A<sub>1</sub>receptor activation decreases fatigue in mammalian slow-twitch skeletal muscle in vitro
Bibliographic record
Abstract
To test the hypothesis that adenosine improves skeletal muscle cell function, we exposed curarized mouse soleus and extensor digitorum longus (EDL) to a range of concentrations of adenosine (10(-9) M to 10(-5) M). Muscles contracted in Krebs-Henseleit bicarbonate buffer (27 degrees C, 95% O2 and 5% CO2) for 500 ms at 50 Hz once every 90 s. Soleus fatigued significantly less with adenosine present at concentrations of 10(-8) M and higher than with the Krebs-Henseleit vehicle control. Adenosine significantly improved force generation or delayed fatigue of EDL only with the initial adenosine challenge. To investigate the receptor population involved, we exposed soleus to agonists specific for A1 receptors (N6-cyclopentyladenosine, CPA), or A2 receptors (CGS 21680 hydrochloride, CGS), or A3 receptors (N6-benzyl-5'-N-ethylcarboxamidoadenosine, BNECA). CPA (A1) significantly decreased fatigue compared with the Krebs-Henseleit vehicle control at concentrations of 10(-9) M and higher. Muscles exposed to the A2 and A3 agonists did not differ from a Krebs-Henseleit plus methanol control. Phenylephrine (10(-6) M), an alpha-adrenergic agonist that increases the concentration of inositol triphosphate (IP3), significantly improved developed force in soleus. Neither a permeable cAMP analog, 8-bromo-cAMP (10(-5) M), nor a beta, agonist, isoproterenol (10(-6) M), had an effect on force generation in the soleus when compared with a saline control. Thus adenosine slowed fatigue in slow-twitch skeletal muscle through A1 receptors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".