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Record W3023775962 · doi:10.1016/j.immuni.2020.04.001

Niche-Specific Reprogramming of Epigenetic Landscapes Drives Myeloid Cell Diversity in Nonalcoholic Steatohepatitis

2020· article· en· W3023775962 on OpenAlexaff
Jason S. Seidman, Ty D. Troutman, Mashito Sakai, Anita Gola, Nathanael J. Spann, Hunter Bennett, Cassi M. Bruni, Zhengyu Ouyang, Rick Z. Li, Xiaoli Sun, Binh T. Vu, Martina P. Pasillas, Kaori M. Ego, David Gosselin, Verena M. Link, Ling-Wa Chong, Ronald M. Evans, Bonne M. Thompson, Jeffrey G. McDonald, Mojgan Hosseini, Joseph L. Witztum, Ronald N. Germain, Christopher K. Glass

Bibliographic record

VenueImmunity · 2020
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversité Laval
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesUniversity of California, San DiegoNational Cancer InstituteNational Institutes of HealthNational Heart, Lung, and Blood InstituteManpei Suzuki Diabetes FoundationNational Institute of General Medical SciencesAmerican Heart AssociationIsrael National Road Safety AuthorityNational Institute of Allergy and Infectious DiseasesDivision of Intramural Research, National Institute of Allergy and Infectious DiseasesFondation Leducq
KeywordsBiologyKupffer cellSteatohepatitisEpigeneticsReprogrammingTREM2PhenotypeMacrophageCell biologyTranscriptomeCellImmunologyGene expressionCancer researchFatty liverGeneGeneticsPathologyInflammationMicrogliaMedicineDisease

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0000.000
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 head0.040
GPT teacher head0.255
Teacher spread0.215 · 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 designObservational
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

Citations428
Published2020
Admission routes1
Has abstractno

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