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These studies suggest that we study and model the effects of nanomaterials on human health using computational modeling (including QSAR and machine learning), high-throughput screening, and data mining techniques to predict toxicity, cellular uptake, and biological effects, guiding safer design and risk assessment.
20 papers analyzed
The study and modeling of the effects of nanomaterials on human health is a rapidly evolving field, driven by the increasing use of nanomaterials in various applications. Understanding the potential risks associated with nanomaterials is crucial for ensuring their safe use. This involves investigating their interactions with biological systems, predicting their toxicological impacts, and developing models to assess their safety.
Biophysical Interactions and Cellular Responses:
Predictive Toxicological Approaches:
Quantitative Structure-Activity Relationship (QSAR) Models:
Data Mining and Machine Learning:
Gene Expression Analysis:
The study and modeling of nanomaterial effects on human health involve a multifaceted approach, integrating biophysical interaction studies, predictive toxicology, QSAR models, data mining, and gene expression analysis. These methods collectively enhance our understanding of nanomaterial toxicity and aid in developing safer nanomaterials for biomedical and industrial applications.
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