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Strong fluid movement near mammalian skeletal muscle improves their stability for force and metabolic studies at 37°C

2016· article· en· W4389026893 on OpenAlexaffabout
Jean‐Marc Renaud, Mathieu Paquette

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsChemistryGlycogenSkeletal muscleMuscle contractionBiophysicsInternal medicineEndocrinologyBiochemistryBiologyMedicine

Abstract

fetched live from OpenAlex

Studying mammalian skeletal muscles at 37°C is challenging because of a high metabolic rate requiring rapid O2 diffusion and more importantly a high production rate of reactive oxygen species (ROS) requiring fast removal. Both problems can be circumvented when small muscles up to 10 mg wet weight are immersed in physiological solutions and superfused near the surface with rapid solution flow (10–15 ml/min). However, rapid superfusion is not feasible when using expensive drugs and radioactive markers representing conditions that require muscle incubation in small volume. The objective of this study was therefore to optimize the experimental conditions that will give rise to stable muscle preparations at 37°C when a rapid flow of physiological saline solution is not an option. We tested the hypothesis that in the absence of superfusion, one must create fluid movement in order to replace the convection effect of superfusion. Tetanic force can be maintained constant for hours when mouse FDB bundles (1–2 mg wet weight) were superfused whereas a continuous loss of force reaching 10% force by 25 min was observed when superfusion was interrupted. The decrease in tetanic force during fatigue, elicited with one contraction every sec for 3 min, was significantly faster and the final extent of the decrease greater in the absence than in the presence of superfusion. There was, on the other hand, no difference in the decrease in tetanic force between superfusion and gas bubbling both prior to fatigue and during fatigue. A lack of superfusion was also important for the glycogen content. Glycogen levels prior to fatigue were 119 μmoles glucose equivalent/g dry weight when superfusion was maintained while interrupting superfusion for 10 min reduced glycogen levels to 82 μmoles/g dry weight; representing a decrease of 37 μmoles/g dry weight. Following fatigue, glycogen levels were 77 μmoles/g dry muscle with superfusion and 43 μmoles/g dry weight in the absence of superfusion. Notably, the amount of glycogen depletion during fatigue was not different with and without superfusion, the values being 42 and 39 μmoles/g dry weight, respectively. The effect of interrupting superfusion on tetanic force was much greater following a first fatigue bout and 50 min recovery. That is, interrupting superfusion resulted in 35% decrease in tetanic force within 10 min, which was again prevented by gas bubbling or the addition of NAC or tiron, two known ROS scavengers. So, a lack of fluid movement near mammalian skeletal muscle muscles at 37°C results in large loss of tetanic force and glycogen. The loss of tetanic force can be prevented by applying gas bubbling near the muscle or by scavenging ROS. Support or Funding Information Study was supported by the National Science and Engineering Research Council of Canada (NRC)

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)0.001
metaresearch head score (Gemma)0.001
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.027
GPT teacher head0.270
Teacher spread0.242 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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