The association between dietary antioxidant quality score and intensity and frequency of migraine headaches among women: a cross-sectional study

Background: Migraine is an episodic disorder and a frequent form of headache. An impaired balance between free radical production and an impaired antioxidant defense system leading to oxidative damage may play a major role in migraine etiology. We sought to investigate whether dietary antioxidant quality score (DAQS) is associated with migraine intensity and frequency among women suffering from migraine. Methods: This cross-sectional study was conducted on 265 women. The data related to anthropometric measures and dietary intake were collected. DAQS score was calculated based on FFQ (food frequency questionnaire) vs. the reference daily intake (RDI) quantity. To measure migraine intensity, the migraine disability assessment questionnaire (MIDAS) and visual analog scale (VAS) were used. The frequency of headaches was defined as the days the participants had headaches in the last month and a 30-day headache diary was used. Results: The results of the study demonstrated that VAS, MIDAS, and frequency of headaches were reduced significantly from the low DAQS (poor quality of antioxidants) to high DAQS (high quality of antioxidants) after adjusting covariates. Also, multinomial regression showed there was an inverse association between higher DAQS and the frequency of headaches. In the adjusted model, subjects with the higher DAQS were 69% less likely to have moderate migraine disability, compared with those with the lower DAQS. Linear regression showed, there was an inverse association between vitamin C intake and the grades of pain severity.َAlso in a crude model, a negative association was found between vitamin E and the frequency of headaches. Conclusion: In conclusion, Participants with higher DAQS had lower migraine intensity and headache frequency. In addition, the consumption of vitamin C may potentially associate with decreasing the severity of headaches. Dietary antioxidants should be monitored closely in individuals suffering from migraine

Surface functionalization of anodized tantalum with Mn3O4 nanoparticles for effective corrosion protection in simulated inflammatory condition

| openaire: EC/H2020/860462/EU//PREMUROSA This study has received funding from the European Union's Horizon 2020 Research and Innovation Program under grant agreement no. 860462 for “PREMUROSA” project (A.B–K., M.G.). The fabrication of the anodic and EPD coatings was performed at Materials and Energy Research Center, Tehran, Iran. The authors are willing to express their gratitude for this support.The study highlights the corrosion behavior of untreated and treated tantalum with addition of trimanganese tetraoxide (Mn3O4) nanoparticles in simulated inflammatory media. The anodic layer was produced on pure tantalum by anodization in electrolytes composed of ammonium fluoride, ethylene glycol, and water. Nanoparticles were deposited uniformly on the surface of the anodized tantalum with the electrophoretic deposition (EPD) method. The results revealed that the anodic/EPD coating possessed more compact microstructure and higher bond strength than the anodic coating. Simulated inflammatory medium was based on phosphate-buffered saline with additions of H2O2 and HCl. Potentiodynamic polarization and electrochemical impedance spectroscopy studies showed that the anodic and Mn3O4 layers protected the tantalum from corroding in an acidic inflammatory condition. Finally, the corrosion protection mechanism of Mn3O4 NPs in inflammatory condition was presented.Peer reviewe

Evaluation of Four Multiple Imputation Methods for Handling Missing Binary Outcome Data in the Presence of an Interaction between a Dummy and a Continuous Variable

Multiple imputation by chained equations (MICE) is the most common method for imputing missing data. In the MICE algorithm, imputation can be performed using a variety of parametric and nonparametric methods. The default setting in the implementation of MICE is for imputation models to include variables as linear terms only with no interactions, but omission of interaction terms may lead to biased results. It is investigated, using simulated and real datasets, whether recursive partitioning creates appropriate variability between imputations and unbiased parameter estimates with appropriate confidence intervals. We compared four multiple imputation (MI) methods on a real and a simulated dataset. MI methods included using predictive mean matching with an interaction term in the imputation model in MICE (MICE-interaction), classification and regression tree (CART) for specifying the imputation model in MICE (MICE-CART), the implementation of random forest (RF) in MICE (MICE-RF), and MICE-Stratified method. We first selected secondary data and devised an experimental design that consisted of 40 scenarios (2 × 5 × 4), which differed by the rate of simulated missing data (10%, 20%, 30%, 40%, and 50%), the missing mechanism (MAR and MCAR), and imputation method (MICE-Interaction, MICE-CART, MICE-RF, and MICE-Stratified). First, we randomly drew 700 observations with replacement 300 times, and then the missing data were created. The evaluation was based on raw bias (RB) as well as five other measurements that were averaged over the repetitions. Next, in a simulation study, we generated data 1000 times with a sample size of 700. Then, we created missing data for each dataset once. For all scenarios, the same criteria were used as for real data to evaluate the performance of methods in the simulation study. It is concluded that, when there is an interaction effect between a dummy and a continuous predictor, substantial gains are possible by using recursive partitioning for imputation compared to parametric methods, and also, the MICE-Interaction method is always more efficient and convenient to preserve interaction effects than the other methods