Upconverting nanoparticles: assessing the toxicity
Bibliographic record
Abstract
Lanthanide doped nanoparticles (Ln:NPs) hold promise as novel luminescent probes for numerous applications in nanobiophotonics. Despite excellent photostability, narrowband photoluminescence, efficient anti-Stokes emission and long luminescence lifetimes, which are needed to meet the requirements of multiplexed and background free detection at prolonged observation times, concern about their toxicity is still an issue for both in vivo and in vitro applications. Similar to other chemicals or pharmaceuticals, the very same properties that are desirable and potentially useful from a biomedical perspective can also give rise to unexpected and hazardous toxicities. In engineered bionanomaterials, the potentially harmful effects may originate not only from their chemical composition but also from their small size. The latter property enables the nanoparticles to bypass the biological barriers, thus allowing deep tissue penetration and the accumulation of the nanoparticles in a number of organs. In addition, nanoparticles are known to possess high surface chemical reactivity as well as a large surface-to-volume ratio, which may seriously affect their biocompatibility. Herein we survey the underlying mechanisms of nanotoxicity and provide an overview on the nanotoxicity of lanthanides and of upconverting nanoparticles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".