Transcriptional Profile of Cytokines, Regulatory Mediators and TLR in Mesenchymal Stromal Cells after Inflammatory Signaling and Cell-Passaging
Bibliographic record
Abstract
Adult human subcutaneous adipose tissue (AT) harbors a rich population of mesenchymal stromal cells (MSCs) that are of interest for tissue repair. For this purpose, it is of utmost importance to determine the response of AT-MSCs to proliferative and inflammatory signals within the damaged tissue. We have characterized the transcriptional profile of cytokines, regulatory mediators and Toll-like receptors (TLR) relevant to the response of MSCs. AT-MSCs constitutively present a distinct profile for each gene and differentially responded to inflammation and cell-passaging. Inflammation leads to an upregulation of IL-6, IL-8, IL-1β, TNFα and CCL5 cytokine expression. Inflammation and cell-passaging increased the expression of HGF, IDO1, PTGS1, PTGS2 and TGFβ. The expression of the TLR pattern was differentially modulated with TLR 1, 2, 3, 4, 9 and 10 being increased, whereas TLR 5 and 6 downregulated. Functional enrichment analysis demonstrated a complex interplay between cytokines, TLR and regulatory mediators central for tissue repair. This profiling highlights that following a combination of inflammatory and proliferative signals, the sensitivity and responsive capacity of AT-MSCs may be significantly modified. Understanding these transcriptional changes may help the development of novel therapeutic approaches.
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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 | 0.001 |
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".