Mouse models to study kidney development, function and disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: The mouse is the most widely used model organism to study gene function in the kidney in vivo. Here we review recent advances in technologies to manipulate the mouse genome and gene function to study renal biology. We discuss strengths and weaknesses of the approaches and provide examples in which they have been used to address renal questions. In addition, we provide a summary of the available resources of mouse tools and gene-targeting consortia. RECENT FINDINGS: Although conventional gene-targeting and spontaneous genetic mutations in mice have provided great insights into kidney function over several decades, the addition of powerful renal-specific gene-targeting tools and the advent of RNA technologies to manipulate gene function in vivo allow investigators to address research questions more precisely in the laboratory. Together with the establishment of multiple international consortia to target all the genes in the mouse genome and the development of gene trap and N-ethyl-N-nitrosourea resources, genetic manipulation in mice has become more efficient. SUMMARY: The availability of newer technologies and tremendous resources for mouse strains and reagents ensures that the mouse will remain a key model organism to study renal function.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.004 |
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".