Kinetic model of photosensitized homolysis of erythrocytes: multihit target theory

Fotosensitize edilmis eritrositlerdeki hemoliz hız kinetigini örnek sistem olarak kullanarak, hücre zarındaki fotosensitizasyon mekanizmasının açıklanması amaçlanmıstır. Fotohemolizin ısıga baglı olusan hasar (fotokimyasal safha) ve termal aktivasyonun (termal safha) birlikte olan etkisiyle olustugunu kabul eden “Çok Vuruslu HedefTeori” 'de; her safhadaki kinetik düzen özel vuru sayıları ile belirlenebilmektedir. Fotohemoliz hızı formülüyle hesaplanmıs olup, sistemde %50 hemoliz olması için gerekli olan karanlık inkübasyon zamanını, uygulanan ısık dozunu, protoporfirin konsantrasyonu , reaksiyon sabitini, ve ise ölçülen üssel degerleri belirtmektedir. Deneyde, pH 7.4, 10 mM tuzlu fosfat tamponda hazırlanan insan eritrositleri degisik konsantrasyonlarda protoporfirin IX ile fotosensitif hale getirilmis ve ısıga maruz bırakılarak gecikmis fotohemoliz ölçümleri yapılmıstır. Ayrıca gecikmis fotohemoliz verileri “ÇokVuruslu HedefTeori” kullanılarak incelenmistir. Fotohemoliz egrileri s-seklinde olup, düsük protoporfirin konsantrasyonu ve ısınlama zamanında t degeri daha uzamıs olarak ölçülmüstür. Gecikmis fotohemoliz ölçümlerinde, fotohemoliz hızının sogurulan ısınımın karesiyle orantılı oldugu belirlenmistir. Deneysel ve modelle hesaplanan fotohemoliz egrileri uyum içindedir. “Çok Vuruslu Hedef Teori” ile, fotohemoliz sonuçlarının karakterize edilmesi ve karsılastırması açısından önemli oldugu gösterilmistir. Bu kinetik modelle belirlenen degisik konsantrasyonda fotosensitif ajan ve ısık dozunun fotohemoliz egrileri üzerine olan etkisinin, ölçülen deneysel verilerle uyum içinde olması ile “ÇokVuruslu HedefTeori” desteklenmektedir.By using rate kinetics of photosensitized hemolysis of erythrocyte as a model system, understanding the mechanism of photosensitization on the cell membrane was purposed in this work. Photohemolysis required the combined effect of the light activated (photochemical stage) and thermal (thermal stage) process, and these stages can be represented by “MultihitTarget Theory”, defined with photochemical and thermal hit numbers. Photohemolysis rate was calculated by using where is the dark incubation time required for 50% hemolysis, L is the incident light dose, is the bound dye concentration, and are the “as measured” exponents, and g is the reaction constant. Erythrocyte suspension, which was prepared in pH 7.4 10 mM phosphate buffered saline, was photosensitized with various concentration of protoporphyrin IX and was irradiated by visible light. Then, delayed photohemolysis was measured for each sample, and data were analyzed using “MultihitTarget Theory”. Prolonged t values were measured on delayed photohemolysis curve (s-shaped) with low protoporphyrin IX concentration and irradiation time. Delayed photohemolysis measurements are indicative of second power dependence of the photohemolysis rate on the absorbed light energy. Photohemolysis data obtained from experiments and kinetic model calculations were in good agreement. “Multihit Target Theory” is important for characterizing and comparing photohemolysis results. The effects of various concentrations of photosensitizers and light doses on photohemolysis curve were analyzed with kinetic model. Thus, experimental data were in good agreement with recent kinetic model, based on “MultihitTarget Theory”

Sheet metal forming analyses with an emphasis on the springback deformation

An accurate modeling of the sheet metal deformations including the springback is one of the key factors in the efficient utilization of FE process simulation in the industrial setting. In this paper, a rate-independent anisotropic plasticity model accounting the Bauschinger effect is presented and applied in the FE forming and springback analyses. The proposed model uses the Hill's quadratic yield function in the description of the anisotropic yield loci of planar and transversely anisotropic sheets. The material strain-hardening behavior is simulated by an additive backstress form of the nonlinear kinematic hardening rule and the model parameters are computed explicitly based on the stress-strain curve in the sheet rolling direction. The proposed model is employed in the FE analysis of Numisheet'93 U-channel benchmark, and a performance comparison in terms of the predicted springback indicated an enhanced correlation with the average of measurements. In addition, the stamping analyses of an automotive part are conducted, and comparisons of the FE results using both the isotropic hardening plasticity model and the proposed model are presented in terms of the calculated strain, thickness, residual stress and bending moment distributions. It is observed that both models produce similar strain and thickness predictions; however, there appeared to be significant differences in computed residual stress and bending moments. Furthermore, the springback deformations with both plasticity models are compared with CMM measurements of the manufactured parts. The final part geometry and overall springback distortion pattern produced by the proposed model is mostly in agreement with the measurements and more accurate. (c) 2007 Elsevier B.V All rights reserved

Role of iliac crest tangent in correct numbering of lumbosacral transitional vertebrae

Background/aim: The iliac crest tangent (ICT) has recently emerged as a reliable landmark to correctly number the lumbosacral transitional vertebrae (ISTV). We retrospectively evaluated the reproducibility and accuracy of the ICT as a landmark in subjects without disc degeneration

Low-dose CT radiomics features-based neural networks predict lymphoma types

Abstract Background Fluorodeoxyglucose positron emission tomography (PET)–computed tomography (CT) is preferred for pretreatment staging and treatment planning in patients with lymphoma. This study aims to train and validate the neural networks (NN) for predicting lymphoma types using low-dose CT radiomics. Results Few radiomics features were stable in intraclass correlation coefficient and coefficient of variation analysis (n = 119). High collinear ones with variance inflation factor were eliminated (n = 56). Twenty-four features were selected with the least absolute shrinkage and selection operator regression for network training. NN had 75.76% predictive accuracy in the validation set and has 0.73 (95% CI 0.55–0.91) area under the curve (AUC) to differentiate Hodgkin lymphoma from non-Hodgkin lymphoma. NN which was used to differentiate B-cell lymphoma from T-cell lymphoma had 78.79% predictive accuracy and has 0.81 (95% CI 0.63–0.99) AUC. Conclusions In this study, in which we used low-dose CT images of PET–CT scans, predictions of the neural network were near acceptable lower bound for Hodgkin and non-Hodgkin lymphoma discrimination, and B-cell and T-cell lymphoma differentiation