Localization of DNA and RNA in Eosinophil Secretory Granules
Bibliographic record
Abstract
BACKGROUND: Although the accepted paradigm is that the proteins stored in eosinophil crystalloid granules are translated from messenger RNA transcribed in the cell nucleus, recent ultrastructural evidence suggests that protein synthesis may also take place within eosinophilic granules. METHODS: We used 2 different methods to detect the presence of DNA and RNA in eosinophil secretory granules. Using bromodeoxyuridine, a thymidine analogue, and bromouridine, a uracil analogue, we labeled the DNA and RNA in eosinophils in vivo in rabbits. Immunoelectron microscopy to localize these molecules was performed on ultrathin sections of blood and bone marrow eosinophils using monoclonal anti-bromodeoxyuridine antibody with IgG as a control. The immunogold grain density was measured in each subcellular compartment within the eosinophils and analyzed using image analysis software. A combination of DNA/CD63 immunofluorescence staining and a fluorescently labeled molecular probe that stains RNA was used to examine the presence of DNA and RNA in the secretory granules of human blood eosinophils. RESULTS: The mean density of bromodeoxyuridine-labeled DNA and bromouridine-labeled RNA immunogold grains in the secretory granules of blood and bone marrow eosinophils were significantly higher (p < 0.0005) than cytoplasmic or background staining. We also demonstrated the existence of DNA and RNA in the CD63-positive secretory granules of human peripheral blood eosinophils by means of immunofluorescent staining and a fluorescently labeled molecular probe. CONCLUSIONS: These results provide evidence that eosinophil granules are the site of DNA and RNA synthesis and suggest the potential for a new role(s) for eosinophil-secretory granules.
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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".