The functional significance behind expressing two IL–8 receptor types on PMN
Bibliographic record
Abstract
PMN are critical to innate immunity and are fundamental to antibacterial defense. To localize to sites of infection, PMN possess receptors that detect chemoattractant stimuli elicited at the site, such as chemokines, complement split products, or bioactive lipids. Signaling through these receptors stimulates chemotaxis toward the site of infection but also activates a number of biochemical processes, with the result that PMN kill invading bacteria. PMN possess two receptors, CXCR1 and CXCR2, for the N-terminal ELR motif-containing CXC chemokines, although only two chemokine members bind both receptors and the remainder binding only CXCR2. This peculiar pattern in receptor specificity has drawn considerable interest and investigation into whether signaling through each receptor might impart unique properties on the PMN. Indeed, at first glance, CXCR1 and CXCR2 appear to be functionally redundant; however, there are differences. Considering these proinflammatory activities of activating PMN through chemokine receptors, there has been great interest in the possibility that blocking CXCR1 and CXCR2 on PMN will provide a therapeutic benefit. The literature examining CXCR1 and CXCR2 in PMN function during human and modeled diseases will be reviewed, asking whether the functional differences can be perceived based on alterations in the role PMN play in these processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".