Serum and Urine Tissue Kallikrein Concentrations in Male-to-Female Transsexuals Treated with Antiandrogens and Estrogens
Bibliographic record
Abstract
BACKGROUND: The expression of human tissue kallikrein genes is regulated by steroid hormones, but most studies have been conducted with cancer cell lines. Our purpose was to examine serum and urinary tissue kallikrein concentration changes in male-to-female transsexuals before and after treatment with antiandrogens and estrogens. METHODS: Thirty-five male-to-female transsexuals receiving cyproterone acetate and estrogens (orally or transdermally) were included in this study. Serum and urine samples were collected before initiation of therapy and 4 and 12 months post therapy. ELISAs were used to measure multiple kallikreins in serum and urine. RESULTS: After antiandrogen and estrogen therapy, serum testosterone concentrations decreased dramatically, as did serum and urinary concentrations of human glandular kallikrein (hK2) and prostate-specific antigen (PSA; hK3). Statistically significant but relatively small changes in serum and urinary concentrations of many other kallikreins were also seen. Kallikreins in serum and urine were correlated before and after treatment. CONCLUSIONS: The concentrations of hK2 and hK3, but not of any other kallikreins, decrease dramatically after combined antiandrogen and estrogen treatment in male-to-female transsexuals. The smaller responses of the other kallikreins presumably reflect their expression in multiple tissues.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".