Role of myokines in cardiovascular diseases and pre-analytical variables affecting their measurements
Bibliographic record
Abstract
Myokines, specifically cardiomyokines, have emerged as novel molecular targets that prevent and/or treat certain cardiovascular diseases due to its autocrine, paracrine and/or endocrine effects (1). In this regard, the interest in the field and the number of publications have increased during last years. In this Special Issue, Di Raimondo et al. provide an overview of the myokines potentially involved in cardiovascular prevention as well as the role of physical exercise on its expression (2). Moreover, the authors underline the importance of carrying out well-designed cross-sectional and longitudinal studies in order to clarify the implications of exercise-induced myokines in the context of cardiac rehabilitation (2). Importantly, in another review, Lombardi et al. also point out the foremost pre-analytical variables, which may affect the measurements of myokines with cardiovascular functions, such as sample collecting and storage (3). Pre-analytical procedures in myokines measurements are critical since exercise induces a wide-range of physiological adaptations in addition to modulating its expression (4). Therefore, the reader will find cutting edge contributions in this Special Issue. It has been a pleasure for me to bring together these important names in the field and I am convinced that this Special Issue will be extremely useful to both experienced and young scientists who are interested in the field of myokines, exercise, health, and 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".