Evidence That Polyhydroxylated C<sub>60</sub> Fullerenes (Fullerenols) Amplify the Effect of Lipopolysaccharides to Induce Rapid Leukocyte Infiltration in Vivo
Bibliographic record
Abstract
Fullerenols C60(OH) have therapeutic potential, but there is debate regarding their toxicity. Here, we tested the hypothesis that C60(OH)n possesses a pro-inflammatory effect in vivo. Kinetic and dose-dependent experiments performed with the murine air pouch model of acute inflammation revealed that, unlike TiO2 used as a positive control in this model, C60(OH)n NPs were not pro-inflammatory in CD-1, C57BL/6, and BALB/c mice. However, after 3 h of treatment, C60(OH)n NPs were found to amplify the effect of lipopolysaccharides (LPS) causing a rapid leukocyte influx in which the major cells observed are neutrophils. The use of an antibody array assay to detect different analytes simultaneously indicates that the amplification effect is, at least partially, explained by an increased local production of several cytokines/chemokines in the exudates, including the pro-inflammatory cytokine IL-6. Using an ELISA to quantify the amount of IL-6 produced into air pouch exudates, we demonstrated that C60(OH)n increases the LPS-induced local production of this cytokine. Therefore, although C60(OH)n NPs alone do not exert proinflammatory activity under certain conditions, they can act in concert with other agents to cause inflammation, a situation that is likely to occur in vivo.
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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 | 0.000 |
| 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) | 0.002 | 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".