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Record W2108551059 · doi:10.1123/ijsnem.2014-0090

Oxidative Stress Markers After a Race in Professional Cyclists

2014· article· en· W2108551059 on OpenAlexfundno aff
Alfredo Córdova, M. Luisa Albina, Victòria Linares, Montserrat Bellés, Domènec J. Sánchez

Bibliographic record

VenueInternational Journal of Sport Nutrition and Exercise Metabolism · 2014
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsnot available
FundersCentro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y NutriciónWorld Anti-Doping Agency
KeywordsRace (biology)Oxidative stressMedicinePsychologyInternal medicineBiologyBotany

Abstract

fetched live from OpenAlex

The aim was to determine the levels and activities of the oxidative stress markers in erythrocytes, plasma, and urine after a flat cyclist stage. Eight voluntary male professional trained-cyclists participated in the study. Exercise significantly increased erythrocyte, leukocyte, platelet, and reticulocyte counts. The exercise induced significant increases in the erythrocyte activities of catalase (19.8%) and glutathione reductase (19.2%), while glutathione peroxidase activity decreased significantly (29.3%). Erythrocyte GSSG concentration was significantly increased after exercise (21.4%), whereas GSH was significantly diminished (20.4%). Erythrocyte malondialdehyde levels evidenced a significant decrease 3 h after finishing the stage (44.3%). Plasma malondialdehyde, GSH and GSSG levels significantly decreased after 3 hr recovery (26.8%, 48.6%, and 31.1%, respectively). The exercise significantly increased the F2-isoprostane concentration in urine from 359 ± 71 pg/mg creatinine to 686 ± 139 pg/mg creatinine. In conclusion, a flat cycling stage induced changes in oxidative stress markers in erythrocytes, plasma, and urine of professional cyclists. Urine F2-isoprostane is a more useful biomarker for assessing the effects of acute exercise than the traditional malondialdehyde measurement.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.392

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 head0.010
GPT teacher head0.299
Teacher spread0.289 · 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

Citations20
Published2014
Admission routes1
Has abstractyes

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