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Record W2307128310 · doi:10.1093/intimm/dxq082

NK cells and NK receptors (PP-007)

2010· article· en· W2307128310 on OpenAlexaff
Hyo Won Jung, Benjamin Hsiung, Jorge Chavarría, David H. Raulet, Zhiyan Zhou, Chen‐Yu Zhang, Jian Zhang, Zhigang Tian, Paolo Carrega, Gaetana Pezzino, Paola Queirolo, Michela Falco, Daniela Pende, Alessandro Moretta, M Mingari, Lorenzo Moretta, Guido Ferlazzo, Shigemi Sasawatari, Mariko Yoshizaki, Kaori Furuyama-Tanaka, Andrew P. Makrigiannis, Takehiko Sasazuki, Kensuke Inaba, Noriko Toyama‐Sorimachi, Shintaro Kamizono, G S Duncan, Markus G. Seidel, Akira Morimoto, Koichi Hamada, Gerard C. Grosveld, Koichi Akashi, Evan Lind, Jillian Haight, Pamela S. Ohashi, A. Thomas Look, Tak W. Mak

Bibliographic record

VenueInternational Immunology · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversity of OttawaOntario Institute for Cancer Research
FundersUniversity of California, San FranciscoCenter for AIDS Research, University of WashingtonNational Institutes of Health
KeywordsReceptorChemistryCell biologyBiologyBiochemistry

Abstract

fetched live from OpenAlex

We observed a markedly high, but variable, Tim-3 expression on NK cells.Tim-3 was expressed on all CD56dim CD16+ NK cells and heterogeneously expressed on the CD56bright CD16-NK cell subset.We found that NK cells with a lower cell surface density of Tim-3 expression exhibit more effective functional activity.Antibody-mediated cross-linking of Tim-3 on NK cells suppressed their lytic activity.Taken together, Tim-3 is a novel marker that predicts the maturation status of NK cells and constitutes a novel system regulating NK cell function.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.005

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 head0.229
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2010
Admission routes1
Has abstractyes

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