Markers of Oxidative Stress and Inflammation in Ascites and Plasma in Patients with Platinum‐Sensitive, Platinum‐Resistant, and Platinum‐Refractory Epithelial Ovarian Cancer
Bibliographic record
Abstract
Diverse proinflammatory biomarkers and oxidative stress are strongly associated with advanced epithelial ovarian cancer (EOC). Objective. To determine the behavior of markers of oxidative stress and inflammation in plasma and ascites fluid in patients with platinum‐sensitive, platinum‐resistant, and platinum‐refractory EOC. Methods. A prospective cohort study. The colorimetric method was used to determine levels of the markers 8‐isoprostanes (8‐IP), lipid peroxidation products (LPO), and total antioxidant capacity (TAC) in plasma and ascites fluid; and with ELISA, the levels of interleukin‐6 (IL‐6) and tumor necrosis factor alpha (TNF‐α) were determined in patients with EOC. Results. In ascites fluid, a significant increase in 8‐IP versus baseline plasma levels was found (p = 0.002). There was an important leakage of the TAC levels in ascites fluid versus baseline plasma levels (p < 0.001). The IL‐6 was elevated in ascites fluid versus baseline plasma levels (p = 0.003), and there were diminished levels of TNF‐α in ascites fluid versus baseline plasma levels (p = 0.001). Discussion. We hypothesize that the ascites fluid influences the behavior and dissemination of the tumor. Deregulation between oxidants, antioxidants, and the proinflammatory cytokines was found to vary among platinum‐sensitive, platinum‐resistant, and platinum‐refractory patients.
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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".