Modeling to predict the cytotoxicity of SiO2 and TiO2 nanoparticles

N.A.

Makale detayları

Modeling to predict the cytotoxicity of SiO2 and TiO2 nanoparticles

Yazarlar:

N.A.

Yayıncı:

Marmara University

Ana Başlıklar:

Pharmacology and Pharmaceutical Sciences (Other)

Dergi:

Journal of Research in Pharmacy Vol.23 Issue 2, pp.267-274

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001346482

pISSN:

2630-6344

Anahtar kelimeler:

Özet:

 The objective of the current study was to design a suitable model to predict the cytotoxicity induced by SiO2 and TiO2 nanoparticles in different conditions using computational models. To achieve this, we employed various statistical approaches such as linear regression, as well as artificial neural networks and support vector machine nonlinear models. The effective input parameters of the SiO2 nanoparticles were particle size, particle concentration, and cell exposure time. In the case of the TiO2 nanoparticles, the particle size and concentration served as input variables. Cell viability was considered the output response for both nanoparticles. The modeling was performed using both linear and non-linear methods. In addition, an external validation analysis was conducted to evaluate the predictability of the models by splitting the data into training and test data. The best models to predict cell viability were the models developed by artificial neural network. The results of this investigation indicate that non-linear models could be superior to linear models in predicting cell viability for SiO2 and TiO2 nanoparticles.

 The objective of the current study was to design a suitable model to predict the cytotoxicity induced by SiO2 and TiO2 nanoparticles in different conditions using computational models. To achieve this, we employed various statistical approaches such as linear regression, as well as artificial neural networks and support vector machine nonlinear models. The effective input parameters of the SiO2 nanoparticles were particle size, particle concentration, and cell exposure time. In the case of the TiO2 nanoparticles, the particle size and concentration served as input variables. Cell viability was considered the output response for both nanoparticles. The modeling was performed using both linear and non-linear methods. In addition, an external validation analysis was conducted to evaluate the predictability of the models by splitting the data into training and test data. The best models to predict cell viability were the models developed by artificial neural network. The results of this investigation indicate that non-linear models could be superior to linear models in predicting cell viability for SiO2 and TiO2 nanoparticles.

 The objective of the current study was to design a suitable model to predict the cytotoxicity induced by SiO2 and TiO2 nanoparticles in different conditions using computational models. To achieve this, we employed various statistical approaches such as linear regression, as well as artificial neural networks and support vector machine nonlinear models. The effective input parameters of the SiO2 nanoparticles were particle size, particle concentration, and cell exposure time. In the case of the TiO2 nanoparticles, the particle size and concentration served as input variables. Cell viability was considered the output response for both nanoparticles. The modeling was performed using both linear and non-linear methods. In addition, an external validation analysis was conducted to evaluate the predictability of the models by splitting the data into training and test data. The best models to predict cell viability were the models developed by artificial neural network. The results of this investigation indicate that non-linear models could be superior to linear models in predicting cell viability for SiO2 and TiO2 nanoparticles.

 The objective of the current study was to design a suitable model to predict the cytotoxicity induced by SiO2 and TiO2 nanoparticles in different conditions using computational models. To achieve this, we employed various statistical approaches such as linear regression, as well as artificial neural networks and support vector machine nonlinear models. The effective input parameters of the SiO2 nanoparticles were particle size, particle concentration, and cell exposure time. In the case of the TiO2 nanoparticles, the particle size and concentration served as input variables. Cell viability was considered the output response for both nanoparticles. The modeling was performed using both linear and non-linear methods. In addition, an external validation analysis was conducted to evaluate the predictability of the models by splitting the data into training and test data. The best models to predict cell viability were the models developed by artificial neural network. The results of this investigation indicate that non-linear models could be superior to linear models in predicting cell viability for SiO2 and TiO2 nanoparticles.

Makale detayları

Modeling to predict the cytotoxicity of SiO2 and TiO2 nanoparticles

Yazarlar:

N.A.

Yayıncı:

Marmara University

Ana Başlıklar:

Pharmacology and Pharmaceutical Sciences (Other)

Dergi:

Journal of Research in Pharmacy Vol.23 Issue 2, pp.267-274

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001346482

pISSN:

2630-6344

Artificial neural network

cytotoxicity

modeling

nanoparticles

Sayı

Makale detayları

Modeling to predict the cytotoxicity of SiO2 and TiO2 nanoparticles

Yazarlar: N.A.

Yayıncı: Marmara University

Ana Başlıklar: Pharmacology and Pharmaceutical Sciences (Other)

Dergi: Journal of Research in Pharmacy Vol.23 Issue 2, pp.267-274

Belge Tipi: Makale

Yayın Tarihi: 6/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001346482

pISSN: 2630-6344

Artificial neural network

cytotoxicity

modeling

nanoparticles

Sayı

Yazarlar:

N.A.

Yayıncı:

Marmara University

Ana Başlıklar:

Pharmacology and Pharmaceutical Sciences (Other)

Dergi:

Journal of Research in Pharmacy Vol.23 Issue 2, pp.267-274

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001346482

pISSN:

2630-6344