MétaCan
Menu
Back to cohort

Men Supplemented with 17b-Estardiol have Increased Skeletal Muscle Protein Content for b-Oxidation Enzymes

2010· article· en· W2051934550 on OpenAlexaff
Amy C. Maher, Mahmood Akhtar, Mark A. Tarnopolsky

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2010
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInternal medicineEndocrinologySkeletal muscleEstrogenMessenger RNALipid metabolismEnzymeMetabolismCarbohydrate metabolismDehydrogenaseLipid oxidationChemistryBiologyGeneBiochemistryMedicineAntioxidant

Abstract

fetched live from OpenAlex

During endurance exercise women have lower carbohydrate and higher lipid oxidation compared with men. Supplementation of humans and rodents with 17b-estradiol (E2) lowers the respiratory exchange ratio, glucose rate of appearance and disappearance, and metabolic clearance rate. The mechanism(s) for the observed estrogen effects in substrate utilization remains to be determined. PURPOSE: To determine if estrogen would increase the mRNA and protein content for genes involved in the regulation of b-oxidation. METHODS: Ten moderately active men were supplemented with placebo or E2 for 8 days in a randomized double-blind cross-over design. Following supplementation muscle biopsies were obtained from the vastus lateralis and examined for differences in mRNA, microRNA and protein content of genes involved in lipid oxidation. RESULTS: E2 increased the protein abundance of medium chain acyl-CoA dehydrogenase 35% (P<0.05), and tri-functional protein alpha 30% (P=0.08). PGC-1a mRNA was significantly higher after E2 supplementation (29%, P<0.05), and microRNA 103 and 29b (predicted to regulate PGC1a) were significantly lower (37% and 66% respectively, P<0.05). CONCLUSION: E2 regulates lipid metabolism in skeletal muscle by altering RNA and proteins involved in mitochondrial b-oxidation. This work was Supported by NSERC.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.296
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 designObservational
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

Citations1
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueMedicine & Science in Sports & ExerciseSame topicExercise and Physiological ResponsesFrench-language works237,207