A novel sperm FcR binding assay that assesses sperm fertilizing potential
Bibliographic record
Abstract
Freshly ejaculated mammalian sperm have poor fertilizing ability, with fertility only gained after sperm undergo capacitation and the acrosome reaction. To visualize exposed Fc receptors (FcRs), which occur during the acrosome reaction and whose absence has been related to infertility, a novel sperm FcR binding assay (FcR assay) was developed to assess fertilizing potential of sperm in proof-of-concept studies. A competition binding assay between sperm FcR and exogenously added FcR was used to assess whether the FcR was a functioning ligand in bull sperm. Once FcR was confirmed as a functional ligand, time-based expression of FcR was then evaluated in bull and human sperm using the FcR assay. This FcR assay was then used to evaluate fertility outcomes in cattle with cryopreserved intrauterine insemination (IUI) sperm, and to evaluate sperm FcR expression in patients undergoing IUI treatment in a prospective observational study. Time-based analyses of ejaculates from bull and human sperm demonstrated characteristic, reproducible sinusoidal patterns of FcR expression that corresponded to high and low periods of fertility potential in each species. The pregnancy rate in cattle approached statistical significance using the FcR assay results to inform optimum insemination timing windows versus conventional untimed methods (73.0% vs. 68.4%, respectively; p = 0.06; 95% confidence interval [CI]: 0.98, 1.57) with a 4.4% increase in the overall pregnancy rate. In patients undergoing IUI treatment, FcR expression patterns were identified where sperm were at their optimal fertilizing state, with overall pregnancy rates increasing from 21% to 42% (p = 0.01) when inseminations occurred during the windows where the fertilizing potential of the sperm was deemed optimal. These results suggest that sperm fertility potential is quantifiable in semen samples using our novel sperm FcR assay. Importantly, the FcR assay has the ability to identify optimal fertility windows in real-time, and also in the procedure ejaculates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".