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Cholesterol‐lowering effects of Northern wild rice in LDL receptor knockout mice

2012· article· en· W3175927556 on OpenAlexaffabout
Mohammed H. Moghadasian, Khuong Le, Zhaohui Zhao, Tiffany Nicholson, Chunyan Goh, Paymahn Moghadasian, Fatemeh Askarian, Trust Beta

Bibliographic record

VenueThe FASEB Journal · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGABA and Rice Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCholesterolLDL receptorTriglycerideKnockout mouseBiologyWhite riceAnimal scienceEndocrinologyInternal medicineReceptorMedicineFood scienceLipoprotein

Abstract

fetched live from OpenAlex

We have recently reported that Northern wild rice produced in Manitoba, Canada possesses higher antioxidant properties as compared to conventional white rice. This observation plus other data from available literature suggest that Northern wild rice may have anti‐atherogenic properties. The objective of the current study was to investigate potential lipid‐modifying and anti‐atherogenic properties of Northern wild rice in LDL receptor knockout mice. Sixteen 4‐week male LDL receptor knockout mice were divided into two groups of 8 each. These experimental groups received a semi‐synthetic diet containing 0.06% (w/w) cholesterol; carbohydrates in this diet were replaced with Northern wild rice and used for the “treated group.” The experiment will be carried out for 24 weeks. Plasma total cholesterol and triglyceride levels were estimated at baseline and every four weeks using standard enzymatic kits. Body weight and estimated 24‐hour food consumption were recorded. These data are summarized in the following Table. Data suggest strong cholesterol‐lowering activities for Northern wild rice in this animal model. It is most likely that such reductions in total cholesterol levels will lead to significant reductions in atherogenesis by the end of the study. This study was supported by Agriculture Research and Development Initiative (ARDI), Winnipeg, Manitoba, Canada.

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)0.001
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 score0.785
Threshold uncertainty score0.145

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.242
Teacher spread0.225 · 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

Citations0
Published2012
Admission routes2
Has abstractyes

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