Modulation of neutrophil function by the tripeptide feG
Bibliographic record
Abstract
BACKGROUND: Neutrophils are critical in the defense against potentially harmful microorganisms, but their excessive and inappropriate activation can contribute significantly to tissue damage and a worsening pathology. Through the release of endocrine factors submandibular glands contribute to achieving a balance in neutrophil function by modulating the state of activation and migratory potential of circulating neutrophils. A putative hormonal candidate for these effects on neutrophils was identified as a heptapeptide named submandibular gland peptide T (SGP-T; sequence = TDIFEGG). Since the tripeptide FEG, derived from SGP-T, and its D-amino acid analogue feG had similar inhibitory effects on inflammatory reactions, we investigated the effects of feG on human and rat neutrophil function. RESULTS: With human neutrophils feG had no discernible effect on oxidative burst or phagocytosis, but in picomolar amounts it reduced PAF-induced neutrophil movement and adhesion, and the binding of CD11b by 34% and that of CD16b close to control values. In the rat feG (10-11M) reduced the binding of CD11b and CD16 antibodies to PAF-stimulated circulating neutrophils by 35% and 43%, respectively, and at 100 micrograms/kilograms intraperitoneally feG reduced neutrophil in vivo migration by 40%. With ovalbumin-sensitized rats that were challenged with antigen, feG inhibited binding of antibodies against CD16b but not CD11b, on peritoneal leukocytes. CONCLUSIONS: The inhibitory effect of feG on neutrophil movement may be mediated by alterations in the co-stimulatory molecules CD11b and CD16.
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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".