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Record W4200032488 · doi:10.3390/life11121393

Physiological Doses of Hydroxytyrosol Modulate Gene Expression in Skeletal Muscle of Exercised Rats

2021· article· en· W4200032488 on OpenAlexaff
Rafael A. Casuso, S. Al Fazazi, Julio Plaza‐Díaz, Francisco Javier Ruiz‐Ojeda, Ascensión Rueda‐Robles, Jerónimo Aragón‐Vela, Jesús R. Huertas

Bibliographic record

VenueLife · 2021
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsChildren's Hospital of Eastern Ontario
FundersAgencia Estatal de InvestigaciónUniversidad de GranadaFundación Ramón Areces
KeywordsSkeletal muscleGLUT4HydroxytyrosolCD36EndocrinologyInternal medicineMessenger RNABeta oxidationSoleus muscleGene expressionDownregulation and upregulationWestern blotBiologyChemistryGlucose transporterBiochemistryGeneMetabolismInsulinMedicineAntioxidant

Abstract

fetched live from OpenAlex

We tested whether physiological doses of hydroxytyrosol (HT) may alter the mRNA transcription of key metabolic genes in exercised skeletal muscle. Two groups of exercise-trained Wistar rats, HTlow and HTmid, were supplemented with 0.31 and 4.61 mg/kg/d of HT, respectively, for 10 weeks. Another two groups of rats were not supplemented with HT; one remained sedentary and the other one was exercised. After the experimental period, the soleus muscle was removed for qRT-PCR and western blot analysis. The consumption of 4.61 mg/kg/d of HT during exercise increased the mRNA expression of important metabolic proteins. Specifically, 4.61 mg/kg/d of HT may upregulate long-chain fatty acid oxidation, lactate, and glucose oxidation as well as mitochondrial Krebs cycle in trained skeletal muscle. However, a 4.61 mg/kg/d of HT may alter protein translation, as in spite of the increment showed by CD36 and GLUT4 at the mRNA level this was not translated to higher protein content.

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 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 score0.132
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.282
Teacher spread0.255 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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