The Inflammatory Process in Response to Nanoparticles
Bibliographic record
Abstract
products are already available on the market [1, 2]. The use of nanoparticles (NPs) has increased in the past few years in various fields including defence, aerospace, electronics, biology, and medicine. Therefore as human exposure to NPs increases, so do investigations on their toxicity. In one hand, nanotoxicology can be seen as an important subdiscipline of toxicology, and, on the other hand, it also represents a subdiscipline of nanotechnology [3]. Studies show that many parameters such as the diameter, surface area, surface composition, and solubility, to name a few, can all have an effect on cell responses, cellular interactions, and reactive oxygen species (ROS) production. They can also have an effect on NP capacity to bind to certain proteins or receptors. More importantly these parameters and others have also the potential to influence an inflammatory response following NP exposure as it has been shown in many in vitro and in vivo studies [4–7]. It has often been demonstrated that smaller particles tend to induce greater inflammatory responses than their bigger counterparts of same chemical composition [5]. Although some NPs may possess proinflammatory activities, the use of others and NP techniques can be very useful in medicine; for example, they can be used for drug-delivery therapies and may also represent excellent biomarkers for the diagnosis of diverse diseases, including inflammatory diseases. In an effort to increase our general knowledge regarding the inflammatory properties of NPs, this
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How this classification was reachedexpand
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 | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".