What Can Surrogate Tissues Tell Us about the Oxidative Stress Status of the Prostate? A Hypothesis-Generating In-Vivo Study
Bibliographic record
Abstract
BACKGROUND: Prostatic oxidative stress (OS) is androgen-regulated and a key event in the development of prostate cancer (PC). Thus, reducing prostatic OS is an attractive target for PC prevention strategies. We sought to determine if the individual's prostatic OS status can be determined by examining the OS in surrogate androgen regulated tissues from the same host. METHODOLOGY/PRINCIPAL FINDINGS: Adult male rats were divided equally into three groups: (A-) underwent bilateral orchiectomy, (A+) received continuous testosterone supplementation or (C) were eugonadal. Serum testosterone, 8-hydroxy-2-deoxyguanosine (8-OHdG) and anti-oxidative capacity (AOC) were determined after 72 hrs and the prostate, salivary glands and the hair follicles' Dermal Papillary Cells (DPC) from each animal were harvested, embedded into tissue microarray and examined for the expression of 8-OHdG by immuno-staining. Multi-variate regression was used to analyze inter-individual differences in OS staining within each androgen group and if there was a correlation between serum testosterone, 8-OHdG or AOC and Prostatic OS in tissues of same host. At the group level, 8-OHdG staining intensity directly correlated with serum testosterone levels in all three target tissues (p>0.01, Mann-Whitney Test). Although different levels of prostatic OS were noted between rats with similar serum testosterone levels and similar systemic OS measurements (p<0.01), there were no intra-individual differences between the OS status of the prostate and DPC (p<0.05). CONCLUSIONS/SIGNIFICANCE: The level of prostatic OS is correlated with the OS of hair follicles and salivary glands, but not systemic OS. Moreover, systemic AOC negatively correlates with both prostatic and hair follicle OS. This suggests that hair follicle and salivary gland OS can serve as surrogate markers for the efficiency of OS reduction. This has tremendous potential for the rational evaluation of patient response to prevention strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".