Paraxanthine, a caffeine metabolite, dose dependently increases [Ca<sup>2+</sup>]<sub>i</sub>in skeletal muscle
Bibliographic record
Abstract
It was hypothesized that the caffeine derivative paraxanthine results in subcontracture increases in intracellular calcium concentration ([Ca(2+)](i)) in resting skeletal muscle. Single fibers obtained from mouse flexor digitorum brevis were loaded with a fluorescent Ca(2+) indicator, indo 1-acetoxymethyl ester. After a stable baseline was recorded, the fiber was superfused with physiological salt solution (Tyrode) containing 0.5, 1.0, 2.5, or 5 mM paraxanthine, resulting in [Ca(2+)](i) increases of 6.4 +/- 2.5, 9.7 +/- 3.6, 26.8 +/- 11.7, and 39.6 +/- 9.6 nM, respectively. The increases in [Ca(2+)](i) were transient and were also observed with exposure to 5 mM theophylline and theobromine. Six fibers were exposed to 5 mM paraxanthine followed by 5 mM paraxanthine in the presence of 10 mM procaine (sarcoplasmic reticulum Ca(2+) release channel blocker). There was no increase from baseline [Ca(2+)](i) when fibers were superfused with paraxanthine and procaine, suggesting that the sarcoplasmic reticulum is the primary Ca(2+) source in the paraxanthine-induced response. In separate experiments, intact flexor digitorum brevis (n = 13) loaded with indo 1-acetoxymethyl ester had a significant increase in [Ca(2+)](i) with exposure to 0.01 mM paraxanthine. It is concluded that physiological and low pharmacological concentrations of paraxanthine result in transient, subcontracture increases in [Ca(2+)](i) in resting skeletal muscle, the magnitude of which is related to paraxanthine concentration.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".