Th17 immune response to adipose tissue‐derived mesenchymal stromal cells
Bibliographic record
Abstract
Adipose tissue-derived mesenchymal stromal cells (ASCs) hold the promise of achieving successful immunotherapeutic results due to their ability to regulate different T-cell fate. ASCs also show significant adaptability to environmental stresses by modulating their immunologic profile. Cell-based therapy for inflammatory diseases requires a detailed understanding of the molecular relation between ASCs and Th17 lymphocytes taking into account the influence of inflammation and cell ratio on such interaction. Accordingly, a dose-dependent increase in Th17 generation was only observed in high MSC:T-cell ratio with no significant impact of inflammatory priming. IL-23 receptor (IL-23R) expression by T cells was not modulated by ASCs when compared to levels in activated T cells, while ROR-γt expression was significantly increased reaching a maximum in high (1:5) unprimed ASC:T-cell ratio. Finally, multiplex immunoassay showed substantial changes in the secretory profile of 15 cytokines involved in the Th17 immune response (IL-1β, IL-4, IL-6, IL-10, IL-17A, IL-17F, IL-22, IL-21, IL-23, IL-25, IL-31, IL-33, IFN-γ, sCD40, and TNF-α), which was modulated by both cell ratio and inflammatory priming. These findings suggest that Th17 lymphocyte pathway is significantly modulated by ASCs that may lead to immunological changes. Therefore, future ASC-based immunotherapy should take into account the complex and detailed molecular interactions that depend on several factors including inflammatory priming and cell ratio.
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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 | 0.001 |
| 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".