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Record W2134171187 · doi:10.1186/1710-1492-4-3-106

Nature of Regulatory T Cells in the Context of Allergic Disease

2008· article· en· W2134171187 on OpenAlexvenueno aff
Cevdet Özdemir, Mübeccel Akdiş, Cezmi A. Akdiş

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2008
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsnot available
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsImmunologyImmunoglobulin EAllergic inflammationAllergenAntibodyMedicineInflammationAllergyContext (archaeology)ImmunotherapyBiologyImmune system

Abstract

fetched live from OpenAlex

: Allergen-specific immunotherapy (SIT) is the cornerstone of the management of allergic diseases, which targets modification of the immunologic response, along with environmental allergen avoidance and pharmacotherapy. SIT is associated with improved tolerance to allergen challenge, with a decrease in immediate-phase and late-phase allergic inflammation. SIT has the potential to prevent development of new sensitizations and progression of allergic rhinitis to asthma. It has a role in cellular and humoral responses in a modified pattern. The ratio of T helper (Th)1 cytokines to Th2 cytokines is increased following SIT, and functional regulatory T cells are induced. Interleukin-10 production by monocytes, macrophages, and B and T cells is increased, as well as expression of transforming growth factor beta. SIT is associated with increases in allergen-specific antibodies in IgA, IgG1, and IgG4 isotypes. These blocking-type immunoglobulins, particularly IgG4, may compete with IgE binding to allergen, decreasing the allergen presentation with the high- and low-affinity receptors for IgE (FcepsilonRI and FcepsilonRII, respectively). Additionally, SIT reduces the number of mast cells and eosinophils in the target tissues and release of mediators from these cells.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.298

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 studies0.0000.001
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 head0.283
Teacher spread0.266 · 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 designObservational
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

Citations9
Published2008
Admission routes1
Has abstractyes

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