The effects of hemodialysis treatment on the level of <scp>DNA</scp> strand breaks and oxidative <scp>DNA</scp> lesions measured by the comet assay
Bibliographic record
Abstract
Hemodialysis patients have a higher risk for oxidative stress-related complications, such as cardiovascular disease and cancer. The increased level of oxidative stress is due to several factors, e.g., the hemodialysis treatment itself and the uremic state. In the present study, the effects of dialysis treatment on the level of DNA breaks and oxidative DNA lesions in mononuclear cells were measured with the comet assay. Factors possibly affecting DNA damage (reported as % DNA in tail) such as the duration of dialysis, time since last dialysis session, years of dialysis treatment, nutritional status (measured as protein catabolic rate), age, and diabetes were also investigated. The levels of DNA breaks (13.6 ± 4.7 before dialysis) and oxidative DNA lesions (7.9 ± 4.8 before dialysis) were significantly higher in dialysis patients (n = 31) compared to the levels of DNA breaks (5.8 ± 1.1) and oxidative DNA lesions (3.4 ± 1.7) in 10 healthy controls (P < 0.001). A decrease of DNA breaks was observed after dialysis (P = 0.038), and the level of oxidative DNA lesions was higher when the time between two treatment sessions were 68 hours compared to 44 hours (P < 0.001). Older subjects had a higher level of DNA breaks (P = 0.003), a good nutritional status predicted a lower level of DNA breaks (P < 0.001), and the duration of the dialysis session was inversely correlated with oxidative DNA lesions (P = 0.014). Diabetes or years of dialysis treatment did not affect DNA damage. The observations in the present study suggest that accumulation of uremic toxins induce DNA damage. The hemodialysis treatment seems to change the DNA damage.
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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 | 0.000 | 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".