DNA damage in kidney transplant patients. Role of organ origin
Bibliographic record
Abstract
Chronic kidney disease (CKD) patients are characterized by elevated levels of genomic damage. This damage increases when kidney function decreases being maximum in hemodialysis patients. As kidney transplantation improves renal function, and it is related with better survival, the aim of our study was to evaluate potential changes in DNA damage levels after kidney transplantation, and comparing living donor recipients with cadaveric donor recipients. The alkaline comet assay was used to determine DNA breaks and oxidative damaged DNA; and the micronucleus assay was used to determine chromosomal breakage and/or aneuploidy. Fifty CKD patients were followed up after 6 and 12 months of their kidney transplantation. All patients increased their genomic damage levels after 6 and 12 months of renal transplantation, compared with those observed before transplantation, despite of the improvement of their metabolic functions. Donor advanced age correlated positively with higher DNA damage. Genomic damage was lower in living donor transplants with respect to cadaveric donor transplants. Our conclusion is that DNA damage increased in kidney transplantation patients, whereas their renal function improved. Higher levels of DNA damage were found in cadaveric donor transplants when compared to living donor transplants. Environ. Mol. Mutagen. 58:712-718, 2017. © 2017 Wiley Periodicals, Inc.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".