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Record W2749234203 · doi:10.1039/c7fo01631j

The influence of dietary fat and intestinal pH on calcium bioaccessibility: an<i>in vitro</i>study

2018· article· en· W2749234203 on OpenAlexaff
Elhaam Bandali, Yu U. Wang, Yaqi Lan, Michael A. Rogers, Sue A. Shapses

Bibliographic record

VenueFood & Function · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Guelph
FundersNew Jersey Agricultural Experiment StationU.S. Department of Agriculture
KeywordsCalciumFood scienceIn vitroChemistryBiochemistryEnvironmental chemistryAnimal scienceBiology

Abstract

fetched live from OpenAlex

In vivo studies measuring true fractional calcium (Ca) absorption have shown that dietary fat is a significant predictor of absorption and is influenced by luminal pH levels. However, whether changes in Ca bioaccessibility (CaB) can explain the effects on absorption has not been examined. In the current study, we examined two high fat diets enriched in either monounsaturated fatty acids or saturated fatty acids (SFA), and a low-fat diet (LFD), each with 50 mg Ca, and measured CaB at different intestinal regions during normal acidic or higher (pH = 7) gastrointestinal conditions using an in vitro gastrointestinal model. During normal pH conditions in the jejunum, there was an interaction between diet and time for CaB (P < 0.02), and CaB during the SFA diet was higher than LFD (P = 0.05). CaB was reduced by 90 ± 3% during higher compared with normal pH under all dietary conditions (P < 0.001). These findings indicate that fat intake, especially SFA enriched, is associated with a greater CaB in the jejunum, and may explain the higher Ca absorption in previous studies. In addition, the marked reduction in CaB under higher pH conditions could have implications in persons taking medications to reduce gastric acid.

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 score0.619
Threshold uncertainty score0.242

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 head0.021
GPT teacher head0.271
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

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

Citations20
Published2018
Admission routes1
Has abstractyes

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