Cell “vision”: complementary factor of protein corona in nanotoxicology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it