Yazar "Iyit, Neslihan" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    Association of Apolipoprotein E Polymorphism with Intravitreal Ranibizumab Treatment Outcomes in Age-Related Macular Degeneration
    (Taylor & Francis Inc, 2016) Bakbak, Berker; Ozturk, Banu Turgut; Zamani, Ayse Gul; Gonul, Saban; Iyit, Neslihan; Gedik, Sansal; Yildirim, M. Selman
    Purpose: Genetic factors are known to influence the response to anti-vascular endothelial growth factor (VEGF) treatment in exudative age-related macular degeneration (AMD). The current study was conducted to investigate the association of Apolipoprotein E (ApoE) polymorphism with the treatment response to ranibizumab for exudative AMD.Methods: One hundred nine eyes (109 patients, 59.6% male, mean age 63.847.22 years) treated with intravitreal ranibizumab injections were included in the analysis. Smoking status and lesion type were recorded. Patients were categorized into three groups according to visual acuity (VA) change at 6 months after the first injection: VA loss >5 Early Treatment Diabetic Retinopathy Study (ETDRS) letters (Group 1); VA change between five ETDRS letters gain and loss (Group 2); VA improvement >5 ETDRS letters (Group 3). The association of ApoE gene polymorphisms with the three groups was evaluated.Results: Both smoking status and lesion type showed no significant association with VA change (p=0.12 and p=0.64, respectively). A lower frequency of 2 and a higher frequency of 4 were observed in Group 3 (2.9 and 25.7%, respectively). VA improvement with more than five ETDRS letters was significantly associated with the presence of the 4 genotype (p=0.01).Conclusions: This study demonstrated that carriers of the ApoE 4 polymorphism genotype show demonstrable improvement in VA after treatment with ranibizumab in exudative AMD. ApoE polymorphism identification may be used as a genetic screening to tailor individualized therapeutic approach for optimal treatment in neovascular AMD.
  • Küçük Resim Yok
    Öğe
    Investigating the impact of CO2 emissions on the COVID-19 pandemic by generalized linear mixed model approach with inverse Gaussian and gamma distributions
    (De Gruyter Poland Sp Z O O, 2023) Iyit, Neslihan; Sevim, Ferhat; Kahraman, Umran Munire
    Carbon dioxide (CO2) rate within the atmosphere has been rising for decades due to human activities especially due to usage of fuel types such as coal, cement, flaring, gas, oil, etc. Especially in 2020, COVID-19 pandemic caused major economic, production, and energy crises all around the world. As a result of this situation, there was a sharp decrease in the global CO2 emissions depending on the fuel types used during this pandemic. The aim of this study was to explore the effects of CO2 emissions due to the fuel types on percentage of deaths in total cases attributed to the COVID-19 pandemic using generalized linear model and generalized linear mixed model (GLMM) approaches with inverse Gaussian and gamma distributions, and also to obtain global statistical inferences about 169 World Health Organization member countries that will disclose the impact of the CO2 emissions due to the fuel types during this pandemic. The response variable is taken as percentage of deaths in total cases attributed to the COVID-19 pandemic calculated as (total deaths/total confirmed cases attributed to the COVID-19 pandemic until December 31, 2020)*100. The explanatory variables are taken as production-based emissions of CO2 from different fuel types, measured in tonnes per person, which are coal, cement, flaring, gas, and oil. As a result of this study, according to the goodness-of-fit test statistics, GLMM approach with gamma distribution called gamma mixed regression model is determined as the most appropriate statistical model for investigating the impact of CO2 emissions on the COVID-19 pandemic. As the main findings of this study, 1 t CO2 emissions belonging to the fuel types cement, coal, flaring, gas, and oil per person cause increase in deaths in total cases attributed to the COVID-19 pandemic by 2.8919, 2.6151, 2.5116, 2.5774, and 2.5640%, respectively.
  • Küçük Resim Yok
    Öğe
    Investigating the impact of CO2 emissions on the COVID-19 pandemic by generalized linear mixed model approach with inverse Gaussian and gamma distributions
    (De Gruyter Poland Sp Z O O, 2023) Iyit, Neslihan; Sevim, Ferhat; Kahraman, Umran Munire
    Carbon dioxide (CO2) rate within the atmosphere has been rising for decades due to human activities especially due to usage of fuel types such as coal, cement, flaring, gas, oil, etc. Especially in 2020, COVID-19 pandemic caused major economic, production, and energy crises all around the world. As a result of this situation, there was a sharp decrease in the global CO2 emissions depending on the fuel types used during this pandemic. The aim of this study was to explore the effects of CO2 emissions due to the fuel types on percentage of deaths in total cases attributed to the COVID-19 pandemic using generalized linear model and generalized linear mixed model (GLMM) approaches with inverse Gaussian and gamma distributions, and also to obtain global statistical inferences about 169 World Health Organization member countries that will disclose the impact of the CO2 emissions due to the fuel types during this pandemic. The response variable is taken as percentage of deaths in total cases attributed to the COVID-19 pandemic calculated as (total deaths/total confirmed cases attributed to the COVID-19 pandemic until December 31, 2020)*100. The explanatory variables are taken as production-based emissions of CO2 from different fuel types, measured in tonnes per person, which are coal, cement, flaring, gas, and oil. As a result of this study, according to the goodness-of-fit test statistics, GLMM approach with gamma distribution called gamma mixed regression model is determined as the most appropriate statistical model for investigating the impact of CO2 emissions on the COVID-19 pandemic. As the main findings of this study, 1 t CO2 emissions belonging to the fuel types cement, coal, flaring, gas, and oil per person cause increase in deaths in total cases attributed to the COVID-19 pandemic by 2.8919, 2.6151, 2.5116, 2.5774, and 2.5640%, respectively.