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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".