The incidence rate of unresponsive thin endometrium in frozen embryo transfer cycles: A case-series of therapy with granulocyte colony stimulating factor

Background: Treatment-resistant thin endometrium (TTE) during in-vitro fertilization is a relatively uncommon and challenging problem. Objective: The primary aim of the study was to assess the TTE rate during frozen embryo transfer (FET) cycles and the secondary aim was to evaluate the effect of intrauterine instillation of granulocyte colony stimulating factor (G-CSF) in these cases. Materials and Methods: In this cross-sectional study, all of the women who underwent FET cycles with hormonal endometrial preparation in Royan Institute from June 2015 to March 2018 were evaluated and all of the cases with TTE diagnosis (endometrial thickness < 7 mm after using high doses of estradiol) were included. In the eligible cases, 300 μgr of G-CSF was infused intrauterine. If the endometrium had not reached at least a 7-mm, a second infusion was prescribed within 48 hr later. Results: During the study, 8,363 of FET cycles were evaluated and a total of 30 infertile patients (0.35%) with TTE diagnosis were detected. Finally, 20 eligible patients were included. The changes of endometrial thickness after G-CSF therapy were significant (p< 0.001); however, the endometrial thickness did not reach 7 mm in nine patients (45%) and the embryo transfer was canceled. Conclusion: It was found that the rate of TTE during the FET cycle is very low and intrauterine perfusion of G-CSF has a potential effect to increase the endometrial thickness in these patients; however, the rate of cancellation was still high and poor pregnancy outcomes were observed. Key words: Granulocyte colony-stimulating factor, Cryopreservation, Embryo transfer, Endometrial diseases

Birth prevalence of genital anomalies among males conceived by intracytoplasmic sperm injection cycles: A cross-sectional study

Background: Several studies have been conducted worldwide to evaluate the prevalence and relative risks of congenital anomalies associated with assisted reproductive technology cycles; however, there is limited data in Iran. Objective: To investigate male genital anomalies among live births from assisted reproductive technology. Materials and Methods: This cross-sectional study was conducted on children born after intracytoplasmic sperm injection (ICSI) at Royan Institute, Tehran, Iran from April 2013-December 2015. The prevalence of male genitalia disorders that included hypospadias, epispadias, cryptorchidism, micropenis, and vanishing testis were reported. The relationship between the cause of infertility and type of embryo transfer (fresh or frozen), gestational age at birth (term or preterm), and birth weight with these male genitalia anomalies were evaluated. Results: In total, 4409 pregnant women were followed after their ICSI cycles to evaluate genitalia anomalies in their children. Out of 5608 live births, 2614 (46.61%) newborns were male, of which 14 cases (0.54%) had genital anomalies. The prevalence of various anomalies were cryptorchidism (0.34%), hypospadias (0.038%), micropenis (0.038%), vanishing testis (0.038%), and epispadias (0.077%). No relationship was found between the cause of infertility, type of embryo transfer (fresh or frozen), gestational age at birth (term or preterm), and male genital malformation (p = 0.33, p = 0.66, and p = 0.62, respectively). Conclusion: The prevalence of each male genital anomaly after the ICSI cycle was rare and less than 0.5%; however, no significant infertility-related factor was observed with these anomalies. Key words: Cryptorchidism, Hypospadias, Microinjections, Prevalence, Reproductive techniques, Urogenital abnormalitie

Gestational Diabetes Mellitus and Metabolic Disorder Among the Different Phenotypes of Polycystic Ovary Syndrome

Objectives: Polycystic ovary syndrome (PCOS) is a common endocrine disorder related to several metabolic consequences. However, there remains uncertainty regarding the metabolic features of various phenotypes. The aim of this study was to explore the relationship between the prevalence of gestational diabetes mellitus (GDM) and metabolic disorders among the four different phenotypes of PCOS. Methods: A cross-sectional study was performed in Royan Institute including 208 pregnant women with a history of infertility and PCOS. Using the diagnostic criteria of the American Diabetes Association (ADA), pregnant women with a documented diagnoses of PCOS were further categorized into four different phenotypes (A, B, C, and D) as defined by the Rotterdam criteria. Results: The prevalence of GDM failed to demonstrate a significant relationship among the four phenotypes of PCOS. The mean levels of fasting blood sugar, plasma glucose concentrations at three hours (following the 100 g oral glucose tolerance test) and triglyceride levels were significantly higher in phenotype B compared to the remaining phenotypes (p < 0.050). There was a statistically significant difference between the mean free testosterone level and phenotypes A and C groups (1.8±1.6 vs. 1.1±1.0, p = 0.003). Conclusions: Women with a known diagnosis of PCOS who exhibited oligo/anovulation and hyperandrogenism demonstrated an increase of metabolic disorders. These results suggest that metabolic screening, before conception or in the early stages of pregnancy, can be beneficial particularly in women with PCOS phenotypes A and B. Early screening and identification may justify enhanced maternal fetal surveillance to improve maternal and fetal morbidity among women affected with PCOS

