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Record W3210249191 · doi:10.1042/cs20190654

The immune niche of the liver

2021· article· en· W3210249191 on OpenAlexafffund

Bibliographic record

VenueClinical Science · 2021
Typearticle
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchPublic Health AgencyPublic Health Agency of Canada
KeywordsImmune systemInflammationNicheHomeostasisImmunityIdentification (biology)

Abstract

fetched live from OpenAlex

The liver is an essential organ that is critical for the removal of toxins, the production of proteins, and the maintenance of metabolic homeostasis. Behind each liver functional unit, termed lobules, hides a heterogeneous, complex, and well-orchestrated system. Despite parenchymal cells being most commonly associated with the liver's primary functionality, it has become clear that it is the immune niche of the liver that plays a central role in maintaining both local and systemic homeostasis by propagating hepatic inflammation and orchestrating its resolution. As such, the immunological processes that are at play in healthy and diseased livers are being investigated thoroughly in order to understand the underpinnings of inflammation and the potential avenues for restoring homeostasis. This review highlights recent advances in our understanding of the immune niche of the liver and provides perspectives for how the implementation of new transcriptomic, multimodal, and spatial technologies can uncover the heterogeneity, plasticity, and location of hepatic immune populations. Findings from these technologies will further our understanding of liver biology and create a new framework for the identification of therapeutic targets.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.079
GPT teacher head0.408
Teacher spread0.329 · 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

Citations111
Published2021
Admission routes2
Has abstractyes

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