Secreted Protein Acidic and Rich in Cysteine (SPARC)—Mediated Exercise Effects: Illustrative Molecular Pathways against Various Diseases
Bibliographic record
Abstract
The strong benefits of exercise, in addition to the development of both the therapeutic applications of physical activity and molecular biology tools, means that it has become very important to explore the underlying molecular patterns linking exercise and its induced phenotypic changes. Within this context, secreted protein acidic and rich in cysteine (SPARC) has been characterized as an exercise-induced protein that would mediate and induce some important effects of exercise. Herein, we suggest some underlying pathways to explain such SPARC-induced exercise-like effects. Such mechanistic mapping would not only allow us to understand the molecular processes of exercise and SPARC effects but would also highlight the potential to develop novel molecular therapies. These therapies would be based on mimicking the exercise benefits via either introducing SPARC or pharmacologically targeting the SPARC-related pathways to produce exercise-like effects. This is of a particular importance for those who do not have the ability to perform the required physical activity due to disabilities or diseases. The main objective of this work is to highlight selected potential therapeutic applications deriving from SPARC properties that have been reported in various publications.
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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) | 0.001 | 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".