Growing Pains: GH-induced Fibrosis Across Multiple Organs in bGH Mice
Bibliographic record
Abstract
Fibrosis, excessive extracellular matrix deposition, disrupts normal tissue function. It has been observed in select tissues of individuals with acromegaly and in transgenic mouse models of acromegaly, suggesting a role of GH and/or IGF-1. However, analysis across multiple tissues and ages has not been reported. This study evaluated fibrosis in 6 tissues -lung, kidney, liver, spleen, quadriceps, and heart-from young (3 months) and aged (12-15 months) bovine GH transgenic and wild-type mice of both sexes. Fibrosis was assessed using hydroxyproline content, picrosirius red (PSR) staining, and serum biomarkers of collagen turnover (PINP, ICTP, and FAP). Hydroxyproline assays showed collagen content significantly increased with age across all tissues and both sexes. Compared to wild-type, aged male bGH mice had elevated hydroxyproline in the lung, kidney, liver, and quadriceps; aged female bGH mice showed increases in kidney, liver, and quadriceps. PSR staining showed minimal differences in young mice. In aged bGH mice, males exhibited increased PSR staining in all tissues except lung; females showed increases in all tissues except lung and heart. Serum biomarkers showed sex- and age-specific patterns: PINP decreased with age in both sexes; ICTP increased with age in both sexes; FAP was lower in bGH mice and decreased with age in females. In conclusion, excess GH promotes fibrosis in most tissues studied and becomes more pronounced with advancing age, suggesting fibrosis is a common outcome of excess GH. Whether fibrosis is directly caused by GH/IGF-1 or secondary to poor health of bGH mice requires further investigation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".