Immunochemical Characterization of <i>Acacia</i> Pollen Allergens and Evaluation of Cross-Reactivity Pattern with the Common Allergenic Pollens
Bibliographic record
Abstract
Pollen from the Acacia has been reported as an important source of pollinosis in tropical and subtropical regions of the world. The aim of this study was to characterize the IgE binding protein of Acacia farnesiana pollen extract and evaluate cross-reactivity with the most allergenic pollens. In this study, pollen extract was fractionated by SDS-PAGE and the allergenic profile was determined by IgE-immunoblotting and specific ELISA using forty-two Acacia allergic patients. Potential cross-reactivity among Acacia and selected allergenic plants was evaluated with ELISA and immunoblotting inhibition experiments. There were several resolved protein fractions on SDS-PAGE which ranged from 12 to 85 kDa. Several allergenic protein bands with molecular weights approximately between 12 and 85 kDa were recognized by IgE-specific antibodies from Acacia allergic patients in the immunoblot assay. The inhibition by the Prosopis juliflora pollen extract was more than those by other pollen extracts. Moreover, the wheal diameters generated by the Acacia pollen extract were highly correlated with those of P. juliflora pollen extracts. The findings suggest that several proteins such as 15, 23, 45, and 50 kDa proteins could be used as diagnostic and therapeutic reagents for patients allergic to A. farnesiana and P. juliflora.
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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".