Germ cell mutagens: Risk assessment challenges in the 21st century
Bibliographic record
Abstract
Heritable mutations may result in a wide variety of detrimental outcomes, from embryonic lethality to genetic disease in the offspring. Despite this, today's commonly used test batteries do not include assays for germ cell mutation. Current challenges include a lack of practical assays and concrete evidence for human germline mutagens, and large data gaps that often impede risk assessment. Moreover, most regulatory assessments are based on the assumption that somatic cell mutation assays also protect the germline by default, which has not been adequately confirmed. The field is also faced with new challenges aimed at dramatically reducing animal testing, and attempts to rapidly classify thousands of chemicals using high throughput in vitro assays. These approaches may not adequately capture effects that may be particular to gametes, since many aspects of the germline are unique. In light of these challenges, an urgent need exists to develop new approaches to evaluate the potential of toxicants to cause germline mutation. The application of new technologies will greatly enhance our understanding of mutation in humans exposed to environmental mutagens. However, we must be poised to collect and interpret these data, and facilitate risk translation to regulators and the public. Genetic toxicologists must also become actively involved in the development of high-throughput tools to study germline mutation. Appropriate attention to these areas will result in the development of policies that prioritize the protection of the germline and future generations from DNA sequence mutations.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| 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".