Detection of Cross-Reactive Allergens in Kentucky Bluegrass Pollen and Six Other Grasses by Crossed Radioimmunoelectrophoresis
Bibliographic record
Abstract
Using crossed immunoelectrophoretic analysis of an aqueous extract of Kentucky bluegrass (KBG)pollen employing hyperimmune rabbit and sheep precipitating antibodies to KBG and various grasses we detected at least 33 antigenic components. The majority of these antigens were extractable from the pollen within a period of 14 min and possessed anodal electrophoretic mobility. The extensive recognition of antigens in the KBG extract by antibodies raised to false oat, and to the combined extracts of timothy, orchard, meadow, velvet and rye grasses indicate that these grasses contain many cross-reactive antigens. One of the KBG antigens with cathodal mobility--the major component of the previously isolated allergen C--was identified as immunologically identical to timothy Ag 30. Crossed radioimmunoelectrophoretic analysis revealed differences in the extent to which IgE antibodies present in sera of individuals allergic to KBG bound to various antigens of KBG pollen. Those antigens that were recognized as allergens by all of the allergic sera examined were regarded as candidates for future isolation and characterization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".