Transgenic and physiological mouse models give insights into different aspects of amyotrophic lateral sclerosis
Bibliographic record
Abstract
A wide range of genetic mouse models is available to help researchers dissect human disease mechanisms. Each type of model has its own distinctive characteristics arising from the nature of the introduced mutation, as well as from the specific changes to the gene of interest. Here, we review the current range of mouse models with mutations in genes causative for the human neurodegenerative disease amyotrophic lateral sclerosis. We focus on the two main types of available mutants: transgenic mice and those that express mutant genes at physiological levels from gene targeting or from chemical mutagenesis. We compare the phenotypes for genes in which the two classes of model exist, to illustrate what they can teach us about different aspects of the disease, noting that informative models may not necessarily mimic the full trajectory of the human condition. Transgenic models can greatly overexpress mutant or wild-type proteins, giving us insight into protein deposition mechanisms, whereas models expressing mutant genes at physiological levels may develop slowly progressing phenotypes but illustrate early-stage disease processes. Although no mouse models fully recapitulate the human condition, almost all help researchers to understand normal and abnormal biological processes, providing that the individual characteristics of each model type, and how these may affect the interpretation of the data generated from each model, are considered and appreciated.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".