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Record W2071294952 · doi:10.1002/eji.200939910

TGF‐β affects development and differentiation of human natural killer cell subsets

2010· article· en· W2071294952 on OpenAlex
David Allan, Basya Rybalov, Génève Awong, Juan Carlos Zúñiga‐Pflücker, Hernan D. Kopcow, James R. Carlyle, Jack L. Strominger

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEuropean Journal of Immunology · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health Research
KeywordsCD16BiologyProgenitor cellCD34HaematopoiesisCell biologyImmunologyLymphokine-activated killer cellInterleukin 21Peripheral bloodCellular differentiationStem cellImmune systemT cellGeneticsCD3Gene

Abstract

fetched live from OpenAlex

Human peripheral blood NK cells may be divided into two main subsets: CD56(bright)CD16(-) and CD56(dim)CD16(+). Since TGF-β is known to influence the development of many leukocyte lineages, its effects on NK cell differentiation either from human CD34(+)Lin(-) hematopoietic progenitor/stem cells in vitro or from peripheral blood NK cells were investigated. TGF-β represses development of NK cells from CD34(+) progenitors and inhibits differentiation of CD16(+) NK cells. Moreover, TGF-β also results in conversion of a minor fraction of CD56(bright)CD16(+) cells found in peripheral blood into CD56(bright)CD16(-) cells, highlighting a possible role of the former as a developmental intermediate and of TGF-β in influencing the genesis of NK subsets found in blood.

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 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 score0.253
Threshold uncertainty score0.621

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 integrity0.0000.001
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 head0.206
Teacher spread0.199 · 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