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Record W2031040204 · doi:10.4161/mabs.27029

IgE immunotherapy

2013· review· de· W2031040204 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuemAbs · 2013
Typereview
Languagede
FieldImmunology and Microbiology
TopicMast cells and histamine
Canadian institutionsSt. Thomas Hospital
FundersAcademy of Medical SciencesNational Institute for Health and Care ResearchCancer Research UK
KeywordsEffectorImmunoglobulin EImmune systemAntibodyImmunotherapyImmunologyCancer immunotherapyBlockadeImmune checkpointBiologyReceptorCancer research

Abstract

fetched live from OpenAlex

The importance of antibodies in activating immune responses against tumors is now better appreciated with the emergence of checkpoint blockade antibodies and with engineered antibody Fc domains featuring enhanced capacity to focus potent effector cells against cancer cells. Antibodies designed with Fc regions of the IgE class can confer natural, potent, long-lived immune surveillance in tissues through tenacious engagement of high-affinity cognate Fc receptors on distinct, often tumor-resident immune effector cells, and through ability to activate these cells under tumor-induced Th2-biased conditions. Here, we review the properties that make IgE a contributor to the allergic response and a critical player in the protection against parasites, which also support IgE as a novel anti-cancer modality. We discuss IgE-based active and passive immunotherapeutic approaches in disparate in vitro and in vivo model systems, collectively suggesting the potential of IgE immunotherapies in oncology. Translation toward clinical application is now in progress.

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.

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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.902
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1420.176

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 head0.022
GPT teacher head0.262
Teacher spread0.240 · 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

Quick stats

Citations55
Published2013
Admission routes1
Has abstractyes

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