Highly efficient transgenic mouse production using piggyBac and its application to rapid phenotyping at the founder generation
Bibliographic record
Abstract
Abstract Pronuclear microinjection is the most popular method for producing transgenic (Tg) animals. Because the production efficiency is typically less than 20%, phenotypic characterization of Tg animals is generally performed on the next generation (F 1 ) onwards. However, apart from in rodents, in many animal species with long generation times, it is desirable to perform phenotyping in the founder (F0) generation. In this study, we attempted to optimize a method of Tg mouse production to achieve higher Tg production efficiency using piggyBac transposon systems and established optimal conditions under which almost all individuals in the F0 generation were Tg. We also succeeded in generating bacterial artificial chromosome Tg mice with efficiency of approximately 70%. By combining this method with genome editing technology, we established a new strategy to perform phenotyping of mice with tissue-specific knockout using the F0 generation. Taking the obtained findings together, by using this method, experimental research using Tg animals can be carried out more efficiently.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".