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Fructans and Mineral Nutrition

2016· article· en· W2530818246 on OpenAlexvenueno aff
Noureddine Benkeblia

Bibliographic record

VenueJournal of Nutritional Therapeutics · 2016
Typearticle
Languageen
FieldNursing
TopicMicrobial Metabolites in Food Biotechnology
Canadian institutionsnot available
Fundersnot available
KeywordsFructanMineral absorptionFood scienceAbsorption (acoustics)ChemistryBiologySucrose

Abstract

fetched live from OpenAlex

Fructan molecules have a history of more than 150 years and ancient peoples used fructans containing plants as food, feed or medicine. The modern history of fructans began with their discovery by Rose (1804) and known at the turn of the 20th century considerable development with Edelman's proposal concerning their metabolism in higher plant. At present time, fructans are considered food not food ingredients, and are found in more than 500 food products resulting in significant daily consumption. Because the science of nutrition itself has changed, fructans are now considered as functional foods and the passionate history of their health benefits continues to arise interest of scientists. Contrary to the fact that non-digestible carbohydrates have been accused of causing an impairment in the small intestine absorption of minerals, research conducted during the last three decades demonstrated that fructans enhanced mineral absorption, and the scientific evidence claiming that fructans enhance mineral absorption is based on both animal and human experiments which are very conclusive. Although different hypotheses have been proposed to explain the roles of fructans in enhancing minerals absorption, the mechanisms behind this enhancement are still unclear, even though investigation have demonstrated that different fructans-related mechanisms may be involved in the increased absorption of minerals due to fructans intake. This review paper aims to report on the recent development and the roles of fructans in enhancing mineral absorption and their deficiencies prevention.

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 score0.681
Threshold uncertainty score0.317

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 head0.024
GPT teacher head0.279
Teacher spread0.254 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

Explore more

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