WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Citation - WoS: 13Citation - Scopus: 17Machine Learning-Assisted Prediction of the Toxicity of Silver Nanoparticles: a Meta-Analysis(Springer, 2023) Bilgi, Eyüp; Öksel Karakuş, CeydaSilver nanoparticles are likely to be more dangerous than other forms of silver due to the intracellular release of silver ions upon dissolution and the formation of mixed ion-containing complexes. Such concerns have resulted in an ever-growing pile of scientific evaluations addressing the safety aspects of nanosilver with widely varying methodological approaches. The substantial differences in the conduct/design of nanotoxicity screening have led to the generation of conflicting findings that may be accurate in their narrative but fail to provide a complete picture. One strategy to maximize the use of individual risk assessments with potentially biased estimates of toxicological effects is to homogenize results across several studies and to increase the generalizability and human relevance of their findings. Here, we collected a large pool of data (n=162 independent studies) on the cytotoxicity of nanosilver and unrevealed potential triggers of toxicity. Two different machine learning approaches, decision tree (DT) and artificial neural network (ANN), were primarily employed to develop models that can predict the cytotoxic potential of nanosilver based on material- and assay-related parameters. Other machine learning algorithms (logistic regression, Gaussian Naive Bayes, k-nearest neighbor, and random forest classifiers) were also applied. Among several attributes compared, exposure concentration, duration, zeta potential, particle size, and coating were found to have the most substantial impact on nanotoxicity, with biomolecule- and microorganism-assisted surface modifications having the most beneficial and detrimental effects on cell survival, respectively. Such machine learning-assisted efforts are critical to developing commercially viable and safe nanosilver-containing products in the ever-expanding nanobiomaterial market.Article Citation - WoS: 10Citation - Scopus: 13Overcoming Roadblocks in Computational Roadmaps To the Future for Safe Nanotechnology(IOP Publishing, 2021) Öksel Karakuş, Ceyda; Winkler, David A.The rapid rise of nanotechnology has resulted in a parallel rise in the number of products containing nanomaterials. The unusual properties that nano forms of materials exhibit relative to the bulk has driven intense research interest and relatively rapid adoption by industry. Regulatory agencies are charged with protecting workers, the public, and the environment from any adverse effects of nanomaterials that may also arise because of these novel physical and chemical properties. They need data and models that allow them to flag nanomaterials that may be of concern, while balancing potential stifling of commercial innovation. Roadmaps for the future of safe nanotechnology were defined more than a decade ago, but many roadblocks identified in these studies remain. Here, we discuss the roadblocks that are still hindering the effective application of informatics and predictive computational nanotoxicology methods from providing more effective guidance to nanomaterials regulatory agencies and safe-by-design rationale for industry. We describe how developments in high throughput synthesis, characterization, and biological assessment of nanomaterials will overcome many of these roadblocks, allowing a clearly defined roadmap for computational design of effective but safe-by-design nanomaterials to be realized.