skip to main content
Idioma:

Machine learning-assisted prediction of the toxicity of silver nanoparticles: a meta-analysis

Bilgi, Eyup ; Karakus, Ceyda Oksel

Journal of nanoparticle research : an interdisciplinary forum for nanoscale science and technology, 2023-08, Vol.25 (8), p.157, Article 157

Dordrecht: Springer Netherlands

Texto completo disponível

Citações Citado por
  • Título:
    Machine learning-assisted prediction of the toxicity of silver nanoparticles: a meta-analysis
  • Autor: Bilgi, Eyup ; Karakus, Ceyda Oksel
  • Assuntos: Algorithms ; Artificial neural networks ; Biomedical materials ; Biomolecules ; Cell survival ; Characterization and Evaluation of Materials ; Chemistry and Materials Science ; Cytotoxicity ; Decision trees ; Inorganic Chemistry ; Lasers ; Learning algorithms ; Machine learning ; Materials Science ; Meta-analysis ; Nanomaterials ; Nanoparticles ; Nanotechnology ; Neural networks ; Optical Devices ; Optics ; Photonics ; Physical Chemistry ; Research Paper ; Risk assessment ; Silver ; Toxicity ; Zeta potential
  • É parte de: Journal of nanoparticle research : an interdisciplinary forum for nanoscale science and technology, 2023-08, Vol.25 (8), p.157, Article 157
  • Descrição: Silver 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.
  • Editor: Dordrecht: Springer Netherlands
  • Idioma: Inglês
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.