Evidence for Functional Roles of MicroRNAs in Lineage SpecificationDuring Mouse and Human Preimplantation Development
Bibliographic record
Abstract
Proper formation of the blastocyst, including the specification of the first embryonic cellular lineages, is required to ensure healthy embryo development and can significantly impact the success of assisted reproductive technologies (ARTs).However, the regulatory role of microRNAs in early development, particularly in the context of preimplantation lineage specification, remains largely unknown.Taking a cross-species approach, this review aims to summarize the expression dynamics and functional significance of microRNAs in the differentiation and maintenance of lineage identity in both the mouse and the human.Findings are consolidated from studies conducted using in vitro embryonic stem cell models representing the epiblast, trophectoderm, and primitive endoderm lineages (modeled by naïve embryonic stem cells, trophoblast stem cells, and extraembryonic endoderm stem cells, respectively) to provide insight on what may be occurring in the embryo.Additionally, studies directly conducted in both mouse and human embryos are discussed, emphasizing similarities to the stem cell models and the gaps in our understanding, which will hopefully lead to further investigation of these areas.By unraveling the intricate mechanisms by which microRNAs regulate the specification and maintenance of cellular lineages in the blastocyst, we can leverage this knowledge to further optimize stem cell-based models such as the blastoids, enhance embryo competence, and develop methods of non-invasive embryo selection, which can potentially increase the success rates of assisted reproductive technologies and improve the experiences of those receiving fertility treatments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".