Assessing spermatozoal small ribonucleic acids and their relationship to blastocyst development in idiopathic infertile males
Bibliographic record
Abstract
Abstract Clinical testing strategies for diagnosing male factor infertility are limited. A deeper analysis of spermatozoa-derived factors could potentially diagnose some cases of ‘unexplained infertility’. Spermatozoa carry a rich and dynamic profile of small RNAs, which have demonstrated potential developmental importance and association with fertility status. We used next-generation sequencing to correlate sperm small RNA profiles of normozoospermic males (n = 54) with differing blastocyst development rates, when using young donor oocytes. While ribosomal RNAs accounted for the highest number of sequencing reads, transfer RNA fragments of tRNA Gly/GCC and tRNA Val-CAC were the most abundant sequences across all sperm samples. A total of 324 small RNAs were differentially expressed between samples with high (n = 18) and low (n = 14) blastocyst rates ( p -adj < 0.05). Ninety three miRNAs were differentially expressed between these groups ( p -adj < 0.05). Differentially expressed transfer RNA fragments included: 5'-tRF-Asp-GTC; 5'-tRF-Phe-GAA; and 3'-tRF-Ser-GCA. Differentially expressed miRNAs included: let-7f-2-5p; miR-4755-3p; and miR-92a-3p. This study provides the foundation on which to validate a clinical panel of fertility-related sperm small RNAs, as well as to pursue potential mechanisms through which they alter blastocyst development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".