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Record W2903918176 · doi:10.3389/fphar.2018.01459

Herbal Extracts and Natural Products in Alleviating Non-alcoholic Fatty Liver Disease via Activating Autophagy

2018· review· en· W2903918176 on OpenAlexaff
Li Zhang, Zemin Yao, Guang Ji

Bibliographic record

VenueFrontiers in Pharmacology · 2018
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversity of Ottawa
FundersNational Natural Science Foundation of China
KeywordsAutophagyFatty liverSteatosisNonalcoholic fatty liver diseaseMedicineDiseaseChronic liver diseaseOxidative stressInflammationBioinformaticsPharmacologyApoptosisImmunologyBiologyInternal medicineCirrhosisBiochemistry

Abstract

fetched live from OpenAlex

Non-alcoholic fatty liver disease (NAFLD) is the most common form of chronic liver disease world-wide, and currently therapeutic options for NAFLD are limited. Herbal medicine (HM) may offer an attractive alternative for the treatment of NAFLD. Recent years have witnessed a growing interest in the autophagy-inducing agents, and autophagy activation has been recognized as an efficient strategy in managing NAFLD and related complications. Pharmacological studies have demonstrated certain potential of HM extracts and natural products in inducing autophagy, which might contribute to the efficacy of HM in preventing and treating NAFLD. This review aims to summarize current understanding of mechanisms of HM extracts and natural products in preventing and treating NAFLD. Specially, we focused on mechanisms by which autophagy can target the main pathogenesis events associated with NAFLD, including hepatic steatosis, inflammation, oxidative stress, and apoptosis. It is hoped that this brief review can provide a general understanding of HM extracts and natural products in treating NAFLD, and raise awareness of potential clinical application of HM in general.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.342
Teacher spread0.310 · 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 teacher head, not a consensus.

Study designOther design
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

Citations56
Published2018
Admission routes1
Has abstractyes

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