Journal
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Volume 24, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/ijms24044220
Keywords
Ag-NPs; cytotoxicity; HEK293; PC 12 cell line; machine learning; Decision Tree; Random Forest; k-means clustering; regression metrics
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This study investigated the cytotoxicity of silver nanoparticles (Ag-NPs) using the MTT assay. Machine learning models, Decision Tree (DT) and Random Forest (RF), were used to analyze the relationship between physical parameters and cytotoxicity. The results showed that DT outperformed RF in predicting the toxicity parameter. The study suggests using algorithms to optimize and design the synthesis of Ag-NPs in various applications such as drug delivery and cancer treatments.
Silver nanoparticles (Ag-NPs) demonstrate unique properties and their use is exponentially increasing in various applications. The potential impact of Ag-NPs on human health is debatable in terms of toxicity. The present study deals with MTT(3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl-tetrazolium-bromide) assay on Ag-NPs. We measured the cell activity resulting from molecules' mitochondrial cleavage through a spectrophotometer. The machine learning models Decision Tree (DT) and Random Forest (RF) were utilized to comprehend the relationship between the physical parameters of NPs and their cytotoxicity. The input features used for the machine learning were reducing agent, types of cell lines, exposure time, particle size, hydrodynamic diameter, zeta potential, wavelength, concentration, and cell viability. These parameters were extracted from the literature, segregated, and developed into a dataset in terms of cell viability and concentration of NPs. DT helped in classifying the parameters by applying threshold conditions. The same conditions were applied to RF to extort the predictions. K-means clustering was used on the dataset for comparison. The performance of the models was evaluated through regression metrics, viz. root mean square error (RMSE) and R-2. The obtained high value of R-2 and low value of RMSE denote an accurate prediction that could best fit the dataset. DT performed better than RF in predicting the toxicity parameter. We suggest using algorithms for optimizing and designing the synthesis of Ag-NPs in extended applications such as drug delivery and cancer treatments.
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