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Record W2988141874 · doi:10.1016/j.jhepr.2019.10.003

Intrahepatic macrophage populations in the pathophysiology of primary sclerosing cholangitis

2019· article· en· W2988141874 on OpenAlexaff
Yung‐Yi Chen, Kathryn Arndtz, Gwilym J. Webb, Margaret Corrigan, Sarah Akiror, Evaggelia Liaskou, Paul R. Woodward, David Adams, Chris J. Weston, Gideon M. Hirschfield

Bibliographic record

VenueJHEP Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsUniversity Health NetworkToronto Liver CentreUniversity of Toronto
FundersManchester Biomedical Research CentreBiotechnology and Biological Sciences Research CouncilDepartment of Health and Social CareMedical Research CouncilUniversity of BirminghamNational Institute for Health and Care ResearchBirmingham Biomedical Research CentreUniversity Hospitals Birmingham NHS Foundation Trust
KeywordsPrimary sclerosing cholangitisCD68SteatohepatitisCD14Fatty liverMedicineInternal medicineGastroenterologyPathologyLiver diseasePrimary biliary cirrhosisLiver transplantationChronic liver diseaseMacrophageBiologyImmunohistochemistryTransplantationReceptorDiseaseCirrhosis

Abstract

fetched live from OpenAlex

Background & Aims Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease characterized by progressive inflammatory and fibrotic injury to the biliary tree. We sought to further delineate the contribution of macrophage lineages in PSC pathobiology. Methods Human liver tissues and/or blood samples from patients with PSC, primary biliary cholangitis, other non-cholestatic/non-autoimmune diseases, including alcohol-related liver disease and non-alcoholic steatohepatitis, as well as normal liver, were sourced from our liver transplantation program. Liver fibrosis was studied using Van Gieson staining, while the frequencies of infiltrating monocyte and macrophage lineages, both in the circulation and the liver, were investigated by flow cytometry, including the expression of TGR-5, a G protein-coupled receptor (GPBAR1/TGR-5). Results Significantly higher frequencies of CD68 + CD206 + macrophages were detected in the livers of patients with PSC (median 19.17%; IQR 7.25–32.8%; n=15) compared to those of patients with other liver diseases (median 12.05%; IQR 5.61–16.03%; n=12; p = 0.0373). CD16 + monocytes, including both intermediate (CD14 + CD16 ++ ) and non-classical (CD14 dim CD16 ++ ) monocytes, were preferentially recruited into chronically diseased livers, with the highest recruitment ratios in PSC (median 15.83%; IQR 9.66–29.5%; n=15), compared to other liver diseases (median 6.66%; IQR 2.88–11.64%, n=14, p = 0.0152). The expression of TGR-5 on CD68 + intrahepatic macrophages was increased in chronic liver disease; TGR-5 expression on intrahepatic macrophages was highest in PSC (median 36.32%; IQR 17.71–63.61%; n=6) and most TGR-5 + macrophages were CD68 + CD206 + macrophages. Conclusions Underlying a potential role for macrophages in PSC pathobiology, we demonstrate, using patient-derived tissue, increased CD16 + monocyte recruitment and a higher frequency of CD68 + CD206 + macrophages in the livers of patients with PSC; the CD68 + CD206 + macrophage subset was associated with significantly higher TGR-5 expression in PSC. Lay summary Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease associated with progressive inflammation of the bile duct, leading to fibrosis and end-stage liver disease. In this study we explore the role of a type of immune cell, the macrophage, in contributing to PSC as a disease, hoping that our findings direct scientists towards new treatment targets. Our findings based on human liver and blood analyses demonstrate a greater frequency of a particular subset of immune cell, the CD68 + CD206 + macrophage, with significantly higher TGR-5 expression on this subset in PSC.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.031
GPT teacher head0.273
Teacher spread0.243 · 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".

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Citations61
Published2019
Admission routes1
Has abstractyes

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