Minireview: Insights from Insulin-Like Growth Factor Binding Protein Transgenic Mice
Bibliographic record
Abstract
The existence of abundant high affinity binding proteins for the IGFs, the IGF binding proteins (IGFBPs), was first demonstrated more than 40 yr ago in the very early days of somatomedin research. With the development of molecular techniques and transgenic and knockout mouse models, the nature, complexity, and redundancy of the IGFBPs have now started to be elucidated. Indeed the functional role of the circulating IGFs and the originally proposed endocrine somatomedin hypothesis have recently been questioned. The limited reports to date indicate that IGFBP knockout mice have few phenotypic manifestations. In contrast, overexpression of IGFBPs in transgenic mice is associated with manifestations that provide some insight into the physiological role of the binding proteins. The predominant effect of generalized or tissue-specific overexpression of the IGFBPs has been growth inhibition as would be anticipated from inhibition of the actions of IGF-I and -II. In addition, impaired glucose homeostasis and reduced fecundity have been observed in both IGFBP-1- and IGFBP-3-overexpressing transgenic mice. This review examines the data reported to date for transgenic mouse models that overexpress IGFBPs. In addition, data from transgenic mice that overexpress the acid-labile subunit, an important component of the ternary complex, have also been reviewed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".