Cell “vision”: complementary factor of protein corona in nanotoxicology
Bibliographic record
Abstract
Engineered nanoparticles are increasingly being considered for use as biosensors, imaging agents and drug delivery vehicles. Their versatility in design and applications make them an attractive proposition for new biological and biomedical approaches. Despite the remarkable speed of development in nanoscience, relatively little is known about the interaction of nanoscale objects with living systems. In a biological fluid, proteins associate with nanoparticles, and the amount and the presentation of the proteins on their surface could lead to a different in vivo response than an uncoated particle. Here, in addition to protein adsorption, we are going to introduce concept of cell "vision", which would be recognized as another crucial factor that should be considered for the safe design of any type of nanoparticles that will be used in specific biomedical applications. The impact of exactly the same nanoparticles on various cells is significantly different and could not be assumed for other cells; the possible mechanisms that justify this cellular response relate to the numerous detoxification strategies that any particular cell can utilize in response to nanoparticles. The uptake and defence mechanism could be considerably different according to the cell type. Thus, what the cell "sees", when it is faced with nanoparticles, is most likely dependent on the cell type.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".