The Function of Pre-mRNA Alternative Splicing in Mammal Spermatogenesis
Bibliographic record
Abstract
Alternative pre-mRNA splicing plays important roles in co-transcriptional and post-transcriptional regulation of gene expression functioned during many developmental processes, such as spermatogenesis.The studies focusing on alternative splicing on spermatogenesis supported the notion that the development of testis is regulated by a higher level of alternative splicing than other tissues.Here, we aim to review the mechanisms underlying alternative splicing, particularly the splicing variants functioned in the process of spermatogenesis and the male infertility.There are five points regarding the alternative splicing including ⅰ) a brief introduction of alternative pre-mRNA splicing; ⅱ) the alternative splicing events in spermatogenesis-associated genes enriched in different stages of spermatogenesis; ⅲ) the mechanisms of alternative splicing regulation, such as splicing factors and m 6 A demethylation; ⅳ) the splice site recognition and alternative splicing, including the production and degradation of abnormal transcripts caused by gene variations and nonsense-mediated mRNA decay, respectively; ⅴ) abnormal alternative splicing correlated with male infertility.Taking together, this review highlights the impacts of alternative splicing and splicing variants in mammal spermatogenesis and provides new insights of the potential application of the alternative splicing into the therapy of male infertility.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".