Endocrine aberrations of human nonobstructive azoospermia
Bibliographic record
Abstract
Nonobstructive azoospermia (NOA) refers to the failure of spermatogenesis, which affects approximately 1% of the male population and contributes to 10% of male infertility. NOA has an underlying basis of endocrine imbalances since proper human spermatogenesis relies on complex regulation and cooperation of multiple hormones. A better understanding of subtle hormonal disturbances in NOA would help design and improve hormone therapies with reduced risk in human fertility clinics. The purpose of this review is to summarize the research on the endocrinological aspects of NOA, especially the hormones involved in hypothalamic-pituitary-testis axis (HPTA), including gonadotropin-releasing hormone, follicle-stimulating hormone, luteinizing hormone, prolactin, testosterone, estradiol, sex hormone binding globulin, inhibin B, anti-Müllerian hormone, and leptin. For the NOA men associated with primary testicular failure, the quality of currently available evidence has not been sufficient enough to recommend any general hormone optimization therapy. Some other NOA patients, especially those with hypogonadotropic hypogonadism, could be treated with hormonal replacement. Although these approaches have succeeded in resuming the fertility in many NOA patients, the prudent strategies should be applied in individuals according to specific NOA etiology by balancing fertility benefits and potential risks. This review also discusses how NOA can be induced by immunization against hormones.
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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 | 0.001 |
| 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) | 0.002 | 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".