Urinary Biomarkers of Oxidative Status in a Clinical Model of Oxidative Assault
Bibliographic record
Abstract
BACKGROUND: We used doxorubicin-based chemotherapy as a clinical model of oxidative assault in humans. METHODS: The study recruited newly diagnosed breast cancer patients (n = 23). Urine samples were collected immediately before (T0) and at 1 hour (T1) and 24 hours (T24) after i.v. administration of treatment. Measurements included allantoin and the isoprostanes iPF(2alpha)-III, iPF(2alpha)-VI, and 8,12-iso-iPF(2alpha)-VI along with the prostaglandin 2,3-dinor-iPF(2alpha)-III, a metabolite of iPF(2alpha)-III. All biomarkers were quantified using liquid chromatography-tandem mass spectrometry. RESULTS: In all subjects, the levels of the biomarkers increased at T1: allantoin by 22% (P = 0.06), iPF(2alpha)-III by 62% (P < 0.05), iPF(2alpha)-VI by 41% (P < 0.05), 8,12-iso-iPF(2alpha)-VI by 58% (P < 0.05), and 2,3-dinor-iPF(2alpha)-III by 52% (P < 0.05). At T24, the F2-isoprostanes returned to their baseline levels; the levels of allantoin continued to increase, although the T24-T0 difference was not statistically significant. CONCLUSIONS: These results indicate that urinary F2-isoprostanes are valid biomarkers and allantoin is a promising biomarker of oxidative status in humans. IMPACT: The levels of biomarkers change quickly in response to oxidative assault and can be used to monitor oxidative status in humans in response to treatments related either to generation of free radicals (chemotherapy and radiation therapy) or to antioxidants (inborn metabolic diseases and Down syndrome).
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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 | 0.001 | 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".