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Record W3041860193 · doi:10.1126/science.aaw2622

Blood factors transfer beneficial effects of exercise on neurogenesis and cognition to the aged brain

2020· article· en· W3041860193 on OpenAlexaff
Alana Horowitz, Xuelai Fan, Gregor Bieri, Lucas K. Smith, Cesar Sanchez-Diaz, Adam B. Schroer, Géraldine Gontier, Kaitlin B. Casaletto, Joel H. Kramer, Katherine Williams, Saul Villeda

Bibliographic record

VenueScience · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsIONICS Mass Spectrometry (Canada)
FundersNational Institute on Aging
KeywordsNeurogenesisCognitionTreadmillPhysical exercisePsychologyPlasma levelsEndocrinologyInternal medicineNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Reversing brain aging may be possible through systemic interventions such as exercise. We found that administration of circulating blood factors in plasma from exercised aged mice transferred the effects of exercise on adult neurogenesis and cognition to sedentary aged mice. Plasma concentrations of glycosylphosphatidylinositol (GPI)-specific phospholipase D1 (Gpld1), a GPI-degrading enzyme derived from liver, were found to increase after exercise and to correlate with improved cognitive function in aged mice, and concentrations of Gpld1 in blood were increased in active, healthy elderly humans. Increasing systemic concentrations of Gpld1 in aged mice ameliorated age-related regenerative and cognitive impairments by altering signaling cascades downstream of GPI-anchored substrate cleavage. We thus identify a liver-to-brain axis by which blood factors can transfer the benefits of exercise in old age.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.028
GPT teacher head0.237
Teacher spread0.209 · 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

Citations500
Published2020
Admission routes1
Has abstractyes

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