A genetically clamped renin transgene for the induction of hypertension
Bibliographic record
Abstract
Experimental analysis of the effects of individual components of complex mammalian systems is frequently impeded by compensatory adjustments that animals make to achieve homeostasis. We here introduce a genetic procedure for eliminating this type of impediment, by using as an example the development and testing of a transgene for "genetically clamping" the expression of renin, the major homeostatically responding component of the renin-angiotensin system, one of the most important regulators of blood pressure. To obtain a renin transgene whose expression is genetically clamped at a constant level, we have used single-copy chosen-site gene targeting to insert into a liver-specific locus a single copy of a modified mouse renin transgene driven by a liver-specific promoter/enhancer. The resulting transgene expresses renin ectopically at a constant high level in the liver and leads to elevated plasma levels of prorenin and active renin. The transgenic mice display high blood pressure, enhanced thirst, high urine output, proteinuria, and kidney damage. Treatment with the angiotensin II type I receptor antagonist, losartan, reduces the hypertension, albuminuria, and kidney damage, but does not affect expression of the transgene. This genetically clamped renin transgene can be used in models in which hypertension and its complications need to be investigated in a high prorenin/renin environment that is not subject to homeostatic compensations by the animal when other factors are changed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".