Detection of Human Reagins with Rat Peritoneal Mast Cells by Histamine Release
Bibliographic record
Abstract
An immunologically specific method was developed for the detection of reaginic antibodies in sera of allergic individuals. It is based on the determination of histamine release (HR) from rat peritoneal mast cells (RPMC), presensitized with the allergic serum, on challenge with the appropriate allergen. The amount of HR was shown to depend on the concentrations of reagins and allergens. From experiments involving RPMC sensitized with different fractions of allergic serum isolated by chromatography or with appropriate reverse immunosorbent, it was deduced that the bulk of HR was due to antibodies of the IgE class and that about 10% of HR was attributable to antibodies of the IgG type; however, paradoxically, IgG of normal human serum did not have the capacity to sensitize RPMC. Both classes of immunoglobulins present in reaginic serum and capable of sensitizing RPMC were inactivated at 56 °C or with 0.1 M 2-mercaptoethanol. Peritoneal mast cells or a rat, which had been immunized to produce homocytotropic (HCT) antibodies, or RPMC sensitized in vitro with rat HCT antibodies, did not fix human reagins. By contrast, human reagins to two distinct allergens were fixed on the same RPMC preparation by successive sensitization of the cells with the corresponding sera. The cells sensitized with rat HCT antibodies released histamine on challenge with anti-human IgE serum. It is therefore concluded that there exist structural homologies between the groups of human and rat reaginic antibodies which are responsible for fixation on rat RPMC, as well as between the antigenic groups of these two reaginic antibodies capable of crossreacting with anti-human IgE.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".