Disruption of the Gut Microbiota with Antibiotics Increases Acute Vascular Rejection
Bibliographic record
Abstract
Introduction: Organ transplantation is the most effective treatment for end stage organ failure, but almost all transplants fail due to immune-mediated rejection. Vascular rejection is a component of acute rejection and contributes to the development of transplant arteriosclerosis, which is a main cause of chronic heart transplant failure. The endogenous gut microbiota influences many immunological processes and its disruption with antibiotics affects several immunopathological conditions but little is known about how these microbes affect organ graft rejection. Methods: Aortic interposition grafts were performed from Balb/c donor mice into the infrarenal aorta of allogeneic C57Bl/6 recipient mice. The gut microbiota was disrupted in graft recipients starting as neonates using an antibiotic cocktail (ampicillin, vancomycin, metronidazole, neomycin sulfate) in their drinking water. Results: Mice treated with antibiotics showed significantly decreased bacterial load in fecal samples as well as reduced splenic Tregs. In transplant recipients, ablation of gut bacteria with antibiotics led to a significant loss of medial area, reflective of increased acute rejection, but no difference in intimal thickening, reflective of transplant arteriosclerosis. Antibiotic treatment for only the first three weeks of life was sufficient to increase acute rejection. There was significantly more infiltration of CD4 T cells, CD8 T cells, and neutrophils into arteries transplanted into antibiotic-treated mice as compared to untreated counterparts. Further, treatment of graft recipients with antibiotics led to greater production of IFNg, IL-17 and IL-4 by T cells after transplantation and this was related to reduced expression of Foxp3, indicative of fewer Tregs. Conclusion: Disruption of the gut microbiota with antibiotics alters allogeneic immune responses and increases the development of acute vascular rejection.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".