Mesenchymal Stromal Cells as Potential Antimicrobial for Veterinary Use—A Comprehensive Review
Bibliographic record
Abstract
The emergence of "superbugs" resistant to antimicrobial medications threatens populations both veterinary and human. The current crisis has come about from the widespread use of the limited number of antimicrobials available in the treatment of livestock, companion animal, and human patients. A different approach must be sought to find alternatives to or enhancements of present conventional antimicrobials. Mesenchymal stromal cells (MSC) have antimicrobial properties that may help solve this problem. In the first part of the review, we explore the various mechanisms at work across species that help explain how MSCs influence microbial survival. We then discuss the findings of recent equine, canine, and bovine studies examining MSC antimicrobial properties in which MSCs are found to have significant effects on a variety of bacterial species either alone or in combination with antibiotics. Finally, information on the influence that various antimicrobials may have on MSC function is reviewed. MSCs exert their effect directly through the secretion of various bioactive factors or indirectly through the recruitment and activation of host immune cells. MSCs may soon become a valuable tool for veterinarians treating antimicrobial resistant infections. However, a great deal of work remains for the development of optimal MSC production conditions and testing for efficacy on different indications and species.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".