Health-related quality of life in infertile couples receiving IVF or ICSI treatment

<p>Abstract</p> <p>Background</p> <p>Infertile couples might experience psychological distress and suffer from impaired health-related quality of life. This study aimed to examine health-related quality of life in infertile couples receiving either in-vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) treatment.</p> <p>Methods</p> <p>This was a cross-sectional study of quality of life in infertile couples attending to Vali-e-Asr Reproductive Health Research Center or Royan Institute for either IVF or ICSI treatment in Tehran, Iran. Health-related quality of life was assessed using the Short Form Health Survey (SF-36). Patients' demographic and clinical characteristics were also recorded. Data were analyzed to compare quality of life in infertile women and men and to indicate what variables predict quality of life in infertile couples.</p> <p>Results</p> <p>In all 514 women and 514 men (n = 1028) were studied. There were significant differences between women and men indicating that male patients had a better health-related quality of life. Also health-related quality of life was found to be better in infertility due to male factor. Performing logistic regression analysis it was found that female gender, and lower educational level were significant predictors of poorer physical health-related quality of life. For mental health-related quality of life in addition to female gender and lower educational level, younger age also was found to be a significant predictor of poorer condition. No significant results were observed for infertility duration or causes of infertility either for physical or mental health-related quality of life.</p> <p>Conclusion</p> <p>The findings suggest that infertility duration or causes of infertility do not have significant effects on health-related quality of life in infertile couples. However, infertile couples, especially less educated younger women, are at risk of a sub-optimal health-related quality of life and they should be provided help and support in order to improve their health-related quality of life.</p

Multivariate Gaussian Copula Mutual Information to Estimate Functional Connectivity with Less Random Architecture

Recognition of a brain region&rsquo;s interaction is an important field in neuroscience. Most studies use the Pearson correlation to find the interaction between the regions. According to the experimental evidence, there is a nonlinear dependence between the activities of different brain regions that is ignored by Pearson correlation as a linear measure. Typically, the average activity of each region is used as input because it is a univariate measure. This dimensional reduction, i.e., averaging, leads to a loss of spatial information across voxels within the region. In this study, we propose using an information-theoretic measure, multivariate mutual information (mvMI), as a nonlinear dependence to find the interaction between regions. This measure, which has been recently proposed, simplifies the mutual information calculation complexity using the Gaussian copula. Using simulated data, we show that the using this measure overcomes the mentioned limitations. Additionally using the real resting-state fMRI data, we compare the level of significance and randomness of graphs constructed using different methods. Our results indicate that the proposed method estimates the functional connectivity more significantly and leads to a smaller number of random connections than the common measure, Pearson correlation. Moreover, we find that the similarity of the estimated functional networks of the individuals is higher when the proposed method is used

Multivariate Gaussian Copula Mutual Information to Estimate Functional Connectivity with Less Random Architecture

Recognition of a brain region’s interaction is an important field in neuroscience. Most studies use the Pearson correlation to find the interaction between the regions. According to the experimental evidence, there is a nonlinear dependence between the activities of different brain regions that is ignored by Pearson correlation as a linear measure. Typically, the average activity of each region is used as input because it is a univariate measure. This dimensional reduction, i.e., averaging, leads to a loss of spatial information across voxels within the region. In this study, we propose using an information-theoretic measure, multivariate mutual information (mvMI), as a nonlinear dependence to find the interaction between regions. This measure, which has been recently proposed, simplifies the mutual information calculation complexity using the Gaussian copula. Using simulated data, we show that the using this measure overcomes the mentioned limitations. Additionally using the real resting-state fMRI data, we compare the level of significance and randomness of graphs constructed using different methods. Our results indicate that the proposed method estimates the functional connectivity more significantly and leads to a smaller number of random connections than the common measure, Pearson correlation. Moreover, we find that the similarity of the estimated functional networks of the individuals is higher when the proposed method is used