Generation of an inducible arginase‐1 deficient mouse model
Bibliographic record
Abstract
Arginase‐1 (Arg1) catalyzes the final step in the urea cycle to convert arginine to ornithine + urea. In the rarest of the urea cycle disorders, genetic deficiency of Arg1 results in hyperargininemia with occasional bouts of hyperammonemia, eventually leading to neurological problems and failure to thrive. Arg1 deficient mice die at two weeks of age. In order to circumvent the early mortality we generated an inducible knockout model by crossing “floxed” Arg1 deficient mice (JAX strain C57BL/6‐Arg1tm1Pmu/J) with Cre‐ER‐T2 mice (JAX strain B6.129‐Gt(ROSA)26Sortm1(cre/ERT2)Tyj/J). Arg1‐Cre mice were indistinguishable from control mice. However, when treated with 4‐OH‐tamoxifen for 2 days at birth by intragastric administration, or for 5 consecutive days with i.p. tamoxifen at 4, 8, or 12 weeks (n=4–8) all mice died within 12–14 days or reached humane endpoints. DNA analysis confirmed the deletion of exons 7 and 8 and Western blot analysis showed loss of Arg1 expression in mouse embryo fibroblasts and liver hepatocytes derived from tamoxifen‐treated Arg1‐Cre mice. Hyperammonemia was evident in the crisis period 7–12 days after tamoxifen administration prior to gait and imbalance problems that presented clinically at the end stage. These mice should be a useful model to study the mechanisms of neurological deficits resulting from Arg1 deficiency in aged mice provided that the hyperammonemia can be controlled.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".