ELR+CXC Chemokine Antagonist Targets Neutrophilic Pathology at Multiple Levels
Bibliographic record
Abstract
The Glu‐Leu‐Arg (ELR + ) chemokines (e.g., CXCL8/IL‐8, CXCL1/GROα) play an important role in neutrophilic inflammation through binding to two distinct G protein‐coupled receptors (GPCR), the CXCR1 and CXCR2. We have generated a potent human ELR + CXC chemokine antagonist, CXCL8 (3–72) K11R/G31P (hG31P), which blocks neutrophil chemotaxis and intracellular calcium mobilization induced by the ELR + CXC chemokines. We report herein that hG31P also antagonizes acute lung injury induced by lipopolysaccharide, >95% reducing neutrophil infiltration into, and activation within, the airways, as determined by direct counting and assays of neutrophil granule markers (i.e., myeloperoxidase, lactoferrin, and MMP‐9). This hG31P also antagonized chemotaxis and calcium mobilization responses induced in neutrophils by C5a, LTB4 and fMLP, which signal via alternate GPCR. Moreover, hG31P dose‐dependently inhibited the expression of CXCL1 and CXCL8 by LPS‐challenged A549 human airway epithelial cells, and reversed the anti‐apoptotic effects of ELR + CXC chemokines on neutrophils. These data indicate that ELR + CXC chemokine antagonism targets inflammatory responses at multiple levels, through actions on epithelial cells, neutrophils, and heterologous GCPR. ( Research supported by funds from CIHR, NSERC and IL Therapeutics )
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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) | 0.001 | 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) | 0.003 | 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 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".