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Record W4409643150 · doi:10.1007/s10571-025-01555-z

Energy Metabolism and Brain Aging: Strategies to Delay Neuronal Degeneration

2025· review· en· W4409643150 on OpenAlexaff
Donghui Na, Z. Zhang, Meng Meng, Meiyu Li, Junyan Gao, Jiming Kong, Guohui Zhang, Ying Guo

Bibliographic record

VenueCellular and Molecular Neurobiology · 2025
Typereview
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of Manitoba
FundersNatural Science Foundation of Hebei Province
KeywordsEnergy metabolismNeuroscienceBrain agingMitochondrionDegeneration (medical)Brain functionMetabolismBiologyMedicineCognitionCell biologyEndocrinologyPathology

Abstract

fetched live from OpenAlex

Aging is characterized by a gradual decline in physiological functions, with brain aging being a major risk factor for numerous neurodegenerative diseases. Given the brain's high energy demands, maintaining an adequate ATP supply is crucial for its proper function. However, with advancing age, mitochondria dysfunction and a deteriorating energy metabolism lead to reduced overall energy production and impaired mitochondrial quality control (MQC). As a result, promoting healthy aging has become a key focus in contemporary research. This review examines the relationship between energy metabolism and brain aging, highlighting the connection between MQC and energy metabolism, and proposes strategies to delay brain aging by targeting energy metabolism.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
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)0.0000.000

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 head0.017
GPT teacher head0.290
Teacher spread0.273 · 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.

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

Citations29
Published2025
Admission routes1
Has abstractyes

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