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10.1016/j.taap.2017.03.011 http://scihub22266oqcxt.onion/10.1016/j.taap.2017.03.011 C5581002!5581002 !28344110     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28344110 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Toxicol+Appl+Pharmacol 2017 ; 323 (ä): 66-73 Nephropedia Template TP {{tp|p=28344110 |t=2017. Toward a systematic exploration of nano-bio interactions |pdf=|usr=}}{{28344110 }} gab.com Text Toward a systematic exploration of nano-bio interactions JO: Toxicol Appl Pharmacol http://www.kidney.de/pget.php?&tw=1&pmid=28344110 #FOAvip Many studies of nanomaterials make non-systematic alterations of nanoparticle physicochemical properties. Given the immense size of the property space for nanomaterials, such approaches are not very useful in elucidating fundamental relationships between inherent physicochemical properties of these materials and their interactions with, and effects on, biological systems. Data driven artificial intelligence methods such as machine learning algorithms have proven highly effective in generating models with good predictivity and some degree of interpretability. They can provide a viable method of reducing or eliminating animal testing. However, careful experimental design with the modelling of the results in mind is a proven and efficient way of exploring large materials spaces. This approach, coupled with high speed automated experimental synthesis and characterization technologies now appearing, is the fastest route to developing models that regulatory bodies may find useful. We advocate greatly increased focus on systematic modification of physicochemical properties of nanoparticles combined with comprehensive biological evaluation and computational analysis. This is essential to obtain better mechanistic understanding of nano-bio interactions, and to derive quantitatively predictive and robust models for the properties of nanomaterials that have useful domains of applicability. Twit Text FOAVip Toward a systematic exploration of nano-bio interactions ????Toxicol Appl Pharmacol http://www.kidney.de/pget.php?&tw=1&pmid=28344110 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28344110 #FOAvip English Wikipedia <ref>{{Cite pmid|28344110 }}</ref> Toward a systematic exploration of nano-bio interactions #MMPMID28344110 Bai X ; Liu F ; Liu Y ; Li C ; Wang S ; Zhou H ; Wang W ; Zhu H ; Winkler DA ; Yan B Toxicol Appl Pharmacol 2017[May]; 323 (ä): 66-73 PMID28344110 show ga Many studies of nanomaterials make non-systematic alterations of nanoparticle physicochemical properties. Given the immense size of the property space for nanomaterials, such approaches are not very useful in elucidating fundamental relationships between inherent physicochemical properties of these materials and their interactions with, and effects on, biological systems. Data driven artificial intelligence methods such as machine learning algorithms have proven highly effective in generating models with good predictivity and some degree of interpretability. They can provide a viable method of reducing or eliminating animal testing. However, careful experimental design with the modelling of the results in mind is a proven and efficient way of exploring large materials spaces. This approach, coupled with high speed automated experimental synthesis and characterization technologies now appearing, is the fastest route to developing models that regulatory bodies may find useful. We advocate greatly increased focus on systematic modification of physicochemical properties of nanoparticles combined with comprehensive biological evaluation and computational analysis. This is essential to obtain better mechanistic understanding of nano-bio interactions, and to derive quantitatively predictive and robust models for the properties of nanomaterials that have useful domains of applicability. |*Artificial Intelligence [MESH] |Algorithms [MESH] |Animal Testing Alternatives [MESH] |Animals [MESH] |Computational Biology/*methods [MESH] |Humans [MESH] |Machine Learning [MESH] |Nanostructures/chemistry/*toxicity [MESH] |Nanotechnology/*methods [MESH] |Risk Assessment [MESH]Toward a systematic exploration of nano-bio interactions (epmc).pdf DeepDyve Pubget Overpricing