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Record W2044829059 · doi:10.1139/h05-142

The Versatility of the Pump-Perfused Rat Hindlimb Preparation: Examples Relating to Skeletal Muscle Function and Energy Metabolism

2005· review· en· W2044829059 on OpenAlexaff
David J. Baker, Russell T. Hepple

Bibliographic record

VenueCanadian Journal of Applied Physiology · 2005
Typereview
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHindlimbPerfusionContext (archaeology)Skeletal muscleAnatomyBiologyChemistryMedicineInternal medicine

Abstract

fetched live from OpenAlex

The pump-perfused rat hindlimb model, in various forms, has been in use for several decades. There are many applications for this model, owing to the ability to control the content and rate of perfusion. In the context of exercise physiology this model has been put to particularly good use. In this report we summarize some of the central surgical differences between different versions of the pump-perfused rat hindlimb model, including the double hindlimb + trunk, double hindlimb alone, single hindlimb, and distal hindlimb-alone models. We also summarize specific elements of the perfusion medium and measurement of force used in our lab during assessment of muscle metabolic and contractile responses, and illustrate some of the differences from the in vivo condition that merit consideration. We then provide specific examples of how the single pump-perfused hindlimb and distal hindlimb-alone versions of this model have been used to study muscle function and energy metabolism. In this context we show how this model can be used to permit the experimenter to manipulate and control the rate of O(2)delivery and to add specific compounds that inhibit a particular aspect of muscle metabolism, such that in combination with measurements of the flux of specific substances across the muscle and/or fast-freezing of muscle after contractions, more can be understood about the metabolic state of the contracting muscles.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.257
Teacher spread0.237 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2005
Admission routes1
Has abstractyes

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