MétaCan
Menu
Back to cohort
Record W3129051764 · doi:10.1242/bio.056515

A glucose-supplemented diet enhances gut barrier integrity in <i>Drosophila</i>

2021· article· en· W3129051764 on OpenAlexafffund
Anthony Galenza, Edan Foley

Bibliographic record

VenueBiology Open · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsBiologyDrosophila (subgenus)Cell biologyFood scienceGeneticsGene

Abstract

fetched live from OpenAlex

ABSTRACT Dietary intervention has received considerable attention as an approach to extend lifespan and improve aging. However, questions remain regarding optimal dietary regimes and underlying mechanisms of lifespan extension. Here, we asked how an increase of glucose in a chemically defined diet extends the lifespan of adult Drosophilamelanogaster. We showed that glucose-dependent lifespan extension is not a result of diminished caloric intake, or changes to systemic insulin activity, two commonly studied mechanisms of lifespan extension. Instead, we found that flies raised on glucose-supplemented food increased the expression of cell-adhesion genes, delaying age-dependent loss of intestinal barrier integrity. Furthermore, we showed that chemical disruption of the gut barrier negated the lifespan extension associated with glucose treatment, suggesting that glucose-supplemented food prolongs adult viability by enhancing the intestinal barrier. We believe our data contribute to understanding intestinal homeostasis, and may assist efforts to develop preventative measures that limit effects of aging on health.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
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 head0.023
GPT teacher head0.302
Teacher spread0.279 · 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

Citations10
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueBiology OpenSame topicGenetics, Aging, and Longevity in Model OrganismsFrench-language works237,207