Rosette nanotubes inhibit bovine neutrophil chemotaxis
Bibliographic record
Abstract
Migration of activated neutrophils that have prolonged lifespan into inflamed organs is an important component of host defense but also contributes to tissue damage and mortality. In this report, we used biologically-inspired RGD-tagged rosette nanotubes (RNT) to inhibit neutrophil chemotaxis. We hypothesize that RGD-RNT will block neutrophil migration through inhibition of MAPK. In this report, RNT conjugated to lysine (K-RNT) and arginine-glycine-aspartic acid-serine-lysine (RGDSK-RNT) were co-assembled in a molar ratio of 95/5. The effect of the resulting composite RNT (RGDSK/K-RNT) on neutrophil chemotaxis, cell signaling and apoptosis was then investigated. Exposure to RGDSK/K-RNT reduced bovine neutrophil migration when compared to the non-treated group (p<0.001). Similar effect was seen following treatment with ERK1/2 or p38 MAPK inhibitors. Phosphorylation of the ERK1/2 and p38 MAPK was inhibited at 5 min by RGDSK/K-RNT (p<0.05). The RGDSD/K-RNT did not affect the migration of neutrophils pre-treated with αvβ3 integrin antibody suggesting that both bind to the same receptor. RGDSK/K-RNT did not induce apoptosis in bovine neutrophils, which was suppressed by pre-exposing them to LPS (p<0.001). We conclude that RGDSK/K-RNT inhibit phosphorylation of ERK1/2 and p38 MAPK and inhibit chemotaxis of bovine neutrophils.
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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) | 0.001 | 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".