IGF-Binding Protein mRNAs in the Human Fetus: Tissue and Cellular Distribution of Developmental Expression
Bibliographic record
Abstract
Insulin-like growth factors (IGF-I and IGF-II) are synthesized by most embryonic and fetal tissues, and regulate cellular growth and differentiation as autocrine/paracrine factors. A family of six IGF-binding proteins (IGFBPs) modulate IGF biological actions as both negative (inhibitory) and positive (potentiating) modulators. To determine the tissue distribution of IGFBP mRNA expression, we performed Northern blot analysis and in situ hybridization of human fetal tissues during gestational ages 10-16 weeks (n = 8). IGFBP-1 mRNA was expressed only in the liver, whereas other IGFBP mRNAs were expressed in variable abundance in every tissue examined. IGFBP-2 mRNA was expressed in moderate abundance in every tissue with the highest level observed in the liver; IGFBP-3 mRNA was expressed most abundantly in the skin, muscle and heart; IGFBP-4 mRNA was expressed in moderate abundance equally in all tissues; IGFBP-5 mRNA was expressed most abundantly in the skin, muscle and stomach, and IGFBP-6 mRNA was expressed in low abundance in all tissues. Notable exceptions were that liver expressed little or no IGFBP-4, -5 and -6 mRNAs, spleen and thymus expressed low levels of IGFBP-5 mRNA, and brain expressed little or no IGFBP-5 and IGFBP-6 mRNA. In situ hybridization of human fetal tissues showed IGFBP mRNAs were expressed in both epithelial and mesenchymal cells depending on the specific IGFBP and the stage of development. IGFBP-3, -4, and -5 mRNAs were localized mainly in the mesenchymal cells, and IGFBP-2 mRNA was localized predominantly in the epithelial cells. IGFBP-6 mRNA was localized in low abundance in both epithelial and mesenchymal cells. These studies indicate that IGFBPs are important paracrine modulators of IGF action on cellular growth and differentiation, by modulating IGF-dependent or IGF-independent actions.
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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 | 0.000 |
| 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 | 0.001 |
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".