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Record W2955745111 · doi:10.5772/intechopen.83485

Dietary Antioxidants in Experimental Models of Liver Diseases

2019· book-chapter· en· W2955745111 on OpenAlex

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

VenueIntechOpen eBooks · 2019
Typebook-chapter
Languageen
FieldMedicine
TopicBiochemical effects in animals
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFood scienceMedicineBiology

Abstract

fetched live from OpenAlex

Oxidative stress is caused by the imbalance between the amount of reactive oxygen species (ROS) and antioxidant capacity in the body. A balanced diet involving the daily intake of antioxidant-rich foods makes improvements in the total antioxidant capacity of individuals and would therefore reduce the incidence of oxidation-related diseases. It may also regulate the degree of oxidative stress. In fact, dietary micronutrients are either direct antioxidants or components of antioxidant enzymes, which may contribute positively to certain indicators of hepatic function. Liver plays an important role in the regulation of various processes such as metabolism, secretion, storage, and the clearance of endogenous and exogenous substances. Once liver is damaged by pursuing a wrong diet and inflammation takes place, most of these physiological functions get altered. Apart from drugs that used to treat the ailments, it is also necessary to determine the pharmaceutical alternatives for the drugs that are used in the treatment of liver diseases. Therefore, this chapter aims to summarize all known information on the effects of dietary nutrients on oxidative stress in experimental liver models.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.291
Teacher spread0.250 · 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