Transgenic and Knockout Mouse Models Clarify Pituitary Development, Function and Disease
Bibliographic record
Abstract
Mouse models have been used to study various aspects of pituitary development, function and disease. Transgenic or knockout technology has been applied to examine the regulation of hormone gene expression and the pathophysiology of its alterations, to ascertain the factors that determine cell differentiation, and to manipulate oncogenesis. Transgenic mice have elucidated the necessary elements required for the tissue- and cell-specific expression of pituitary hormones. Transgenic and knockout technologies have derived mice with hormone overexpression or abrogation of hormone action, and have identified novel hormones. The role of precursor cells in cell differentiation has been confirmed by genetic ablation of cell lineages. Inactivation of transcription factors implicated in pituitary organogenesis and cytogenesis has proven their critical roles in pituitary development. Pituitary oncogenesis has been studied by promoter-directed oncogene expression or tumor suppressor gene ablation, by adenohypophysiotropic hormone overexpression, or by growth factor or receptor overexpression. The tumors have provided a number of cell lines for use in the continuing study of pituitary physiology and pathology. These models may also be used in the future to examine novel therapeutic strategies for the management of patients with pituitary disorders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".