Negative biomarker based male fertility evaluation: Sperm phenotypes associated with molecular-level anomalies
Bibliographic record
Abstract
Biomarker-based sperm analysis elevates the treatment of human infertility and ameliorates reproductive performance in livestock. The negative biomarker-based approach focuses on proteins and ligands unique to defective spermatozoa, regardless of their morphological phenotype, lending itself to analysis by flow cytometry (FC). A prime example is the spermatid specific thioredoxin SPTRX3/TXNDC8, retained in the nuclear vacuoles and superfluous cytoplasm of defective human spermatozoa. Infertile couples with high semen SPTRX3 are less likely to conceive by assisted reproductive therapies (ART) and more prone to recurrent miscarriage while low SPTRX3 has been associated with multiple ART births. Ubiquitin, a small, proteolysis-promoting covalent posttranslational protein modifier is found on the surface of defective posttesticular spermatozoa and in the damaged protein aggregates, the aggresomes of spermiogenic origin. Semen ubiquitin content correlates negatively with fertility and conventional semen parameters, and with sperm binding of lectins LCA (Lens culinaris agglutinin; reveals altered sperm surface) and PNA (Arachis hypogaea/peanut agglutinin; reveals acrosomal malformation or damage). The Postacrosomal Sheath WWI Domain Binding Protein (PAWP), implicated in oocyte activation during fertilization, is ectopic or absent from defective human and animal spermatozoa. Consequently, FC-parameters of PAWP correlate with ART outcomes in infertile couples and with fertility in bulls. Assays based on the above biomarkers have been combined into multiplex FC semen screening protocols, and the surface expression of lectins and ubiquitin has been utilized to develop nanoparticle-based bull semen purification method validated by field artificial insemination trials. These advances go hand-in-hand with the innovation of FC-technology and genomics/proteomics-based biomarker discovery.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".