Human Oral Mucosal Stem Cells Reduce Anastomotic Leak in an Animal Model of Colonic Surgery
Bibliographic record
Abstract
BACKGROUND: Anastomotic leak is regarded as one of the most feared complications of bowel surgery; avoiding leaks is a major priority. Attempts to reduce or eliminate leaks have included alternate anastomotic techniques. Human oral mucosa stem cells (hOMSC) are self-renewing and expandable cells derived from buccal mucosa. Studies have shown that hOMSC can accelerate tissue regeneration and wound healing. The objective of this study was to evaluate whether hOMSC can decrease anastomotic leak rates in a murine model of colon surgery. METHODS: Two experiments were performed. In the first study, mice underwent colonic anastomosis using five interrupted sutures. hOMSC (n = 7) or normal saline (NS; n = 17) was injected into the colon wall at the site of the anastomosis. To evaluate whether hOMSC can impact anastomotic healing, the model was stressed by repeating the first experiment, reducing the number of sutures used for the construction of the anastomosis from five to four. Either hOMSC (n = 8) or NS (n = 20) was injected at the anastomosis. All mice that survived were sacrificed on postoperative day 7. Anastomotic leak rate, mortality, daily weight, and daily wellness scores were compared. RESULTS: In the five-suture anastomosis, there were no differences in anastomotic leak rate, mortality, or daily weight. Mice that received hOMSC had significantly higher wellness scores on postoperative day 2 (p < 0.05). In the four-suture anastomosis, there was a significant decrease in leak rate (70% [NS] vs. 25% [hOMSC], p = 0.029) and higher wellness scores in mice that received hOMSC (p < 0.05). CONCLUSION: Our study suggests that injecting hOMSC at the colonic anastomosis can potentially reduce anastomotic leak and improve postoperative wellness in a murine model of colon surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".