The Ontogeny of Growth Hormone, Insulin-Like Growth Factors and Sex Steroids: Molecular Aspects
Bibliographic record
Abstract
Insulin-like growth factors (IGF-1 and IGF-2) are synthesized by many tissues in response to GH treatment and regulate cellular growth and differentiation. Fetal serum contains abundant GH, and many fetal tissues express GH receptors, but the clinical significance of GH in fetal development in humans is uncertain because hypopituitary newborns have normal birth size. The biological actions of IGFs are modulated by a family of binding proteins (IGFBPs). The demonstration of IGF and IGFBP transcripts in preimplantation embryos indicates that the influence of IGFs and IGFBPs in fetal development begins even prior to implantation. IGF and IGFBP mRNAs, except IGFBP-1 mRNA, are expressed at variable levels in many fetal tissues throughout gestation. Although the IGF mRNAs are widely expressed, IGFBP mRNAs manifest in specific cell types in a spatially and temporally specific manner, suggesting that they indicate sites of IGF action. Conditions of undernutrition and chronic hypoxemia, known to cause intrauterine growth retardation in fetuses, alter IGFBP and IGF-1 but not IGF-2 gene expression, thus indicating the role for IGF-1 and IGFBPs as mediators of altered growth. IGF and IGFBP genes are also expressed in many fetal endocrine tissues including those secreting sex steroids. Null mutation of the IGF-1 gene leads to retarded development of the primary sex organs. In the fetal adrenal gland, IGF-2 mRNA is localized to 3 beta-hydroxysteroid hydrogenase (3 beta-HSD) immunoreactive cells, suggesting a close relationship to steroid hormone biosynthesis. IGFBPs are important paracrine modulators of IGF action during development, and are crucial regulators of cellular growth and differentiation by modulating IGF-dependent or -independent actions in all tissues including developing endocrine glands.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".