Common and Uncommon Mouse Models of Growth Hormone Deficiency
Bibliographic record
Abstract
Mouse models of growth hormone deficiency (GHD) have provided important tools for uncovering the various actions of GH. Nearly 100 years of research using these mouse lines has greatly enhanced our knowledge of the GH/IGF-1 axis. Some of the shared phenotypes of the 5 "common" mouse models of GHD include reduced body size, delayed sexual maturation, decreased fertility, reduced muscle mass, increased adiposity, and enhanced insulin sensitivity. Since these common mouse lines outlive their normal-sized littermates-and have protection from age-associated disease-they have become important fixtures in the aging field. On the other hand, the 12 "uncommon" mouse models of GHD described herein have tremendously divergent health outcomes ranging from beneficial aging phenotypes (similar to those described for the common models) to extremely detrimental features (such as improper development of the central nervous system, numerous sensory organ defects, and embryonic lethality). Moreover, advancements in next-generation sequencing technologies have led to the identification of an expanding array of genes that are recognized as causative agents to numerous rare syndromes with concomitant GHD. Accordingly, this review provides researchers with a comprehensive up-to-date collection of the common and uncommon mouse models of GHD that have been used to study various aspects of physiology and metabolism associated with multiple forms of GHD. For each mouse line presented, the closest comparable human syndromes are discussed providing important parallels to the clinic.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".