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Record W2948856752 · doi:10.1002/bies.201800257

The Microbiome Mediates Environmental Effects on Aging

2019· review· en· W2948856752 on OpenAlexafffund
B. Brett Finlay, Sven Pettersson, Melissa K. Melby, Thomas C. G. Bosch

Bibliographic record

VenueBioEssays · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British ColumbiaCanadian Institute for Advanced Research
FundersCanadian Institutes of Health ResearchDeutsche Forschungsgemeinschaft
KeywordsLongevityMicrobiomeBiologyStressorDysbiosisEvolutionary biologyBioinformaticsGeneticsNeuroscience

Abstract

fetched live from OpenAlex

Humans' indigenous microbes strongly influence organ functions in an age- and diet-dependent manner, adding an important dimension to aging biology that remains poorly understood. Although age-related differences in the gut microbiota composition correlate with age-related loss of organ function and diseases, including inflammation and frailty, variation exists among the elderly, especially centenarians and people living in areas of extreme longevity. Studies using short-lived as well as nonsenescent model organisms provide surprising functional insights into factors affecting aging and implicate attenuating effects of microbes as well as a crucial role for certain transcription factors like forkhead box O. The unexpected beneficial effects of microbes on aged animals imply an even more complex interplay between the gut microbiome and the host. The microbiome constitutes the major interface between humans and the environment, is influenced by biosocial stressors and behaviors, and mediates effects on health and aging processes, while being moderated by sex and developmental stages.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.300
Teacher spread0.282 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations45
Published2019
Admission routes2
Has abstractyes

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