Tissue‐resident mucosal‐associated invariant T (MAIT) cells in the human kidney represent a functionally distinct subset
Bibliographic record
Abstract
Abstract Mucosal‐associated invariant T (MAIT) cells are innate‐like T‐cells that recognize bacterial riboflavin metabolites. They are present in human blood but are abundant at barrier sites, including the liver, lungs, and kidneys, where they possess a CD69 + /CD103 +/− tissue‐resident phenotype. In renal tissue, MAIT cells likely defend against the ascending uropathogens responsible for urinary tract infections (UTIs), which are common, especially among renal transplant recipients (RTRs). Nevertheless, the functional role for MAIT cells in renal tissue and the influence of renal transplantation on MAIT cells remains unclear. Using multiparameter flow cytometry and the MR1‐tetramer, we characterized MAIT cell phenotype and function in healthy renal tissue (n = 6), renal transplants explanted after allograft failure (n = 14) and in blood from healthy controls (n = 20) and RTRs before and 1‐year after transplantation (n = 21). MAIT cells in renal tissue constitute a distinct CD69 + CD103 +/− population that displays typical phenotypic features of tissue‐resident T‐cells and is skewed toward IL‐2, GM‐CSF, and IL‐17A production upon stimulation. The circulating MAIT cell population was not decreased in number in RTRs pre‐ or post‐transplantation. Tissue‐resident MAIT cells in the kidney represent a functionally distinct population. This shows how MAIT cells in the kidney may be involved in the protection against microorganisms.
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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 | 0.000 |
| 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".