The Versatility of the Pump-Perfused Rat Hindlimb Preparation: Examples Relating to Skeletal Muscle Function and Energy Metabolism
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".