Mesenchymal stromal cell priming in the treatment of systemic sclerosis
Bibliographic record
Abstract
Introduction: Systemic sclerosis (SSc) is characterized by immune dysfunction, vasculopathy, and fibrosis. Skin and lung fibrosis are attributable to high morbidity and mortality. Anti-fibrotic agents such as nintedanib slow the progression of lung fibrosis but do not improve the skin. Mesenchymal stromal cells (MSCs) have known anti-inflammatory, angiogenic, and potential anti-fibrotic properties. In vitro, human MSCs have anti-fibrotic properties, potentiated by priming with IFN-γ and TNF-α. However, it is unknown whether priming enhances MSC’s anti-fibrotic properties in vivo. Methods: On day 0, C57BL/6 male mice were implanted with minipumps containing phosphate buffered saline (PBS) or bleomycin (BLM; n=6/group). On day 7, PBS or 5x105 resting or primed MSCs were injected. Nintedanib was administered orally from days 7 to 21. On day 21, bronchoalveolar lavage fluid (BALF) was collected and cellularity assessed. Lungs and skin were collected for histology and RT-qPCR. Results: BLM-treated mice had increased BALF cellularity and protein content and increased lung and skin collagen and vasculopathy. Treatment with nintedanib and resting and primed MSCs reduced BALF cellularity and pulmonary collagen. Compared to resting, primed MSC-treated mice had significantly less protein in BALF (p=0.038), vasculopathy (p=0.014), and collagen mRNA (p=0.028) in the lungs. Both resting and primed MSCs, but not nintedanib, comparably reduced skin collagen (p<0.0001). Conclusion: Administration of MSCs reduced lung fibrosis to a similar extent as nintedanib. Priming may enhance the anti-fibrotic effects of MSCs in pulmonary fibrosis. Future work will optimize the use of MSCs and investigate the anti-fibrotic mechanisms.
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".