Absence of sperm RNA elements correlates with idiopathic male infertility
Bibliographic record
Abstract
Semen parameters are typically used to diagnose male infertility and specify clinical interventions. In idiopathic infertile couples, an unknown male factor could be the cause of infertility even when the semen parameters are normal. Next-generation sequencing of spermatozoal RNAs can provide an objective measure of the paternal contribution and may help guide the care of these couples. We assessed spermatozoal RNAs from 96 couples presenting with idiopathic infertility and identified the final reproductive outcome and sperm RNA elements (SREs) reflective of fecundity status. The absence of required SREs reduced the probability of achieving live birth by timed intercourse or intrauterine insemination from 73 to 27%. However, the absence of these same SREs does not appear to be critical when using assisted reproductive technologies such as in vitro fertilization with or without intracytoplasmic sperm injection. About 30% of the idiopathic infertile couples presented an incomplete set of required SREs, suggesting a male component as the cause of their infertility. Conversely, analysis of couples that failed to achieve a live birth despite presenting with a complete set of SREs suggested that a female factor may have been involved, and this was confirmed by their diagnosis. The data in this study suggest that SRE analysis has the potential to predict the individual success rate of different fertility treatments and reduce the time to achieve live birth.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".