Time-Course Evaluation of Oxidative Stress-Related Biomarkers after Renal Transplantation
Bibliographic record
Abstract
Patients with chronic renal disease have a high prevalence of oxidative stress (OS), which is associated with the cardiovascular complications occurring in this population. The restoration of kidney function after kidney transplantation (KT) can lead to reduction in the metabolic abnormalities and elimination of the OS. Time-dependent changes in OS-related markers and specific kidney function and metabolic parameters were evaluated in patients (N = 39; 23 males; 16 females; mean age = 57 ± 10 years) before (day 0) and after KT (day 1, 7, 30, 90, and 180) to monitor the graft. In particular, total antioxidant capacity (TAC), levels of advanced oxidation protein products (AOPP), lipid peroxidation as thiobarbituric acid-reactive substances (TBARS) and reduced glutathione (GSH); activities of glutathione peroxidase, catalase, and superoxide dismutase; and kidney function markers were measured. AOPP, TAC, and TBARS were significantly decreased, whereas GSH was significantly increased after KT. Antioxidant enzyme activities were not significantly changed during the monitored period after KT. Apropos specific kidney function markers and glomerular filtration significantly increased and creatinine level significantly decreased after transplantation. Changes in high-density lipoprotein cholesterol were also found. Our results show that successful KT results in normalization of the antioxidant status and lipid metabolism that is connected with both improved renal function and reduced cardiovascular complications.
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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) | 0.001 | 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 | 0.001 |
| 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".