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Record W2121676993 · doi:10.1017/s0007114509991036

Wild rice (<i>Zizania latifolia</i>(Griseb) Turcz) improves the serum lipid profile and antioxidant status of rats fed with a high fat/cholesterol diet

2009· article· en· W2121676993 on OpenAlexaff
Hong Zhang, Pei Cao, Luis B. Agellon, Chengkai Zhai

Bibliographic record

VenueBritish Journal Of Nutrition · 2009
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsMalondialdehydeCholesterolFood scienceAntioxidantOxidative stressSuperoxide dismutaseStarchHigh cholesterolCarbohydrateChemistryBiologyBiochemistry

Abstract

fetched live from OpenAlex

The diet consumed by urban residents in modern China has become rich in saturated fats and cholesterol. In addition, the diet is high in carbohydrates from white rice and processed wheat starch. The aim of the present study was to determine the effects of replacing white rice and processed wheat starch with wild rice (WR) as the chief source of dietary carbohydrates. Rats fed with the diet patterned after the diet consumed by city residents of modern China showed elevated serum lipid levels comparable with rats consuming a high fat/cholesterol diet known to induce hyperlipidaemia in this species. Meanwhile, rats consuming the city diet with WR as the carbohydrate source suppressed the increase in serum TAG and total cholesterol, and the decrease in HDL cholesterol level. In addition, the rats fed the WR diet suppressed the build-up of oxidative stress by improving antioxidant capacity, increasing superoxide dismutase activity and reducing malondialdehyde concentration, both in the serum and liver. These findings illustrate that WR is effective in suppressing hyperlipidaemia and oxidative stress in rats even when the diet consumed is high in fat and cholesterol.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations39
Published2009
Admission routes1
Has abstractyes

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