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Record W2805094116 · doi:10.1038/s41598-018-25382-z

Longevity extension in Drosophila through gut-brain communication

2018· article· en· W2805094116 on OpenAlexafffund
Susan Westfall, Nikita Lomis, Satya Prakash

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsLongevityDrosophila (subgenus)Extension (predicate logic)Drosophila melanogasterBiologyComputer scienceGenetics

Abstract

fetched live from OpenAlex

Aging and chronic disease development are multifactorial processes involving the cumulative effects of metabolic distress, inflammation, oxidative stress and mitochondrial dynamics. Recently, variations in the gut microbiota have been associated with age-related phenotypes and probiotics have shown promise in managing chronic disease progression. In this study, novel probiotic and synbiotic formulations are shown to combinatorially extend longevity in male Drosophila melanogaster through mechanisms of gut-brain-axis communication with implications in chronic disease management. Both the probiotic and synbiotic formulations rescued markers of metabolic stress by managing insulin resistance and energy regulatory pathways. Both formulations also ameliorated elevations in inflammation, oxidative stress and the loss of mitochondrial complex integrity. In almost all the measured pathways, the synbiotic formulation has a more robust impact than its individual components insinuating its combinatorial effect. The concomitant action of the gut microbiota on each of the key risk factors of aging and makes it a powerful therapeutic tool against neurodegeneration, diabetes, obesity, cardiovascular disease and other age-related chronic diseases.

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)0.001
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 score0.283
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.276
Teacher spread0.257 · 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

Citations122
Published2018
Admission routes2
Has abstractyes

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