Negative biomarker based male fertility evaluation: Sperm phenotypes associated with molecular-level anomalies
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it