Experimental IGF-I Receptor Deficiency Generates a Sexually Dimorphic Pattern of Organ-Specific Growth Deficits in Mice, Affecting Fat Tissue in Particular
Bibliographic record
Abstract
Reduced IGF type I receptor levels diminish postnatal growth rate and adult body weight in mice. Here, we studied the impact of experimental IGF receptor deficiency on tissue-specific growth by Cre-lox-mediated dosage of a floxed IGF-IR gene. We generated mice with a wide spectrum of receptor deficiency (5-82%), and separated them into two groups with either strong (> or =50%) IGF-IR deficiency (XS mice) or moderate deficiency (<50%, M mice). The growth of XS mice was significantly retarded from 3 wk after birth onward, with respect to M littermates. This effect was twice as strong in males as in females. Growth deficits persisted throughout adult life, and at 10-12 months, most organs and tissues showed specific weight defects. Skin, bone and connective tissue, muscle, spleen, heart, lung, and brain were the most severely affected organs in the XS males. With the exception of muscle and spleen, the same tissues were also significantly reduced in size in females, although to a lesser extent. The most severe growth defect, however, concerned adipose tissue. Fat pad size in XS males was only 29% (females, 44%) of M mice. The estimated number of adipocytes in XS male fat pads was only 21% that of M males (XS female, 27%). Lipid content per cell was significantly higher in XS adipocytes, whereas plasma glucose and insulin levels were low in XS males. Thus, IGF type I receptor deficiency produced mice with disproportionate postnatal organ growth, and these effects depended strongly on sex. A marked reduction in IGF-IR levels resulted in a major defect in adipose tissue.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".