Potential Implications of Exercise Training on Pannexin Expression and Function
Bibliographic record
Abstract
Pannexins (PANX1, 2, 3) are channel-forming glycoproteins that are expressed throughout the cardiovascular and musculoskeletal system. The canonical function of these proteins is to release nucleotides that act as purinergic signalling at the cell membrane or Ca2+ channels at the endoplasmic reticulum membrane. These two forms of signalling are essential for autocrine and paracrine signalling in health, and alterations in this signalling have been implicated in the pathogenesis of many diseases. Many musculoskeletal and cardiovascular diseases are largely the result of a lack of physical activity which causes altered gene expression. Considering exercise training has been shown to alter a wide array of gene expression in musculoskeletal tissues, understanding the interaction between exercise training, gene function and expression in relevant diseases is warranted. With regards to pannexins, multiple publications have shown that exercise training can influence pannexin expression and may influence the significance of its function in certain diseases. This review further discusses the potential interaction between exercise training and pannexin biology in relevant tissues and disease models. We propose that exercise training in relevant animal and human models will provide a more comprehensive understanding of the implications of pannexin biology in disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".