Genomic imprinting, growth and maternal–fetal interactions
Bibliographic record
Abstract
ABSTRACT In the 1980s, mouse nuclear transplantation experiments revealed that both male and female parental genomes are required for successful development to term (McGrath and Solter, 1983; Surani and Barton, 1983). This non-equivalence of parental genomes is because imprinted genes are predominantly expressed from only one parental chromosome. Uniparental inheritance of these genomic regions causes paediatric growth disorders such as Beckwith–Wiedemann and Silver–Russell syndromes (reviewed in Peters, 2014). More than 100 imprinted genes have now been discovered and the functions of many of these genes have been assessed in murine models. The first such genes described were the fetal growth factor insulin-like growth factor 2 (Igf2) and its inhibitor Igf2 receptor (Igf2r) (DeChiara et al., 1991; Lau et al., 1994; Wang et al., 1994). Since then, it has emerged that most imprinted genes modulate fetal growth and resource acquisition in a variety of ways. First, imprinted genes are required for the development of a functional placenta, the organ that mediates the exchange of nutrients between mother and fetus. Second, these genes act in an embryo-autonomous manner to affect the growth rate and organogenesis. Finally, imprinted genes can signal the nutritional status between mother and fetus, and can modulate levels of maternal care. Importantly, many imprinted genes have been shown to affect postnatal growth and energy homeostasis. Given that abnormal birthweight correlates with adverse adult metabolic health, including obesity and cardiovascular disease, it is crucial to understand how the modulation of this dosage-sensitive, epigenetically regulated class of genes can contribute to fetal and postnatal growth, with implications for lifelong health and disease.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".