Protein expression pattern of PAWP in bull spermatozoa is associated with sperm quality and fertility following artificial insemination
Bibliographic record
Abstract
Post-acrosomal WW-domain binding protein (PAWP) is a signaling molecule located in the post-acrosomal sheath (PAS) of mammalian spermatozoa. We hypothesized that the proper integration of PAWP in the sperm PAS is reflective of bull-sperm quality and fertility. Cryopreserved semen samples from 298 sires of acceptable, but varied, fertility used in artificial insemination services were analyzed using immunofluorescence microscopy and flow cytometry for PAWP protein. In normal spermatozoa, PAWP fluorescence formed a regular band around the proximal PAS. Anomalies of PAWP labeling in defective spermatozoa were reflected in flow cytometry by varied intensities of PAWP-induced fluorescence. Distinct sperm phenotypes were also identified, including morphologically normal and some defective spermatozoa with moderate levels of PAWP; grossly defective spermatozoa with low/no PAWP; and defective spermatozoa with high PAWP. Analysis by ImageStream flow cytometry confirmed the prevalence of abnormal sperm phenotypes in the spermatozoa with abnormal PAWP content. Live/dead staining and video recording showed that some abnormal spermatozoa are viable and capable of progressive motility. Conventional flow-cytometric measurements of PAWP correlated significantly with semen quality and fertility parameters that reflect the sires' artificial insemination fertility, including secondary sperm morphology, conception rate, non-return rate, and residual value. A multiplex, flow-cytometric test detecting PAWP, aggresomes (ubiquitinated protein aggregates), and acrosomal integrity (peanut-agglutinin-lectin labeling) had a predictive value for conception rate, as demonstrated by step-wise regression analysis. We conclude that PAWP correlates with semen/fertility parameters used in the cattle artificial insemination industry, making PAWP a potential biomarker of bull fertility.
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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 | 0.000 | 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 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".