o-Vanillin Modulates Cell Phenotype and Extracellular Vesicles of Human Mesenchymal Stem Cells and Intervertebral Disc Cells
Bibliographic record
Abstract
Human mesenchymal stem cell (hMSC) and extracellular vesicle (EV) therapy is a promising treatment for discogenic low back pain (LBP). Although promising, major obstacles remain to be overcome. Cellular senescence reduces self-renewal and multipotent potentials, and the senescence-associated secretory phenotype creates an inflammatory environment negatively affecting tissue homeostasis. Reducing senescence could therefore improve regenerative approaches. Ortho-Vanillin (o-Vanillin) has senolytic activity and anti-inflammatory properties and could be a valuable supplement to MSC and EV therapy. Here, we used direct co-culture experiments to evaluate proteoglycan synthesis, inflammatory mediators, and senescent cells in the presence or absence of o-Vanillin. EV release and transfer between hMSCs and intervertebral disc cells (DCs) was examined, and the effect on hMSC differentiation and DC phenotype was evaluated in the presence and absence of o-Vanillin. This study demonstrates that o-Vanillin affects cell communication, enhances hMSC differentiation and improves DC phenotype. Co-cultures of DCs and hMSCs resulted in increased proteoglycan synthesis, a decreased number of senescent cells and decreased release of the cytokines IL6 and 8. Effects that were further enhanced by o-Vanillin. o-Vanillin profoundly increased EV release and/or uptake by hMSCs and DCs. DC markers were significantly upregulated in both cell types in response to conditioned media of o-Vanillin treated donor cells. Collectively, this study demonstrates that o-Vanillin affects hMSC and DC crosstalk and suggests that combining hMSCs and senolytic compounds may improve the outcome of cell supplementation and EV therapy for LBP.
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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".