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Record W2759429543 · doi:10.3748/wjg.v23.i37.6777

Intrahepatic vascular changes in non-alcoholic fatty liver disease: Potential role of insulin-resistance and endothelial dysfunction

2017· review· en· W2759429543 on OpenAlexaff
Marcos Pasarín, Juan G. Abraldeṣ, Eleonora Liguori, Beverley Kok, Vincenzo La Mura

Bibliographic record

VenueWorld Journal of Gastroenterology · 2017
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInsulin resistanceFatty liverMetabolic syndromeEndothelial dysfunctionInsulinInternal medicineMedicineHyperinsulinemiaEndocrinologyLiver diseaseDiabetes mellitusCirrhosisNitric oxideAlcoholic hepatitisAlcoholic liver diseaseDisease

Abstract

fetched live from OpenAlex

Metabolic syndrome is a cluster of several clinical conditions characterized by insulin-resistance and high cardiovascular risk. Non-alcoholic fatty liver disease is the liver expression of the metabolic syndrome, and insulin resistance can be a frequent comorbidity in several chronic liver diseases, in particular hepatitis C virus infection and/or cirrhosis. Several studies have demonstrated that insulin action is not only relevant for glucose control, but also for vascular homeostasis. Insulin regulates nitric oxide production, which mediates to a large degree the vasodilating, anti-inflammatory and antithrombotic properties of a healthy endothelium, guaranteeing organ perfusion. The effects of insulin on the liver microvasculature and the effects of IR on sinusoidal endothelial cells have been studied in animal models of non-alcoholic fatty liver disease. The hypotheses derived from these studies and the potential translation of these results into humans are critically discussed in this review.

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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.286
Teacher spread0.264 · 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

Citations46
Published2017
Admission routes1
Has abstractyes

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