Phylogeny can inform animal model development for both inherited and induced conditions: Duchenne Muscular Dystrophy (DMD) and Fetal Alcohol Spectrum Disorders (FASD)
Bibliographic record
Abstract
Abstract: The use of animal models in research on human and veterinary diseases and disorders is retracting, though it is likely to remain critical for decades. In light of increasing regulation and expectations of judicious use of animal subjects, we examine the idea that the use of animal models can be guided by phylogenetic relationships and modern evolutionary and cladistic analyses. Given that inherited disorders, and indeed, even the developmental and physiological responses to non-inherited conditions, are subject to evolutionary forces, it follows that the observed differences in model organisms are the products of evolutionary divergence. Understanding that divergence has the potential to elucidate which taxa are most likely to exhibit any given symptom or manifest a reaction in a broadly predictable fashion. We examine two case studies, one the inherited disorder Duchenne Muscular Dystrophy, and the other an entirely environmentally induced problem, Fetal Alcohol Spectrum Disorder, or Fetal Alcohol Syndrome. Both case studies reveal symptoms are largely congruent with phylogeny, suggesting relatively conservative evolution of developmental pathways. It follows that it is possible to characterize the manifestation of symptoms or dysmorphologies to broad phylogenetic groups. These data can then be used to inform research into possible treatments based on molecular genetic techniques sourced from unaffected taxa or even provide an evolutionary rationale for maximizing ethical decisions in the use and development of animal models in biomedical research. We argue that the technique should become standard practice in the development of animal models.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".