Interference with Glycosaminoglycan-Chemokine Interactions with a Probe to Alter Leukocyte Recruitment and Inflammation In Vivo
Bibliographic record
Abstract
In vivo leukocyte recruitment is not fully understood and may result from interactions of chemokines with glycosaminoglycans/GAGs. We previously showed that chlorite-oxidized oxyamylose/COAM binds the neutrophil chemokine GCP-2/CXCL6. Here, mouse chemokine binding by COAM was studied systematically and binding affinities of chemokines to COAM versus GAGs were compared. COAM and heparan sulphate bound the mouse CXC chemokines KC/CXCL1, MIP-2/CXCL2, IP-10/CXCL10 and I-TAC/CXCL11 and the CC chemokine RANTES/CCL5 with affinities in the nanomolar range, whereas no binding interactions were observed for mouse MCP-1/CCL2, MIP-1α/CCL3 and MIP-1β/CCL4. The affinities of COAM-interacting chemokines were similar to or higher than those observed for heparan sulphate. Although COAM did not display chemotactic activity by itself, its co-administration with mouse GCP-2/CXCL6 and MIP-2/CXCL2 or its binding of endogenous chemokines resulted in fast and cooperative peritoneal neutrophil recruitment and in extravasation into the cremaster muscle in vivo. These local GAG mimetic features by COAM within tissues superseded systemic effects and were sufficient and applicable to reduce LPS-induced liver-specific neutrophil recruitment and activation. COAM mimics glycosaminoglycans and is a nontoxic probe for the study of leukocyte recruitment and inflammation 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) | 0.001 | 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 | 0.001 |
| 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".