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Record W2038044883 · doi:10.1159/000234634

Human Basophilic Cell Differentiation Promoted by 2.5S Nerve Growth Factor

2009· article· en· W2038044883 on OpenAlexaff
Hiroshi Matsuda, Jan Switzer, Michael D. Coughlin, John Bienenstock, Judah A. Denburg

Bibliographic record

VenueInternational Archives of Allergy and Applied Immunology · 2009
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMast cells and histamine
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBasophilNerve growth factorEosinophilBasophilicImmunologyCellular differentiationCord bloodHistamineAllergic inflammationPeripheral blood mononuclear cellBiologyEndocrinologyGranulocyteHaematopoiesisInternal medicineIn vitroCell biologyInflammationImmunoglobulin EStem cellMedicinePathologyAntibodyReceptorBiochemistry

Abstract

fetched live from OpenAlex

In liquid cultures of human cord blood mononuclear cells, the activities of the 2.5S nerve growth factor (NGF) inducing basophil and eosinophil differentiation were investigated. Various concentrations of immunopurified 2.5S NGF derived from murine submaxillary glands were added to cultures with or without conditioned medium from a human T cell line (Mo-CM), which has previously been shown to produce activities stimulating granulocyte-macrophage colonies. Addition of NGF led to significant increases in differentiation of basophilic cells accompanied by histamine synthesis at 2 weeks in vitro; eosinophil differentiation was not increased in these cultures. In addition, NGF could be shown to amplify basophil differentiation induced by Mo-CM, and the activity of NGF inducing basophil differentiation was dependent on the presence of T lymphocytes. These results indicate that NGF stimulates T-lymphocyte-dependent basophilic cell differentiation from human cord blood progenitors and may in this way support differentiation of basophils or mast cells in vivo at sites of allergic tissue inflammation.

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 score0.161
Threshold uncertainty score0.781

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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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

Citations32
Published2009
Admission routes1
Has abstractyes

Explore more

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