Appropriate neck/waist circumference cut-off points for gestational diabetes prediction in Iranian pregnant women: The baseline analysis of the Qazvin maternal and neonatal metabolic study (QMNMS), Iran.

BACKGROUND: Gestational diabetes is the most common complication during pregnancy and it is essential to identify the high-risk groups for gestational diabetes mellitus (GDM) in the first trimester. A large neck circumference (NC) in the first trimester is a measure of obesity. OBJECTIVE: The present study investigated whether pregnant women's first-trimester NC and waist circumference (WC) measurements present a predictive index for GDM diagnosis. MATERIALS AND METHODS: This longitudinal cohort study was conducted on 676 pregnant women aged ≥20 years. Pregnant women at 12-14th (baseline) gestational weeks underwent measurement of neck and other anthropometric indices. GDM was diagnosed with 75-g oral glucose tolerance test at 24-28th gestational week. RESULTS: GDM was developed in 110 (16.3%) pregnant women. The logistic regression analysis showed that baseline NC > 33.5 cm (OR: 2.037, 95% CI: 1.313-3.161; P = 0.002) and WC > 90.5 (OR: 2.299, 95% CI: 1.510-3.501; p < 0.001) were independent predictors of GDM. The area under the receiver operating characteristic (ROC) curve analysis of baseline NC and WC for GDM prediction respectively yielded 0.614 (95% CI: 0.558-0.670, p < 0.001) and 0.641 (CI: 0.583-0.698, p < 0.001). The optimal cut-off level of maternal baseline NC was >33.5 cm, with 68.5% sensitivity and 48.3% specificity, and for baseline WC it was >90.5.50 cm, with 57% sensitivity and 63.4% specificity. CONCLUSION: Pregnant women with an NC > 33.5 cm and a WC > 90.5 cm at baseline (12-14th gestational weeks) had a higher chance of developing GDM. first-trimester NC and waist circumference (WC) measurements present a predictive index for GDM diagnosis.

Clinical and Laboratory Findings of Viral Influenza among Children Hospitalized in Qazvin Pediatric Hospital in Iran (2015-2020).

BACKGROUND: Children are one of the most important groups at risk of catching an influenza infection. The consequences of influenza in some children, especially children with chronic and underlying diseases, can be very severe and lead to hospitalization. OBJECTIVE: The purpose of this research was to determine children with influenza and their clinical and laboratory findings in Qazvin Children's Hospital between 2015 to 2020 years. METHODS: In this descriptive cross-sectional study, epidemiological and clinical findings of children hospitalized due to confirmed influenza were considered. A total of 1468 children with a suspected diagnosis of influenza were included in this study. Then, based on the Real-time Polymerase Chain Reaction (PCR), a total of 229 were confirmed positive for influenza. Statistical analysis was done using software SPSS 23.0, Analysis Of Variance (ANOVA), and t-test (p≤0.05). RESULTS: The results showed that most of the patients (53.7 %) were infected with influenza H1N1 type. Most comorbidities were observed with Central Nervous System (CNS) disease and febrile seizure (each one 3.10 %). The highest clinical feature was fever (83.4 %). Significant relationship was observed between the season (p=0.001), soreness in throat (p=0.001), febrile seizure (p=0.051), muscle and joint pain (p=0.059), rhinorrhea (p=0.006) and shiver (p=0.051), and occurrence of influenza. Also, 4 children had died from influenza during hospitalization. CONCLUSION: Children with influenza were examined in this study. Influenza has some side effects on children's health. Due to the irreversible and dangerous effects of influenza, early diagnosis and appropriate treatment in children is important.

Application of Ni0.5Zn0.5Fe2O4 magnetic nanoparticles for diclofenac adsorption: isotherm, kinetic and thermodynamic investigation.

Ni0.5Zn0.5Fe2O4 magnetic nanoparticles were synthesized to obtain a new efficient adsorbent for diclofenac sodium (DF) removal. Fourier Transform Infrared (FTIR), Energy Dispersive Spectrometer (EDS), scanning electron microscope (SEM), Brunauer-Emmett-Teller (BET) and vibrating sample magnetometer (VSM) were applied to characterize the prepared adsorbent. These analyses revealed that adsorbent was successfully prepared with average particle diameter of about 50 nm and a BET surface area of 168.09 m2/g. The saturation magnetization value of magnetic nanoparticles (MNPs) was found to be 24.90 emu/g, thus, adsorbent was efficiently separated from the solution by a facile and rapid magnetic separation process. The effect of adsorption time, amount of adsorbent, initial pH of the solution, initial diclofenac concentration and temperature on the removal of DF were evaluated. Also, the adsorption data were best fitted to the pseudo-first-order kinetic model and Langmuir isotherm model. The thermodynamics studies suggested spontaneous and exothermic adsorption. The maximum diclofenac adsorption amount of the synthesized nanoadsorbent was 52.91 mg/g, which is higher than many recently studied adsorbents.

Association of sleep quality components and wake time with metabolic syndrome: The Qazvin Metabolic Diseases Study (QMDS), Iran.

PURPOSE: The aim of this study was to determine the association of sleep quality and sleep quantity with metabolic syndrome in Qazvin, Iran. METHODS: this cross sectional study was conducted in 1079 residents of Qazvin selected by multistage cluster random sampling method in 2011. Metabolic syndrome was defined according to the criteria proposed by the national cholesterol education program third Adult treatment panel. Sleep was assessed using the Pittsburgh sleep quality index (PSQI). A logistic regression analysis was used to examine the association of sleep status and metabolic syndrome. RESULTS: Mean age was 40.08±10.33years. Of 1079, 578 (52.2%) were female, and 30.6% had metabolic syndrome. The total global PSQI score in the subjects with metabolic syndrome was significantly higher than subjects without metabolic syndrome (6.30±3.20 vs. 5.83±2.76, P=0.013). In logistic regression analysis, sleep disturbances was associated with 1.388 fold increased risk of metabolic syndrome after adjustment for age, gender, and body mass index. CONCLUSION: Sleep disturbances component was a predictor of metabolic syndrome in the present study. More longitudinal studies are necessary to understand the association of sleep quality and its components with metabolic syndrome.