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Record W2736371481 · doi:10.1111/all.13256

Long‐term effects: Galectin‐1 and specific immunotherapy for allergic responses in the intestine

2017· article· en· W2736371481 on OpenAlexaff
Qinghai Shu, Xiang‐Qian Luo, Zhi‐Qiang Liu, Shuqi Qiu, J.‐Q. Liu, Hua Guo, Longjiang Li, Menghan Li, D.‐B. Liu, Lixin Xia, Ping–Chang Yang

Bibliographic record

VenueAllergy · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsMcMaster University
FundersNational Natural Science Foundation of China
KeywordsMast cellImmunologyImmunoglobulin EAllergyImmunotherapyMedicineGalectin-1Immune systemGalectinAntibody

Abstract

fetched live from OpenAlex

BACKGROUND AND AIMS: Mast cell activation interferes with the effects of allergen-specific immunotherapy (SIT). Galectin-1 (Gal-1) is capable of regulating immune cells' functions. This study tests the hypothesis that administration of Gal-1 promotes and prolongs the efficacy of SIT via suppressing mast cell activation. METHODS: An intestinal allergy mouse model was developed. The coadministration of SIT and Gal-1 on suppression of the allergic responses, prevention of mast cell activation, and generation of antigen-specific regulatory T cells (Treg) in the intestine was observed in sensitized mice. RESULTS: The coadministration of Gal-1 and SIT markedly suppressed the allergic responses in the mouse intestine vs the use of either SIT alone or Gal-1 alone. The Gal-1 binds to the IgE/FcɛRI complexes on the surface of mast cells to prevent mast cell activation during SIT. Gal-1 promoted the SIT-generated allergen-specific Tregs in the intestine of sensitized mice. Coadministration of Gal-1 and SIT significantly enhanced the efficacy of immunotherapy in suppressing allergic responses in the intestine, which lasted for at least for 12 months. CONCLUSIONS: Long-term effects of specific immunotherapy on intestinal allergy can be achieved with Gal-1/SIT therapy by inhibiting mast cell activation and facilitating Treg development.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score0.457

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)0.0000.000

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 head0.021
GPT teacher head0.282
Teacher spread0.261 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

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