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Record W2031766615 · doi:10.1017/s0007114510000553

Effects of oat bran, processed to different molecular weights of β-glucan, on plasma lipids and caecal formation of SCFA in mice

2010· article· en· W2031766615 on OpenAlexaff
Tina Immerstrand, Kristina E. Andersson, Caroline Wange, Ana Rascón, Per Hellstrand, Margareta Nyman, Steve W. Cui, Björn Bergenståhl, Christian Trägårdh, Rickard Öste

Bibliographic record

VenueBritish Journal Of Nutrition · 2010
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsAgriculture and Agri-Food Canada
FundersLunds Universitet
KeywordsCaecumButyric acidGlucanCholesterolChemistryFood scienceAcetic acidBranCholic acidPolysaccharideAvenaCecumBiochemistryBiologyInternal medicineBotanyOrganic chemistryMedicine

Abstract

fetched live from OpenAlex

In the present study, we evaluated the cholesterol-lowering effects of different oat bran (OB) preparations, differing regarding their peak molecular weight (MWp) of beta-glucans (2348, 1311, 241, 56, 21 or < 10 kDa), in C57BL/6NCrl mice. The diets were designed to be atherogenic (0.8 % cholesterol and 0.1 % cholic acid), and they reflected the Western diet pattern (41 % energy fat). All OB preparations that were investigated significantly reduced plasma cholesterol when compared with a cellulose-containing control diet, regardless of the molecular weight of beta-glucan. Moreover, the difference in viscous properties between the processed OB (from 0.11 to 17.7 l/g) did not appear to play a major role in the cholesterol-lowering properties. In addition, there was no correlation between the molecular weight of beta-glucan and the amount of propionic acid formed in caecum. Interestingly, however, there was a significant correlation between the ratio of (propionic acid+butyric acid)/acetic acid and the MWp of beta-glucans: the ratio increased with increasing molecular weight. The results of the present study suggest that the molecular weights and viscous properties of beta-glucan in oat products may not be crucial parameters for their cholesterol-lowering effects.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.356

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 communication0.0000.000
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 head0.005
GPT teacher head0.214
Teacher spread0.209 · 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

Citations51
Published2010
Admission routes1
Has abstractyes

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