Verification of Male Infertility Biomarkers in Seminal Plasma by Multiplex Selected Reaction Monitoring Assay
Bibliographic record
Abstract
Seminal plasma is a promising biological fluid to use for noninvasive clinical diagnostics of male reproductive system disorders. To verify a list of prospective male infertility biomarkers, we developed a multiplex selected reaction monitoring assay and measured the relative abundance of 31 proteins in 30 seminal plasma samples from normal, nonobstructive azoospermia and post-vasectomy individuals. Median levels of some proteins were decreased by more than 100-fold in nonobstructive azoospermia or post-vasectomy samples, in comparison with normal samples. To follow up the most promising candidates and measure their concentrations in seminal plasma, heavy isotope-labeled internal standards were synthesized and used to reanalyze 20 proteins in the same set of samples. Concentrations of candidate proteins in normal seminal plasma were found in the range 0.1-1000 μg/ml but were significantly decreased in nonobstructive azoospermia and post-vasectomy. These data allowed us to select, for the first time, biomarkers to discriminate between normal, nonobstructive azoospermia, and post-vasectomy (simulated obstructive azoospermia) seminal plasma samples. Some testis-specific proteins (LDHC, TEX101, and SPAG11B) performed with absolute or nearly absolute specificities and sensitivities. Cell-specific classification of protein expression indicated that Sertoli or germ cell dysfunction, but not Leydig cell dysfunction, was observed in nonobstructive azoospermia seminal plasma. The proposed panel of biomarkers, pending further validation, could lead to a clinical assay that can eliminate the need for testicular biopsy to diagnose the category of male infertility, thus providing significant benefits to patients as well as decreased costs associated with the differential diagnosis of azoospermia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| 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 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".