Mucosa-associated invariant T cells drive early fibrosis progression in a CCl4-induced liver injury model 2465
Bibliographic record
Abstract
Abstract Description Mucosa-associated invariant T (MAIT) cells are a unique subset of innate-like T lymphocytes that have been shown to promote tissue repair. However, their role in the dysregulated state of fibrosis is not well understood. Therefore, we aimed to investigate the role of MAIT cells in hepatic fibrosis using the carbon tetrachloride (CCl4) mouse model of hepatic fibrosis. In this model, MAIT cell-enriched C56BL/6 (B6)-MAITCAST mice and MAIT cell-deficient Mr1-/- B6-MAITCAST mice were injected intraperitoneally with CCl4 or corn oil control twice a week for 12 or 26 days, representing time points of early fibrosis development and established fibrosis, respectively. At Day 12, CCl4-treated B6-MAITCAST mice displayed worse clinical outcomes, as evidenced by body weight loss and elevated serum alanine aminotransferase (ALT) levels compared to Mr1-/- B6-MAITCAST mice. Histological analysis revealed that CCl4-treated B6-MAITCAST mice also had increased liver myofibroblast activation and collagen deposition at Day 12. However, these differences were not present on Day 26. Flow cytometric analysis further revealed that hepatic MAIT cells were exhausted and skewed towards a Type-17 phenotype following CCl4 treatment at both time points. Together, these findings suggest that MAIT cells drive early fibrosis development through pro-inflammatory mechanisms, but their influence reduces as fibrosis progresses, indicating a time-dependent role in liver injury. Funding Sources Supported by an Ontario Graduate Scholarship and a Canadian Cancer Society Doctoral Award (co-funded by the Canadian Institutes of Health Research) Topic Categories Immune Response Regulation: Cellular Mechanisms (IRC)
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".