Small Extracellular Vesicles from Inflamed Adipose Derived Stromal Cells Enhance the NF-κB-Dependent Inflammatory/Catabolic Environment of Osteoarthritis
Bibliographic record
Abstract
The last decade has seen exponentially growing efforts to exploit the effects of adipose derived stromal cells (ADSC) in the treatment of a wide range of chronic degenerative diseases, including osteoarthritis (OA), the most prevalent joint disorder. In the perspective of developing a cell-free advanced therapy medicinal product, a focus has been recently addressed to the ADSC secretome that lends itself to an allogeneic use and can be further dissected for the selective purification of small extracellular vesicles (sEVs). sEVs can act as “biological drug carriers” to transfer information that mirror the pathophysiology of the providing cells. This is important in the clinical perspective where many OA patients are also affected by the metabolic syndrome (MetS). ADSC from MetS OA patients are dysfunctional and “inflammatory” primed within the adipose tissue. To mimic this condition, we exposed ADSC to IL-1β, and then we investigated the effects of the isolated sEVs on chondrocytes and synoviocytes, either cultured separately or in co-culture, to tease out the effects of these “IL-1β primed sEVs” on gene and protein expression of major inflammatory and catabolic OA markers. In comparison with sEVs isolated from unstimulated ADSC, the IL-1β primed sEVs were able to propagate NF-κB activation in bystander joint cells. The effects were more prominent on synoviocytes, possibly because of a higher expression of binding molecules such as CD44. These findings call upon a careful characterization of the “inflammatory fingerprint” of ADSC to avoid the transfer of an unwanted message as well as the development of in vitro “preconditioning” strategies able to rescue the antiinflammatory/anticatabolic potential of ADSC-derived sEVs.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".