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Vascularity of myocardium and gastrocnemius muscle in rats selectively bred for endurance running capacity

2012· article· en· W2102170239 on OpenAlexaff
Patricia E. Beighley, M. Zamir, Robert J. Wentz, Lauren G. Koch, Steven L. Britton, Erik L. Ritman

Bibliographic record

VenuePhysiological Genomics · 2012
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsWestern University
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute of Diabetes and Digestive and Kidney DiseasesNational Center for Research ResourcesU.S. Public Health Service
KeywordsBiologyAerobic capacityAnatomyGastrocnemius muscleSkeletal muscleEndurance trainingVascularityInternal medicineEndocrinologyMedicineSurgery

Abstract

fetched live from OpenAlex

We tested the hypothesis that changes in the arteriolar branching architecture contributed to increased running capacity of rats subjected to two-way artificial selection for intrinsic aerobic endurance treadmill running capacity resulting in strains of low-capacity and high-capacity endurance rats. Hearts and gastrocnemius muscles were harvested from each strain, and the microvasculature's branching geometry measured from micro-CT images. The vascular branching geometry of the hearts and skeletal muscle from the high capacity was indistinguishable from low-capacity rats. Our hypothesis was not supported. Neither remodeling nor an increase in arteriolar microvasculature branching appears to play a role in the enhanced performance of the high capacity rats. We are led to speculate that endothelial tolerance for shear stress and/or increased coupling of myocardial muscle fiber metabolic-to-contractile function is increased in the high-capacity runner strain to the effect of allowing either higher flow rate per unit volume of muscle or more efficient use of oxygen and nutrients in the high-capacity endurance rats.

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)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.279
Teacher spread0.213 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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