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Record W4405088734 · doi:10.1101/2024.12.03.626404

Platelet bioenergetics correlate with skeletal muscle metabolism in C57BL/6J mice

2024· preprint· en· W4405088734 on OpenAlexaff
Mia Wilkinson, Emily J. Ferguson, J. P. Bureau, Jennifer Veeneman, Patrícia Lima, Chris McGlory, Kimberly J. Dunham‐Snary

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsQueen's University
Fundersnot available
KeywordsBioenergeticsSkeletal muscleMetabolismInternal medicineEnergy metabolismEndocrinologyPlateletOxygen metabolismChemistryMedicineBiochemistryOxygenMitochondrion

Abstract

fetched live from OpenAlex

Summary Skeletal muscle insulin resistance is a key step in progression of cardiometabolic disease, and impaired mitochondrial bioenergetics has been implicated. However, mitochondrial bioenergetic research in skeletal muscle is limited by the need for muscle biopsies. We sought to determine if platelet bioenergetics could be used as a minimally invasive surrogate for skeletal muscle bioenergetics. Multiple parameters of mitochondrial respiration, measured by high resolution respirometry, correlated between platelets and gastrocnemius muscle in mice. We propose the coupling state of platelet mitochondria reflects that of skeletal muscle in mice, providing a foundation for future research on using platelets as a liquid biopsy for muscle mitochondrial health in cardiometabolic disease, offering early insights into muscle metabolism to enhance clinical biomarker implementation. Graphical Abstract

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)0.000
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 score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0190.004

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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicAdipose Tissue and Metabolism→French-language works237,207→