Telomere Length and Arsenic: Improving Animal Models of Toxicity by Choosing Mice With Shorter Telomeres
Bibliographic record
Abstract
Arsenic is both a chemotherapeutic drug and an environmental toxicant that affects hundreds of millions of people each year. Arsenic exposure in drinking water has been called the worst poisoning in human history. How arsenic is handled in the body is frequently studied using rodent models to investigate how arsenic both causes and treats disease. These models, used in a variety of arsenic-related testing, from tumor formation to drug toxicity monitoring, have virtually always been developed from animals with telomeres that are unnaturally long, likely because of accidental artificial selective pressures. Mice that have been bred in captivity in laboratory conditions, often for over 100 years, are the standard in creating animal models for this research. Using these mice introduces challenges to any work that can be affected by the length of telomeres and the related capacities for tissue repair and cancer resistance. However, arsenic research is particularly susceptible to the misuse of such animal models due to the multiple and various interactions between arsenic and telomeres. Researchers in the field commonly find mouse models and humans behaving very differently upon exposure to acute and chronic arsenic, including drug therapies which seem safe in mice but are toxic in humans. Here, some complexities and apparent contradictions of the arsenic carcinogenicity and toxicity research are reconciled by an explanatory model that involves telomere length explained by the evolutionary pressures in laboratory mice. A low-risk hypothesis is proposed which has the power to determine whether researchers can easily develop more powerful and accurate mouse models by simply avoiding mouse lineages that are very old and have strangely long telomeres. Swapping in newer mouse lineages for the older, long-telomere mice may vastly improve our ability to research arsenic toxicity with virtually no increase in cost or difficulty of research.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".