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Record W2478478278 · doi:10.1038/srep30438

Is ragweed pollen allergenicity governed by environmental conditions during plant growth and flowering?

2016· article· en· W2478478278 on OpenAlexaboutno aff
A. Ghiani, S. Ciappetta, Rodolfo Gentili, Riccardo Asero, Sandra Citterio

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsnot available
FundersInstitut National de la Recherche Agronomique
KeywordsRagweedPollenAmbrosiaBiologyBotanyAllergyImmunology

Abstract

fetched live from OpenAlex

Pollen allergenicity is one of the main factors influencing the prevalence and/or severity of allergic diseases. However, how genotype and environment contribute to ragweed pollen allergenicity has still to be established. To throw some light on the factors governing allergenicity, in this work 180 ragweed plants from three Regions (Canada, France, Italy) were grown in both controlled (constant) and standard environmental conditions (seasonal changes in temperature, relative humidity and light). Pollen from single plants was characterized for its allergenic potency and for the underlying regulation mechanisms by studying the qualitative and quantitative variations of the main isoforms of the major ragweed allergen Amb a 1. Results showed a statistically higher variability in allergenicity of pollen from standard conditions than from controlled conditions growing plants. This variability was due to differences among single plants, regardless of their origin, and was not ascribed to differences in the expression and IgE reactivity of individual Amb a 1 isoforms but rather to quantitative differences involving all the studied isoforms. It suggests that the allergenic potency of ragweed pollen and thus the severity of ragweed pollinosis mainly depends on environmental conditions during plant growth and flowering, which regulate the total Amb a 1 content.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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

Citations23
Published2016
Admission routes1
Has abstractyes

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