Hyperargininemia in an inducible arginase‐1 deficient mouse model
Bibliographic record
Abstract
We generated an inducible arginase‐1 (Arg1) deficient mouse model by crossing “floxed” Arg1 mice with the Cre‐ER‐T2 model (Arg1‐Cre). These Arg1‐Cre mice die about 2 weeks after tamoxifen administration regardless of the starting age of inducing the knockout and show signs of hyperammonemia and significantly elevated arginine levels (n=4–5; four time points from 2 weeks to 14 weeks of age). Plasma creatine and creatinine levels are normal but guanidinoacetate is increased as assessed by tandem mass spectrometry. Attempts to rescue the lethality or at least to mitigate the biochemical defects caused by loss of arginase‐1 by supplementation of ornithine in the drinking water (n=5) were unsuccessful. An arginase‐1/green fluorescent protein retains full catalytic activity and is being introduced into liver hepatocytes and induced pluripotent stem cells obtained from Arg1‐Cre mice in vitro and to animals in vivo to restore normal biochemistry and to rescue the severe phenotypic consequences of deficiency of this enzyme. Arg1‐Cre mice should prove useful to explore the biochemical defects associated with arginase‐1 deficiency and to explore genetic correction strategies.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".