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Record W2023388424 · doi:10.1073/pnas.0804895105

RIG-I plays a critical role in negatively regulating granulocytic proliferation

2008· article· en· W2023388424 on OpenAlexaff
Nannan Zhang, Shuhong Shen, Linjia Jiang, Wu Zhang, Hongxin Zhang, Yueping Sun, Xian-Yang Li, Qiuhua Huang, Bao-Xue Ge, Sai‐Juan Chen, Zhugang Wang, Chen Zhu, Jiang Zhu

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2008
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsInstitute for Biological Sciences
FundersE-Institutes of Shanghai Municipal Education CommissionShanghai Municipal Education CommissionStrongUniversity of PennsylvaniaSamuel Waxman Cancer Research Foundation
KeywordsMyelopoiesisMyeloidBiologyCell biologyMyeloproliferative DisordersRegulatorInnate immune systemMyeloid cellsImmunityGeneImmunologyHaematopoiesisGeneticsImmune systemStem cell

Abstract

fetched live from OpenAlex

RIG-I has been implicated in innate immunity by sensing intracellular viral RNAs and inducing type I IFN production. However, we have found a significant RIG-I induction in a biological setting without active viral infection-namely, during RA-induced terminal granulocytic differentiation of acute myeloid leukemias. Here, we present evidence that a significant Rig-I induction also occurs during normal myelopoiesis and that the disruption of the Rig-I gene in mice leads to the development of a progressive myeloproliferative disorder. The initiation of progressive myeloproliferative disorder is mainly due to an intrinsic defect of Rig-I(-/-) myeloid cells, which are characterized by a reduced expression of IFN consensus sequence binding protein, a major regulator of myeloid differentiation. Thus, our study reveals a critical regulatory role of Rig-I in modulating the generation and differentiation of granulocytes.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.106
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.040
GPT teacher head0.305
Teacher spread0.265 · 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

Citations60
Published2008
Admission routes1
Has abstractyes

Explore more

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