Impact of Breaking up of Sitting Time on Anti-inflammatory Response Induced by Extracellular Vesicles
Bibliographic record
Abstract
Abstract: Physical inactivity and sedentary behaviors (SB) have promoted a dramatic increase in the incidence of a host of chronic disorders over the last century. The breaking up of sitting time (i.e., sitting to standing up transition) has been proposed as a promising solution in several epidemiological and clinical studies. In parallel to the large interest it initially created, there is a growing body of evidence indicating that breaking up prolonged sedentary time (i.e., > 7 h in sitting time) could reduce overall mortality risks by normalizing the inflammatory profile and cardiometabolic functions. Recent advances suggest that the latter health benefits, may be mediated through the immunomodulatory properties of extracellular vesicles. Primarily composed of miRNA, lipids, mRNA and proteins, these vesicles would influence metabolism and immune system functions by promoting M1 to M2 macrophage polarization (i.e., from a pro-inflammatory to anti-inflammatory phenotype) and improving endothelial function. The outcomes of interrupting prolonged sitting time may be attributed to molecular mechanisms induced by circulating angiogenic cells. Functionally, circulating angiogenic cells contribute to repair and remodel the vasculature. This effect is proposed to be mediated through the secretion of paracrine factors. The present review article intends to clarify the beneficial contributions of breaking up sitting time on extracellular vesicles formation and macrophage polarization (M1 and M2 phenotypes). Hence, it will highlight key mechanistic information regarding how breaking up sitting time protocols improves endothelial health by promoting antioxidant and anti-inflammatory responses in human organs and tissues.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".