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Record W2773534799 · doi:10.1136/rmdopen-2017-000527

Synovial tissue macrophages: friend or foe?

2017· review· en· W2773534799 on OpenAlexaff
Mariola Kurowska‐Stolarska, Stefano Alivernini

Bibliographic record

VenueRMD Open · 2017
Typereview
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsInstitute of Infection and Immunity
FundersVersus ArthritisArthritis Research UKWellcome TrustWellcome
KeywordsMedicineSynovial membraneMacrophageProinflammatory cytokineRheumatoid arthritisImmunologyInflammationHomeostasisPopulationPathologySynovial jointArthritisMonocyteSynovial fluidTumor necrosis factor alphaMacrophage-activating factorOsteoarthritisImmune systemBiologyInternal medicine

Abstract

fetched live from OpenAlex

Healthy synovial tissue includes a lining layer of synovial fibroblasts and macrophages. The influx of leucocytes during active rheumatoid arthritis (RA) includes monocytes that differentiate locally into proinflammatory macrophages, and these produce pathogenic tumour necrosis factor. During sustained remission, the synovial tissue macrophage numbers recede to normal. The constitutive presence of tissue macrophages in the lining layer of the synovial membrane in healthy donors and in patients with RA during remission suggests that this macrophage population may have a role in maintaining and reinstating synovial tissue homeostasis respectively. Recent appreciation of the different origins and functions of tissue-resident compared with monocyte-derived macrophages has improved the understanding of their relative involvement in organ homeostasis in mouse models of disease. In this review, informed by mouse models and human data, we describe the presence of different functional subpopulations of human synovial tissue macrophages and discuss their distinct contribution to joint homeostasis and chronic inflammation in RA.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.004

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 head0.273
GPT teacher head0.522
Teacher spread0.249 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations125
Published2017
Admission routes1
Has abstractyes

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