Identification of novel nutrient sensitive human yolk sac functions required for embryogenesis
Bibliographic record
Abstract
The human yolk sac (hYS) is essential for embryo nutrient biosynthesis/transport and development. However, there lacks a comprehensive study of hYS nutrient-gene interactions. Here we performed a secondary analysis of hYS transcript profiles (n = 9 samples) to identify nutrient-sensitive hYS genes and regulatory networks, including those that associate with adverse perinatal phenotypes with embryonic origins. Overall, 14.8% highly expressed hYS genes are nutrient-sensitive; the most common nutrient cofactors for hYS genes are metals and B vitamins. Functional analysis of highly expressed hYS genes reveals that nutrient-sensitive hYS genes are more likely to be involved in metabolic functions than hYS genes that are not nutrient-sensitive. Through nutrient-sensitive gene network analysis, we find that four nutrient-sensitive transcription regulators in the hYS (with zinc and/or magnesium cofactors) are predicted to collectively regulate 30.9% of highly expressed hYS genes. Lastly, we identify 117 nutrient-sensitive hYS genes that associate with an adverse perinatal outcome with embryonic origins. Among these, the greatest number of nutrient-sensitive hYS genes are linked to congenital heart defects (n = 54 genes), followed by microcephaly (n = 37). Collectively, our study characterises nutrient-sensitive hYS functions and improves understanding of the ways in which nutrient-gene interactions in the hYS may influence both typical and pathological development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".