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Record W2127558641 · doi:10.1002/hep.24477

Hepatocyte cytotoxicity is facilitated by asialoglycoprotein receptor

2011· article· en· W2127558641 on OpenAlexafffund
Clifford S. Guy, Sherri L. Rankin, Tomasz I. Michalak

Bibliographic record

VenueHepatology · 2011
Typearticle
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsHealth Sciences CentreMemorial University of Newfoundland
FundersCanadian Institutes of Health Research
KeywordsAsialoglycoprotein receptorCytotoxicityHepatocyteChemistryCancer researchCell biologyMedicineBiologyBiochemistryIn vitro

Abstract

fetched live from OpenAlex

UNLABELLED: It has been recently identified that hepatocytes can act as cytotoxic effectors and can kill contacted cells by way of CD95 ligand-CD95 and perforin-dependent pathways. However, it remained unknown whether hepatocyte-mediated cell killing is indiscriminant or is directed toward targets with particular cell surface characteristics, as well as whether hepatocytes have the capacity to directly eliminate contacted lymphocytes. In this study, we found that desialylation of surface glycoproteins significantly augments cell susceptibility to hepatocyte-mediated killing. Using asialofetuin as a competitive ligand, and by silencing gene transcription with specific small interfering RNA, we found that the asialoglycoprotein receptor (ASGPR) is involved in hepatocyte recognition of cells predestined for killing, including activated autologous T lymphocytes. CONCLUSION: Hepatocytes are constitutively equipped in the molecular machinery capable of eliminating cells brought into contact with their surface in a manner that is reliant, at least in part, upon the recognition of terminally desialylated glycoproteins by hepatocyte ASGPR. The study adds a new dimension to the physiological role of hepatic ASGPR and provides further evidence that hepatocytes can actively contribute to intrahepatic immune regulation and moderation of the local inflammatory response.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.044
GPT teacher head0.254
Teacher spread0.210 · 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; both teacher heads agree on what is shown here.

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

Citations36
Published2011
Admission routes2
Has abstractyes

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