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Record W2772125201 · doi:10.3390/cells6040048

Differential Location and Distribution of Hepatic Immune Cells

2017· review· en· W2772125201 on OpenAlexaff
Maria Alice Freitas‐Lopes, Kassiana Mafra, Bruna Araújo David, Raquel Carvalho-Gontijo, Gustavo Batista Menezes

Bibliographic record

VenueCells · 2017
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversity of Calgary
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsImmune systemHepatic stellate cellDistribution (mathematics)Differential (mechanical device)MedicineBiologyComputational biologyNeurosciencePathologyImmunologyPhysicsMathematics

Abstract

fetched live from OpenAlex

The liver is one of the main organs in the body, performing several metabolic and immunological functions that are indispensable to the organism. The liver is strategically positioned in the abdominal cavity between the intestine and the systemic circulation. Due to its location, the liver is continually exposed to nutritional insults, microbiota products from the intestinal tract, and to toxic substances. Hepatocytes are the major functional constituents of the hepatic lobes, and perform most of the liver's secretory and synthesizing functions, although another important cell population sustains the vitality of the organ: the hepatic immune cells. Liver immune cells play a fundamental role in host immune responses and exquisite mechanisms are necessary to govern the density and the location of the different hepatic leukocytes. Here we discuss the location of these pivotal cells within the different liver compartments, and how their frequency and tissular location can dictate the fate of liver immune responses.

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.289
Teacher spread0.258 · 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

Citations123
Published2017
Admission routes1
Has abstractyes

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