Determination of Prooxidant and Antioxidant Balance, Clinical Parameters and Nutrient Intakes in Hemodialysis Patients
Bibliographic record
Abstract
Background: There is evidence that hemodialysis (HD) leads to oxidative stress. However, reports about balance between oxidant and antioxidant are contradictory. Thus, the aim of this study was to evaluate the prooxidant-antioxidant balance (PAB) in HD patients and its correlation with clinical parameters and food intakes. Methods: This case-control study was performed in 35 HD patients and 35 healthy controls. Food intake was assessed by a 24-hour dietary recall questionnaire for at least 2 days. Routine biochemical parameters and C-reactive protein (CRP) were measured using the standard techniques. PAB was simultaneously measured by a new modified PAB assay using 3,3?,5,5?-tetramethylbenzidine benzidine (TMB). Results: PAB in HD patients was significantly higher compared with control group (88.2 ± 33 vs. 68.4 ± 23.3, P < 0.01). A significant positive correlation was established between PAB values and serum levels of creatinine (r = 0.38, P < 0.001), blood urea nitrogen (BUN) (r = 0.42, P < 0.0001) and CRP (r = 0.34, P < 0.001). An inverse correlation was found between PAB values and intake of vitamins A (r = -0.28, P < 0.05) and C (r = -0.31, P < 0.01) in HD patients. Conclusion: Since little data exits on the PAB assay in the HD patients, the high values of PAB indicated in our study need to be confirmed by further studies. World J Nephrol Urol. 2015;4(2):207-212 doi: http://dx.doi.org/10.14740/wjnu194w
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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) | 0.001 | 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".