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Record W2163864973 · doi:10.2337/db15-0148

Evaluation of the Effect of Enteral Lipid Sensing on Endogenous Glucose Production in Humans

2015· article· en· W2163864973 on OpenAlexafffund
Changting Xiao, Satya Dash, Cecilia Morgantini, Khajag Koulajian, Gary F. Lewis

Bibliographic record

VenueDiabetes · 2015
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsEndogenyCholecystokininInternal medicineEndocrinologyEnteral administrationSalineDuodenumLipid metabolismBiologySmall intestineGlucose homeostasisInsulinParenteral nutritionMedicineInsulin resistance

Abstract

fetched live from OpenAlex

Administration of lipids into the upper intestine of rats has been shown to acutely decrease endogenous glucose production (EGP) in the preabsorptive state, postulated to act through a gut-brain-liver axis involving accumulation of long-chain fatty acyl-CoA, release of cholecystokinin, and subsequent neuronal signaling. It remains unknown, however, whether a similar gut-brain-liver axis is operative in humans. Here, we infused 20% Intralipid (a synthetic lipid emulsion) or saline intraduodenally for 90 min at 30 mL/h, 4 to 6 weeks apart, in random order, in nine healthy men. EGP was assessed under pancreatic clamp conditions with stable isotope enrichment techniques. Under these experimental conditions, intraduodenal infusion of Intralipid, compared with saline, did not affect plasma glucose concentration or EGP throughout the study period. We conclude that Intralipid infusion into the duodenum at this rate does not elicit detectable effects on glucose homeostasis or EGP in healthy men, which may reflect important interspecies differences between rodents and humans with respect to the putative gut-brain-liver axis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.054
GPT teacher head0.296
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2015
Admission routes2
Has abstractyes

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