Analysis of Seminal Plasma from Patients with Non-obstructive Azoospermia and Identification of Candidate Biomarkers of Male Infertility
Bibliographic record
Abstract
Infertility affects approximately 15% of couples with equivalent male and female contribution. Absence of sperm in semen, referred to as azoospermia, accounts for 5-20% of male infertility cases and can result from pretesticular azoospermia, non-obstructive azoospermia (NOA), and obstructive azoospermia (OA). The current clinical methods of differentiating NOA cases from OA ones are indeterminate and often require surgical intervention for a conclusive diagnosis. We catalogued 2048 proteins in seminal plasma from men presented with NOA. Using spectral-counting, we compared the NOA proteome to our previously published proteomes of fertile control men and postvasectomy (PV) men and identified proteins at differential abundance levels among these clinical groups. To verify spectral counting ratios for candidate proteins, extracted ion current (XIC) intensities were also used to calculate abundance ratios. The Pearson correlation coefficient between spectral counting and XIC ratios for the Control-NOA and NOA-PV data sets is 0.83 and 0.80, respectively. Proteins that showed inconsistent spectral counting and XIC ratios were removed from analysis. There are 34 proteins elevated in Control relative to NOA, 18 decreased in Control relative to NOA, 59 elevated in NOA relative to PV, and 16 decreased in NOA relative to PV. Many of these proteins have expression in the testis and the epididymis and are linked to fertility. Some of these proteins may be useful as noninvasive biomarkers in discriminating NOA cases from OA.
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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 | 0.000 | 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".