Reply to Editorial Comment: Microbiomes in Post-DRE Urine Samples Are Linked to Prostate Cancer Risk
Bibliographic record
Abstract
We generally agree with the editorial comment1 regarding the findings of our study.2 Many pathogens are known to infect the prostate, including bacteria and viruses such as human papillomavirus and polyomavirus. However, their exact role in prostatic diseases remains elusive due to conflicting and controversial results from a limited number of studies. The presence of C. acnes in post-DRE urine samples of patients with cancer in our study also raises further questions regarding its role in the initiation and progression of this disease. Among 9 pathogens found in one study, C. acnes genes were detected in all prostate tumor samples and all adjacent samples, but not in prostate samples from healthy individuals.3 This study included tissue samples from both western and Chinese patients. The complete absence of C. acnes in the normal tissue samples from healthy individuals and its presence not only within tumors but also in tissue samples adjacent to tumors indicate tissue adjacent to tumors may also be undergoing tumor-related changes. C. acnes has been known to induce proinflammatory cytokines4 in both macrophages and keratinocytes5,6; thus, it may pose a higher risk of prostate cancer. The editorial comment is also correct that the presence of F. magna in the control group and its relationship to chemoprevention is a hypothesis at this stage. People consuming soy daily had soy isoflavones accumulating in the prostate tissue at levels four to six times more than in serum.7 Equol is shown to express estrogenic propertieaffs and can be a potent antagonist of dihydrotestosterone. These properties can have significant implications in prevention of prostate cancer and other androgen-related malignancies.8 One confounding factor is not everyone can produce equol at metabolic levels needed for chemoprevention.9 Although the presence of microbiota that can convert soy into equol and other soy metabolites in the control group is encouraging, it is not a guarantee of protection against prostate cancer. Further research with carefully selected patient groups, appropriate controls, and a combination of gene expression and metabolomic data will be required to better understand specific roles of microorganisms in the etiology of prostate cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".