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Record W2006312951 · doi:10.1139/h11-156

Repeated high-intensity Wingate cycle bouts influence markers of lymphocyte migration but not apoptosis

2012· article· en· W2006312951 on OpenAlexvenueno aff
Rachel A. Friedman, James W. Navalta, Elizabeth A. Fedor, Holly B. Kell, Scott Lyons, Scott W. Arnett, Mark A. Schafer

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2012
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsnot available
Fundersnot available
KeywordsCD8LymphocyteApoptosisEndocrinologyChemistryInternal medicineCD19AndrologyImmunologyBiologyMedicineImmune systemBiochemistry

Abstract

fetched live from OpenAlex

Studies have shown significant changes in lymphocytes during continuous exercise, but little has been shown on the effect of repeated high intensity bouts. This study was designed to examine the effect of repeated intermittent bouts on lymphocyte subset cell count, apoptosis, and migration. A series of 6 Wingate anaerobic cycle tests were performed by participants (N = 8) with blood samples attained before, immediately following, and after a designated recovery period (excess postexercise oxygen consumption (EPOC)) to observe lymphocyte changes. Lymphocyte subsets (CD4+, CD4/CD45RA+, CD8+, CD8+/CD45RA+, CD19+) were assessed for apoptosis (annexin V+) and cellular migration (CX(3)CR1). Our results indicate that the CD8+ and CD8+/CD45RA+ subsets were significantly influenced by the repetitive Wingate cycling protocol such that cell counts increased with exercise, and then decreased at EPOC termination (p = 0.016). The observed postexercise decrease in CD8+ and CD8+/CD45RA+ cells was accompanied by a significant change in the CX(3)CR1 cell migration receptor (p = 0.019), but not apoptosis (p = 0.87). This indicates that with repetitive high-intensity cycling, the response in CD8+ cells following the bout is likely due to cell migration rather than cell death.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.012
GPT teacher head0.250
Teacher spread0.238 · 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

Citations20
Published2012
Admission routes1
Has abstractyes

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