Biomarkers of oxidant load and type‐specific clearance of prevalent oncogenic human papillomavirus infection: Markers of immune response?
Bibliographic record
Abstract
Human papillomavirus (HPV) infection is the cause of cervical cancer. Increased production of reactive oxygen species (ROS) maybe the common mechanism through which HPV-cofactors (i.e., smoking and inflammation) influence duration of infections. Biomarkers of total oxidant load may serve as cumulative measures of ROS exposure due to these cofactors. Therefore, we conducted a study evaluating the association between biomarkers of oxidant load and duration of HPV infections, early HPV natural history events. Serum samples were obtained from 444 HPV-positive women in the Ludwig-McGill Cohort Study. Anti-5-hydroxymethyl-2'-deoxyuridine autoantibody (anti-HMdU aAb) and malondialdehyde (MDA) were measured at baseline. Cox-proportional hazard models were used to estimate the probability of clearing any HPV, oncogenic HPV, non-oncogenic HPV and HPV-16 infections. Women with elevated MDA were significantly more likely to clear prevalent oncogenic HPV infections compared to those with lower MDA levels (Adjusted Hazard Ratio (AHR) = 2.7; 95%CI = 1.4-5.1). There did not appear to be an association between elevated MDA and clearance of incident oncogenic HPV infections. Similarly, women with elevated anti-HMdU aAb levels had higher rates of prevalent oncogenic HPV infection clearance (Quartile 3:AHR = 2.2; 95%CI = 1.2-4.4; Quartile 4:AHR = 2.4; 95%CI = 1.2-4.9). Higher levels of oxidant load biomarkers were associated with increased clearance of prevalent HPV infections. However, oxidant load biomarkers measured before incident infections were not associated, suggesting that the elevation of MDA and anti-HMdU aAb may reflect an ongoing effective immune response, such as increased innate immunity. More research focused on the immune responses to HPV and elevated markers of oxidant load is needed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".