Semen biomarker TEX101 predicts sperm retrieval success for men with testicular failure
Bibliographic record
Abstract
Background: Azoospermia could be due to either obstruction (obstructive azoospermia: OA) or spermatogenic failure (non-obstructive azoospermia: NOA). Close to 50% of men with NOA have small pockets of sperm in the testis which could be retrieved surgically and then injected into oocytes in a program of intra-cytoplasmic sperm insertion. Presently, there are no accepted non-invasive tests allowing clinicians to predict the success rates of sperm retrieval. Previously, we have identified a germ cell-specific protein TEX101 in semen found in the primary spermatocytes and more mature sperm forms, but not in spermatogonia, Sertoli or Leydig cells. We hypothesized that the semen concentration of TEX101 could be used to predict sperm production in men with NOA. Methods: This was a prospective cohort study on men with NOA being treated at a male infertility centre. Men with NOA planning sperm retrieval provided 1–3 semen samples prior to surgery. Semen TEX101 concentrations were measured by an in-house-developed ELISA assay and compared with the results of the surgery to retrieve sperm. Results: 20/60 karyotypically normal men with NOA had semen TEX101 < LOD (<0.2ng/mL). Of these, 0% had successful sperm retrieval(0-17%: 95% CI) . In contrast, of the 40 men with TEX101> LOD, sperm was found in 50% (34-66%: 95% CI, sig diff. Fisher’s exact test, p<0.05). Conclusions: Undetectable (<0.2 ng/mL) semen TEX101 is highly predictive of sperm retrieval failure for karyotypically normal men with NOA and is the single strongest non-invasive predictor of sperm retrieval failure reported so far. Semen TEX101 concentration will help couples decide their individual chances of successful sperm retrieval.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".