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Record W2910762298 · doi:10.1139/cjpp-2018-0658

Caffeine attenuates contraction-induced diminutions of the intracellular calcium transient in mouse lumbrical muscle ex vivo

2019· article· en· W2910762298 on OpenAlexaffvenue
Ian C. P. Smith, Rene Vandenboom, A. Russell Tupling

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsBrock UniversityUniversity of CalgaryUniversity of Waterloo
Fundersnot available
KeywordsCaffeineCalciumContraction (grammar)ChemistryEndocrinologyMuscle contractionInternal medicineCalcium in biologySkeletal muscleInotropeEndoplasmic reticulumBiologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

The amount of calcium released from the sarcoplasmic reticulum in skeletal muscle rapidly declines during repeated twitch contractions. In this study, we test the hypothesis that caffeine can mitigate these contraction-induced declines in calcium release. Lumbrical muscles were isolated from male C57BL/6 mice and loaded with the calcium-sensitive indicator, AM-furaptra. Muscles were then stimulated at 8 Hz for 2.0 s in the presence or absence of 0.5 mM caffeine, at either 30 °C or 37 °C. The amplitude and area of the furaptra-based intracellular calcium transients and force produced during twitch contractions were calculated. For each of these measures, the values for twitch 16 relative to twitch 1 were higher in the presence of caffeine than in the absence of caffeine at both temperatures. We conclude that caffeine can attenuate contraction-induced diminutions of calcium release during repeated twitch contractions, thereby contributing to the inotropic effects of caffeine.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.013
GPT teacher head0.246
Teacher spread0.233 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

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