Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
Browse
2 results
Search Results
Article Citation - WoS: 8Citation - Scopus: 10Lung Cancer Subtype Differentiation From Positron Emission Tomography Images(Tubitak Scientific & Technological Research Council Turkey, 2020-01-27) Ayyildiz, Oguzhan; Aydin, Zafer; Yilmaz, Bulent; Karacavus, Seyhan; Senkaya, Kubra; Icer, Semra; Kaya, Eser; Taşdemir, ArzuLung cancer is one of the deadly cancer types, and almost 85% of lung cancers are nonsmall cell lung cancer (NSCLC). In the present study we investigated classification and feature selection methods for the differentiation of two subtypes of NSCLC, namely adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). The major advances in understanding the effects of therapy agents suggest that future targeted therapies will be increasingly subtype specific. We obtained positron emission tomography (PET) images of 93 patients with NSCLC, 39 of which had ADC while the rest had SqCC. Random walk segmentation was applied to delineate three-dimensional tumor volume, and 39 texture features were extracted to grade the tumor subtypes. We examined 11 classifiers with two different feature selection methods and the effect of normalization on accuracy. The classifiers we used were the k-nearest-neighbor, logistic regression, support vector machine, Bayesian network, decision tree, radial basis function network, random forest, AdaBoostM1, and three stacking methods. To evaluate the prediction accuracy we performed a leave-one-out cross-validation experiment on the dataset. We also considered optimizing certain hyperparameters of these models by performing 10-fold cross-validation separately on each training set. We found that the stacking ensemble classifier, which combines a decision tree, AdaBoostM1, and logistic regression methods by a metalearner, was the most accurate method for detecting subtypes of NSCLC, and normalization of feature sets improved the accuracy of the classification method.Article Citation - Scopus: 13Paclitaxel-Loaded Polycaprolactone Nanoparticles for Lung Tumors: Formulation, Comprehensive In Vitro Characterization, and Release Kinetic Studies(University of Ankara, 2022-09-29) Ünal, Sedat; Dogan, Osman Talha; Aktaş, YeşimObjective: Today, cancer is still among the most common chronic diseases. Nanoparticular drug delivery systems prepared with biocompatible and biodegradable polymers such as polycaprolactone are rational solution for anticancer agents with poor solubility and low bioavailability. The aim of this study is to prepare paclitaxel-loaded polycaprolactone nanoparticles, which is known to be a potent anticancer, and to elucidate in vitro characteristics and release kinetic mechanisms. Material and Method: It was aimed to prepare paclitaxel-loaded polycaprolactone nanoparticles by nanoprecipitation. Preformulation studies were carried out with different molecular weights of polycaprolactone (Mw: 14.000, Mw: 80.000). Nanoparticles were coated with Chitosan or Poly-l-lysine to obtain cationic surface charge and to increase cellular interaction. Comprehensive characterization of formulations and release kinetic studies were performed. Result and Discussion: The particle size of the formulations ranged from 188 nm to 383 nm. Encapsulation efficiency increased to 77% in different formulations. SEM analysis confirmed the nanoparticles were spherical. Within the scope of in vitro release studies, the release continued for up to 96 hours and less than 50% of the therapeutic load was released in the first 24 hours. Mathematical modeling indicated that the release kinetics fit more than one model with the Korsmeyer-Peppas, Peppas-Sahlin and Weibull models, which show high correlation. © 2023 Elsevier B.V., All rights reserved.