Solid lipid nanoparticles (SLN) were recently proposed as carriers for various pharmaceutical and cosmetic actives. These lipid nanoparticles can act as moisturizers and physical sunscreens on their own. Therefore, the full potential of these carriers has yet to be determined. The present study was aimed to determine and compare moisturizing and UV-protecting effects of different solid lipid nanoparticles (SLN) prepared by different solid lipids including Glyceryl monostearate (GMS), Precirol® (P) and cetyl palmitate (CP) as carrier systems of moisturizers and sunscreens. The influence of the size and matrix crystallinity of the solid lipids on the occlusive factor, skin hydration and UV-protection were evaluated by in vitro and in vivo methods. The SLN were prepared by high-shear homogenization and ultrasound methods. Size, zeta potential and morphological characteristics of the samples were assessed by transmission electron microscopy (TEM) and thermotropic properties with differential scanning calorimetry (DSC) technique. Results of the assessments showed that SLN-CP significantly increases skin hydration and UV-protection, compared to SLN-GMS and SLN-P. It was demonstrated that the size of SLN, crystallinity index of solid lipid in SLN and probably other mechanisms besides the occlusive factor can influence skin hydration and UV-protection indices. Furthermore, findings of the assessments demonstrated significant difference between in vitro and in vivo assessments regarding occlusive factor and moisturizing effects. Findings of the present study indicate that the SLN-CP could be a promising carrier for sunscreens and moisturizers.
Introduction:Breast cancer is the most commonly diagnosed cancers in women worldwide and in Iran. It is expected to account for 29% of all new cancers in women at 2015. This study aimed to assess the 5 years and lifetime risk of breast cancer according to Gail model, and to evaluate the effect of other additional risk factors on the Gail risk.Materials and Methods:A cross sectional study conducted on 296 women aged more than 34-year-old in Qom, Center of Iran. Breast Cancer Risk Assessment Tool calculated the Gail risk for each subject. Data were analyzed by paired t-test, independent t-test, and analysis of variance in bivariate approach to evaluate the effect of each factor on Gail risk. Multiple linear regression models with stepwise method were used to predict the effect of each variable on the Gail risk.Results:The mean age of the participants was 47.8 ± 8.8-year-old and 47% have Fars ethnicity. The 5 years and lifetime risk was 0.37 ± 0.18 and 4.48 ± 0.925%, respectively. It was lower than the average risk in same race and age women (P < 0.001). Being single, positive family history of breast cancer, positive history of biopsy, and radiotherapy as well as using nonhormonal contraceptives were related to higher lifetime risk (P < 0.05). Moreover, a significant direct correlation observed between lifetime risk and body mass index, age of first live birth, and menarche age. While an inversely correlation observed between lifetimes risk of breast cancer and total month of breast feeding duration and age.Conclusion:Based on our results, the 5 years and lifetime risk of breast cancer according to Gail model was lower than the same race and age. Moreover, by comparison with national epidemiologic indicators about morbidity and mortality of breast cancer, it seems that the Gail model overestimate the risk of breast cancer in Iranian women.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.