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Record W2016154509 · doi:10.1155/2014/409056

Immunochemical Characterization of <i>Acacia</i> Pollen Allergens and Evaluation of Cross-Reactivity Pattern with the Common Allergenic Pollens

2014· article· en· W2016154509 on OpenAlexfundno aff
Mohammad Hossein Shams, Ali Khodadadi, Mohammad‐Ali Assarehzadegan, Seyed Hamid Borsi, Akram Amini

Bibliographic record

VenueJournal of Allergy · 2014
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsnot available
FundersAhvaz Jundishapur University of Medical SciencesUniversity of Toronto
KeywordsPollenAcaciaProsopisImmunoglobulin EProsopis julifloraCross-reactivityAllergenBotanyAllergyImmunologyEpitopeBiologyAntibodyCross reactions

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.280
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2014
Admission routes1
Has abstractyes

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