Animal models of amyotrophic lateral sclerosis: a comparison of model validity
Bibliographic record
Abstract
Animal models are necessary to investigate the pathogenic features underlying motor neuron degeneration and for therapeutic development in amyotrophic lateral sclerosis (ALS). Measures of model validity allow for a critical interpretation of results from each model and caution from over-interpretation of experimental models. Face and construct validity refer to the similarity in phenotype and the proposed causal factor to the human disease, respectively. More recently developed models are restricted by limited phenotype characterization, yet new models hold promise for novel disease insights, thus highlighting their importance. In this article, we evaluate the features of face and construct validity of our new zebrafish model of environmentally-induced motor neuron degeneration and discuss this in the context of current environmental and genetic ALS models, including C9orf72, mutant Cu/Zn superoxide dismutase 1 and TAR DNA-binding protein 43 mouse and zebrafish models. In this mini-review, we discuss the pros and cons to validity criteria in each model. Our zebrafish model of environmentally-induced motor neuron degeneration displays convincing features of face validity with many hallmarks of ALS-like features, and weakness in construct validity. However, the value of this model may lie in its potential to be more representative of the pathogenic features underlying sporadic ALS cases, where environmental factors may be more likely to be involved in disease etiology than single dominant gene mutations. It may be necessary to compare findings between different strains and species modeling specific genes or environmental factors to confirm findings from ALS animal models and tease out arbitrary strain- and overexpression-specific effects.
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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 | 0.033 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".