Inflammation Alters the Secretome and Immunomodulatory Properties of Human Skin-Derived Precursor Cells
Bibliographic record
Abstract
Human skin-derived precursors (SKP) represent a group of somatic stem/precursor cells that reside in dermal skin throughout life that harbor clinical potential. SKP have a high self-renewal capacity, the ability to differentiate into multiple cell types and low immunogenicity, rendering them key candidates for allogeneic cell-based, off-the-shelf therapy. However, potential clinical application of allogeneic SKP requires that these cells retain their therapeutic properties under all circumstances and, in particular, in the presence of an inflammation state. Therefore, in this study, we investigated the impact of pro-inflammatory stimulation on the secretome and immunosuppressive properties of SKP. We demonstrated that pro-inflammatory stimulation of SKP significantly changes their expression and the secretion profile of chemo/cytokines and growth factors. Most importantly, we observed that pro-inflammatory stimulated SKP were still able to suppress the graft-versus-host response when cotransplanted with human PBMC in severe-combined immune deficient (SCID) mice, albeit to a much lesser extent than unstimulated SKP. Altogether, this study demonstrates that an inflammatory microenvironment has a significant impact on the immunological properties of SKP. These alterations need to be taken into account when developing allogeneic SKP-based therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".