Nanotoxicity of Inert Materials: The Case of Gold, Silver and Iron
Bibliographic record
Abstract
Nanotechnology has opened a new horizon of research in various fields including applied physics, chemistry, electronics, optics, robotics, biotechnology and medicine. In the biomedical field, nanomaterials have shown remarkable potential as theranostic agents. Materials which are considered inert are often used in nanomedicine owning to their nontoxic profile. At nanoscale, these inert materials have shown unique properties that differ from bulk and dissolved counterparts. In the case of metals, this unique behavior not only imparts paramount advantages but also confers toxicity due to their unwanted interaction with different cellular processes. In the literature, the toxicity of nanoparticles made from inert materials has been investigated and many of these have revealed toxic potential under specific conditions. The surge to understand underlying mechanism of toxicity has increased and different means have been employed to overcome toxicity problems associated with these agents. In this review, we have focused nanoparticles of three inert metallic materials i.e. gold, silver and iron as these are regarded as biologically inert in the bulk and dissolved form. These materials have gained wider research interest and studies indicating the toxicity of these materials are also emerging. Oxidative stress, physical binding and interference with intracellular signaling are the major role player in nanotoxicity and their predominance is highly dependent upon size, surface coating and administered dose of nanoparticles. Current strategies to overcome toxicity have also been reviewed in the light of recent literature. The authors also suggested that uniform testing standards and well-designed studies are needed to evaluate nanotoxicity of these materials that are otherwise considered as inert. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".