The Beneficial Effects of Renal Transplantation on Altered Oxidative Status of ESRD Patients
Bibliographic record
Abstract
Renal transplantation (RT), has been considered the best therapeutic option for end stage renal disease (ESRD). Objective. To determine the effect of RT on the evolution of oxidative DNA status. Methods. Prospective cohort (N = 50 receptors of RT); genotoxic damage, 8-hydroxy-2'-deoxyguanosine (8-OHdG), and DNA repair enzyme, human 8-oxoguanine-DNA-N- glycosylase-1 (hOGG1); and antioxidants, superoxide dismutase (SOD) and glutathione peroxidase (GPx), were evaluated. Results. Before RT, 8-OHdG were significantly elevated (11.04 ± 0.90 versus 4.73 ± 0.34 ng/mL) compared to healthy controls (p = 0.001), with normalization after 6 months of 4.78 ± 0.34 ng/mL (p < 0.001). The same phenomenon was observed with hOGG1 enzyme before RT with 2.14 ± 0.36 ng/mL (p = 0.01) and decreased significantly at the end of the study to 1.20 ng/mL (p < 0.001) but was higher than controls, 0.51 ± 0.07 ng/mL (p < 0.03). Antioxidant SOD was elevated at 24.09 ± 1.6 IU/mL versus healthy controls (p = 0.001) before RT; however, 6 months after RT it decreased significantly to 16.9 ± 1.6 IU/mL (p = 0.002), without achieving the levels of healthy controls (p = 0.01). The GPx, before RT, was significantly diminished with 24.09 ± 1.6 IU/mL versus healthy controls (39.0 ± 1.58) (p = 0.01), while, in the final results, levels increased significantly to 30.38 ± 3.16 IU/mL (p = 0.001). Discussion. Patients with ESRD have important oxidative damage before RT. The RT significantly reduces oxidative damage and partially regulates the antioxidant enzymes (SOD and GPx).
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".