Association of insulin resistance with polycystic ovary syndrome phenotypes and patients' characteristics: a cross-sectional study in Iran.

BACKGROUND: Polycystic ovary syndrome (PCOS) is the most common endocrine disorder in women. This disorder affects 6-15% of women of childbearing age worldwide. It is diagnosed with hyperandrogenism, polycystic ovaries, and chronic anovulation with insulin resistance. This study aimed to assess the prevalence of insulin resistance (IR) in 4 phenotypes of PCOS, and its relationship with demographic, clinical, and paraclinical individual characteristics in a sample of Iranian PCOS patients. METHODS: This particular cross-sectional investigation involved 160 female participants, aged between 18 and 45 years, who were receiving care at gynecology clinics in Urmia, northwestern Iran. All the participants had been diagnosed with PCOS and were categorized into one of four phenotypes. All the participants underwent clinical evaluations, paraclinical assessments, and ultrasound scans. IR was defined as HOMA-IR > 2.5. The statistical significance level was 0.05. RESULTS: Among the 160 participants, the prevalences of the 4 phenotypes were: A: 83 (51.9%), B: 37 (23.1%), C: 21 (13.1%), and D: 19 (11.9%). IR was detected in 119 participants (74.4%); its rate was significantly different between the 4 phenotypes (p-value: 0.008) as A: 62 (74.7%), B: 34 (91.9%), C: 12 (57.1%), D: 11 (57.9%). Linear and logistic regression analyses were performed to control confounding factors. In linear regression, PCOS phenotype, classic phenotype (A&B), economic status, and Hb levels were significantly related to HOMA-IR; in logistic regression Hb levels, exercise, economic status, and PCOS phenotypes were significantly associated with insulin resistance. CONCLUSIONS: The most prevalent PCOS phenotype in this study was A. PCOS phenotypes were significantly related to insulin resistance and HOMA-IR, with the highest levels of insulin resistance and HOMA-IR observed in phenotype B. Determining the phenotype of PCOS may be helpful for better management of PCOS and its associated complications. However, further investigations are recommended in this regard.

Free androgen index (FAI)'s relations with oxidative stress and insulin resistance in polycystic ovary syndrome.

This study aimed to determine the levels of the free androgen index (FAI) and its association with oxidative stress and insulin resistance (IR) in patients with polycystic ovary syndrome (PCOS). This cross-sectional study was performed on 160 women aged 18-45 years, visiting gynecology clinics of Urmia in northwestern Iran during 2020-2021 who were diagnosed with PCOS and exhibited one of the four phenotypes of PCOS. All the participants underwent clinical examinations, paraclinical tests, and ultrasounds. FAI cut-off point was considered to be 5%. The significance level was set at < 0.05. Among the 160 participants, the prevalence of the four phenotypes was as follows: phenotype A: 51.9%, phenotype B: 23.1%, phenotype C: 13.1%, and phenotype D: 11.9%. High FAI was detected in 30 participants (18.75%). Additionally, It was found that phenotype C had the highest FAI levels among the PCOS phenotypes, with a significant difference between phenotypes A and C (p value = 0.03). IR was observed in 119 (74.4%) of the participants, and the median (interquartile range: IQR) of malondialdehyde (MDA) levels among the participants was 0.64 (0.86) µM/L. In linear regression, the PCOS phenotype (standard beta = 0.198, p-value = 0.008), follicle-stimulating hormone (FSH) levels (standard beta = 0.213, p-value = 0.004), and MDA levels (standard beta = 0.266, p-value < 0.001) were significantly related to the FAI level, but the homeostatic model assessment for insulin resistance (HOMA-IR) was not statistically associated with FAI. Thus, in this study, PCOS phenotypes and MDA levels (an indicator of stress oxidative) were significantly related to FAI, but HOMA-IR (the indicator of IR) was not associated with it.

Effectiveness of a smartphone-based educational intervention to improve breastfeeding.

BACKGROUND: Exclusive breastfeeding (EBF) is essential during the first six months of life and confers countless benefits to mothers and infants. This study aimed to assess the effectiveness of a smartphone-based educational intervention to improve new mothers' breastfeeding for infants younger than six months of age in Urmia, Iran. METHODS: A randomized controlled trial study was conducted from January to December 2019 with 40 new mothers and their first child aged < 3 months, assigned to the intervention (mobile app education + routine care) and control groups (routine care). The mean age of infants was 1.25 and 0.98 months for each group consequently. The designed app content categorized according to seven sections (the importance of breastfeeding, behavioral methods, complementary feeding and EBF, pumping and manual expression, managing common breast-related and breastfeeding problems, breastfeeding tips in special situations, and common queries) for educating the required knowledge to nursing mothers. RESULTS: Forty mothers were assessed for primary outcomes in each group. At three months, the mothers' knowledge, attitude, and practice (KAP) had meaningful differences in the intervention group compared to the control group. In the intervention group, the degree of changes in knowledge and attitude were 5.67 ± 0.94 and 8.75 ± 1.37 respectively more than the control group (p < 0.001, p < 0.001). However, this amount for the practice score was 0.8 ± 0.49 which is considered to be marginally significant (p = 0.063). During the study, the mothers' breastfeeding self-efficacy showed significant progress in favor of the intervention group. The score enhancement was 26.85 ± 7.13 for the intervention group and only 0.40 ± 5.17 for the control group that was confirmed to be significant (p < 0.001). CONCLUSION: The smartphone-based app for educating new mothers on breastfeeding had a significantly positive effect on breastfeeding self-efficacy and maternal KAP. In future studies, the intervention can be tested in both prenatal and postpartum periods.

Multiple-source models for electron beams of a medical linear accelerator using BEAMDP computer code.

AIM: The aim of this work was to develop multiple-source models for electron beams of the NEPTUN 10PC medical linear accelerator using the BEAMDP computer code. BACKGROUND: One of the most accurate techniques of radiotherapy dose calculation is the Monte Carlo (MC) simulation of radiation transport, which requires detailed information of the beam in the form of a phase-space file. The computing time required to simulate the beam data and obtain phase-space files from a clinical accelerator is significant. Calculation of dose distributions using multiple-source models is an alternative method to phase-space data as direct input to the dose calculation system. MATERIALS AND METHODS: Monte Carlo simulation of accelerator head was done in which a record was kept of the particle phase-space regarding the details of the particle history. Multiple-source models were built from the phase-space files of Monte Carlo simulations. These simplified beam models were used to generate Monte Carlo dose calculations and to compare those calculations with phase-space data for electron beams. RESULTS: Comparison of the measured and calculated dose distributions using the phase-space files and multiple-source models for three electron beam energies showed that the measured and calculated values match well each other throughout the curves. CONCLUSION: It was found that dose distributions calculated using both the multiple-source models and the phase-space data agree within 1.3%, demonstrating that the models can be used for dosimetry research purposes and dose calculations in radiotherapy.