In Vitro Cellular and Molecular Interplay between Human Foreskin-Derived Mesenchymal Stromal/Stem Cells and the Th17 Cell Pathway
Bibliographic record
Abstract
Foreskin, considered a biological waste material, has been shown to be a reservoir of therapeutic cells. The immunomodulatory properties of mesenchymal stromal/stem cells (MSCs) from the foreskin (FSK-MSCs) are being evaluated in cell-based therapy for degenerative, inflammatory and autoimmune disorders. Within the injured/inflamed tissue, proinflammatory lymphocytes such as IL-17-producing T helper cells (Th17) may interact with the stromal microenvironment, including MSCs. In this context, MSCs may encounter different levels of T cells as well as specific inflammatory signals. Uncovering the cellular and molecular changes during this interplay is central for developing an efficient and safe immunotherapeutic tool. To this end, an in vitro human model of cocultures of FSK-MSCs and T cells was established. These cocultures were performed at different cell ratios in the presence of an inflammatory setting. After confirming that FSK-MSCs respond to ISCT criteria by showing a typical phenotype and multilineage potential, we evaluated by flow cytometry the expression of Th17 cell markers IL-17A, IL23 receptor and RORγt within the lymphocyte population. We also measured 15 human Th17 pathway-related cytokines. Regardless of the T cell/MSC ratio, we observed a significant increase in IL-17A expression associated with an increase in IL-23 receptor expression. Furthermore, we observed substantial modulation of IL-1β, IL-4, IL-6, IL-10, IL-17A, IL-17F, IL-21, IL-22, IL-23, IL-25, IL-31, IL-33, INF-γ, sCD40, and TNF-α secretion. These findings suggest that FSK-MSCs are receptive to their environment and modulate the T cell response accordingly. The changes within the secretome of the stromal and immune environment are likely relevant for the therapeutic effect of MSCs. FSK-MSCs represent a valuable cellular product for immunotherapeutic purposes that needs to be further clarified and developed.
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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) | 0.002 | 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".