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Record W4353087876 · doi:10.54097/hset.v36i.5702

Non-alcoholic Fatty Liver Disease: Pathology, Disease Models and Therapies

2023· article· en· W4353087876 on OpenAlex
Zhiyu Wu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2023
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLipotoxicityFatty liverDiseaseMedicineInsulin resistancePopulationPathogenesisReview articleBioinformaticsIntensive care medicinePathologyObesityEnvironmental healthBiology

Abstract

fetched live from OpenAlex

Non-alcoholic fatty liver disease (NAFLD) is characterized by a range of conditions induced through fat accumulation in the liver. This disease impacts population all around the world. NAFLD prevalence is rising at an alarming rate over the past years. To address the alarming increase in NAFLD prevalence, researchers are attempting to develop effective therapeutics to combat NAFLD. To develop NAFLD therapeutics, it is crucial to address current knowledge in NAFLD pathogenesis. Through summarizing current knowledge in NAFLD pathogenesis, researchers can better visualize current knowledge surrounding the disease and present knowledge gaps in the field. This review aims to deeply understand the role of three key NAFLD pathogenic factors: hepatic lipotoxicity, hepatic inflammation, and insulin resistance, and proposes potential target for NAFLD treatment. Furthermore, this review systematically summarizes current disease models and NAFLD therapies. In general, this review provides an overview of the progress of NAFLD and discusses reliable and practical models of NAFLD.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.249
Teacher spread0.235 · 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