Structural parameters of nanoparticles affecting their toxicity for biomedical applications: a review
Bibliographic record
Abstract
Rapidly growing interest in using nanoparticles (NPs) for biomedical applications has increased concerns about their safety and toxicity. In comparison with bulk materials, NPs are more chemically active and toxic due to the greater surface area and small size. Understanding the NPs' mechanism of toxicity, together with the factors influencing their behavior in biological environments, can help researchers to design NPs with reduced side effects and improved performance. After overviewing the classification and properties of NPs, this review article discusses their biomedical applications in molecular imaging and cell therapy, gene transfer, tissue engineering, targeted drug delivery, Anti-SARS-CoV-2 vaccines, cancer treatment, wound healing, and anti-bacterial applications. There are different mechanisms of toxicity of NPs, and their toxicity and behaviors depend on various factors, which are elaborated on in this article. More specifically, the mechanism of toxicity and their interactions with living components are discussed by considering the impact of different physiochemical parameters such as size, shape, structure, agglomeration state, surface charge, wettability, dose, and substance type. The toxicity of polymeric, silica-based, carbon-based, and metallic-based NPs (including plasmonic alloy NPs) have been considered separately.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".