormone (TSH). Result: - Serum T3, T4 and TSH level were statistically significant in infertile women. Serum thyroid stimulating hormone level was found at higher side in infertile women. Conclusion: - This study concludes that, high occurrence of hypothyroidism was found in infertile women and it shows a positive correlation with menstrual disorder.

2.
Article | IMSEAR | ID: sea-184095

ABSTRACT

Background: Oxidative stress is the topic of interest for most of the researches, as it is associated with the pathogenesis of a number of disorders. Pregnancy though a natural phenomenon is associated with increased susceptibility to oxidative stress. Also, pregnant women are more vulnerable to the nutritional deficiency disorders, most common being iron deficiency anemia. Both oxidative stress and anemia have become a potential threat to both mother and fetus. Therefore, the aim of our study was to determine the association of oxidative stress with anemia in pregnancy. Methods: This study was conducted in Jawahar Lal Nehru Medical College, Aligarh with inclusion of 25 healthy pregnant (Control) and 50 anemic pregnant women. Blood samples were collected and the parameters like Hemoglobin (Hb), malondialdehyde (MDA), Ferritin reducing ability of plasma (FRAP), Total peroxide (TP) and Oxidative stress index (OSI) were estimated. Results: We found that the levels of Hb and FRAP were significantly low and that of MDA, TP and OSI were significantly higher in the anemic pregnant women. Hb was positively correlated with FRAP but negatively with MDA, TP and OSI. The results were statistically significant (p<0.05). Conclusions: Anemia when occurs in disturbs the oxidant- antioxidant balance which further adds to the pre-existing pro-oxidant cellular environment induced by pregnancy. Therefore, in order to prevent both maternal and fetal complications further evaluations are needed.

3.
Article in English | IMSEAR | ID: sea-177695

ABSTRACT

Background: When actual measurement of stature cannot be done, other surrogate parameters can be used to predict stature. Stature in clinical medicine and in the field of scientific research can be easily estimated using various anthropometric parameters like arm span, knee height, foot length and breadth etc. Arm span has been proven to be one of the most reliable predictors. Thus this study was undertaken to estimate the stature from arm span using regression equation and to determine correlation between stature and arm span. Methods: The present study was carried out in the Department of Anatomy, Hind Institute of Medical Sciences, Ataria, UP. Our study consisted of 124 MBBS students (85 boys and 39 girls). The stature and arm span was measured directly using anthropometric technique and measuring tape. The data collected were recorded and analysed with SPSS 16. Regression equations were derived for stature estimation and the relationship between stature and arm span was determined by Pearson correlation coefficient. Results: We found that the males with stature 171.34±9.71cm had the arm span of 174.27±8.63cm and the females with stature 159.41±6.33cm had the arm span of 156.47±7.85cm. The stature calculated using regression equation was 171.25±6.69cm in male and 159.25±4.95cm in females. The correlation between stature and arm span was positive and significant (r=0.689 for male, r=0.783 for female, p<0.05). Conclusion: Body height correlates well with the arm span so it can be used as a reliable marker for stature estimation using regression equation.

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