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Record W3193279114 · doi:10.1038/s41598-021-96003-5

Ketogenesis controls mitochondrial gene expression and rescues mitochondrial bioenergetics after cervical spinal cord injury in rats

2021· article· en· W3193279114 on OpenAlexafffund
Oscar Seira, Kathleen Kolehmainen, Jie Liu, Femke Streijger, Anne Haegert, Stéphane LeBihan, Robert Boushel, Wolfram Tetzlaff

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsGenome British ColumbiaInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersRick Hansen FoundationCraig H. Neilsen Foundation
KeywordsMitochondrial biogenesisNeuroprotectionSpinal cord injuryBioenergeticsMitochondrionKetogenesisCell biologyBiologySpinal cordMedicinePharmacologyNeuroscienceEndocrinologyKetone bodiesMetabolism

Abstract

fetched live from OpenAlex

A better understanding of the secondary injury mechanisms that occur after traumatic spinal cord injury (SCI) is essential for the development of novel neuroprotective strategies linked to the restoration of metabolic deficits. We and others have shown that Ketogenic diet (KD), a high fat, moderate in proteins and low in carbohydrates is neuroprotective and improves behavioural outcomes in rats with acute SCI. Ketones are alternative fuels for mitochondrial ATP generation, and can modulate signaling pathways via targeting specific receptors. Here, we demonstrate that ad libitum administration of KD for 7 days after SCI rescued mitochondrial respiratory capacity, increased parameters of mitochondrial biogenesis, affected the regulation of mitochondrial-related genes, and activated the NRF2-dependent antioxidant pathway. This study demonstrates that KD improves post-SCI metabolism by rescuing mitochondrial function and supports the potential of KD for treatment of acute SCI in humans.

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)0.001
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 score0.186
Threshold uncertainty score0.833

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.291
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.

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

Citations37
Published2021
Admission routes2
Has abstractyes

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