Urine Metabolite Analysis Offers Potential Early Diagnosis of Ovarian and Breast Cancers
Bibliographic record
Abstract
PURPOSE: Metabolomics is a new, rapidly expanding field dedicated to the global study of metabolites in biological systems. In this article metabolomics is applied to find urinary biomarkers for breast and ovarian cancer. EXPERIMENTAL DESIGN: Urine samples were collected from early- and late-stage breast and ovarian cancer patients during presurgical examinations and randomly from females with no known cancer. After quantitatively measuring a set of metabolites using nuclear magnetic resonance spectroscopy, both univariate and multivariate statistical analyses were employed to determine significant differences. RESULTS: Metabolic phenotypes of breast and ovarian cancers in comparison with normal urine and with each other revealed significance at Bonferroni-corrected significance levels resulting in unique metabolite patterns for breast and ovarian cancer. Intermediates of the tricarboxylic acid cycle and metabolites relating to energy metabolism, amino acids, and gut microbial metabolism were perturbed. CONCLUSIONS: The results presented here illustrate that urinary metabolomics may be useful for detecting early-stage breast and ovarian cancer.
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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".