Insulin-Like Growth Factor-I Receptor–Mediated Vasculogenesis/Angiogenesis in Human Lung Development
Bibliographic record
Abstract
The structural and functional development of the pulmonary system is dependent upon appropriate early vascularization of the embryonic lung. Our previous in vitro studies in a rat model indicated that insulin-like growth factor-I (IGF-I) is a potent angiogenic agent for fetal lung endothelial cells. To assess its role on human vascular lung development, we first examined the expression of IGF-I/II and IGF receptor type I (IGF-IR) in human embryonic and fetal lung tissues at 4-12 wk of gestation. Immunohistochemical and in situ hybridization studies revealed the presence of IGF-I/II-IGF-IR ligands and mRNA transcripts in embryonic lungs as early as 4 wk gestation. Immunotargeting using an anti-IGF-IR neutralizing antibody on human fetal lung explants demonstrated a significant blockade of IGF-IR signaling. Inactivation of IGF-IR resulted in a loss of endothelial cells, accompanied by dramatic changes in fetal lung explant morphology. Terminal transferase dUTP end-labeling assay and TEM studies of anti-IGF-IR-treated lungs demonstrated numerous apoptotic mesenchymal cells. Rat embryonic lung explant studies further validated the importance of the IGF-IGF-IR system for lung vascular development. These data provide the first demonstration of IGF-I/II expression in the human lung in early gestation and indicate that the IGF family of growth factors, acting through the IGF-IR, is required as a survival factor during normal human lung vascularization.
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".