Tissue-Specific Mechanisms Control the Retention of IL-8 in Lungs and Skin
Bibliographic record
Abstract
Chemokines are a group of structurally related peptides that promote the directed migration of leukocytes in tissue. Mechanisms controlling the retention of chemokines in tissue are not well understood. In this study we present evidence that two different mechanisms control the persistence of the CXC chemokine, IL-8, in lungs and skin. (125)I-labeled IL-8 was injected into the airspaces of the lungs and the dermis of the skin and the amount of (125)I-labeled IL-8 that remained at specified times was measured by scintillation counting. The (125)I-labeled IL-8 was cleared much more rapidly from skin than lungs, as only 2% of the (125)I-labeled IL-8 remained in skin at 4 h whereas 50% of the (125)I-labeled IL-8 remained in lungs at 4 h. Studies in neutropenic rabbits showed that neutrophils shortened the retention of (125)I-labeled IL-8 in skin but not lungs. A monomeric form of IL-8, N-methyl-leucine 25 IL-8, was not retained as long in lungs as recombinant human IL-8, indicating that dimerization of IL-8 is a mechanism that increases the local concentration and prolongs the retention of (125)I-labeled IL-8 in lungs. These observations show that the mechanisms that control the retention of IL-8 in tissue include neutrophil migration and dimerization, and that the importance of these varies in different tissues.
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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 | 0.001 | 0.001 |
| 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".