A Novel Hematopoietic Granulin Induces Proliferation of Goldfish (Carassius auratus L.) Macrophages
Bibliographic record
Abstract
Granulins are a group of highly conserved growth factors that have been described from a variety of organisms spanning the metazoa. In this study, goldfish granulin was one of the most commonly identified transcripts in the differential cross-screening of macrophage cDNA libraries and was preferentially expressed in proliferating macrophages. Unlike mammalian granulins, which possess 7.5 repeats of a characteristic signature of 12 cysteine residues, the goldfish granulin encoded a putative peptide possessing only 1.5 cysteine repeats. Northern blot and real-time PCR analyses indicated that goldfish granulin was expressed only in the hematopoietic tissues of the goldfish, specifically the kidney and spleen, and in activated peripheral blood mononuclear cells. We expressed granulin using a prokaryotic expression system and produced an affinity-purified rabbit anti-goldfish granulin IgG. Recombinant goldfish granulin induced a dose-dependent proliferative response of goldfish macrophages that was inversely related to the myeloid differentiation stage of the cells studied. The highest proliferative response was observed in macrophage progenitor cells and monocytes. This proliferative response of macrophages was abrogated by the addition of anti-granulin IgG. These results indicate that goldfish granulin is a growth factor that positively modulates cell proliferation at distinct junctures of macrophage differentiation.
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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".