Impacts of Inorganic Arsenic Exposure on Genetic Stability of Human Mesenchymal Stromal Cells
Bibliographic record
Abstract
Human mesenchymal stem/stromal cells (hMSCs) can differentiate into mesoderm-type cells, making them suitable candidates for tissue repair therapies. However, their relatively low frequency in adult tissue necessitates ex vivo expansion prior to regenerative medicine applications, and therefore, long-term hMSC genetic stability during expansion should be studied. hMSC applications in regenerative medicine ensure commercial availability of normal karyotype human primary cells for toxicity assessment and hMSCs could serve as alternatives to immortalized human cell models. In this work, we evaluated the potential of hMSCs in toxicity testing using inorganic arsenic (iAs) as a case study. hMSCs were exposed to iAs at different durations to track cellular aging and study long-term genetic stability. iAs exposures (48 h) resulted in micronuclei induction. hMSCs were also exposed to iAs for 6 days to determine if hMSCs would become more susceptible to chromosomal damage following exposure to the model genotoxicant, mitomycin C (MMC). The culture duration and iAs exposure did not alter MMC potency, indicating that the hMSC susceptibility to chromosomal damage remained unchanged. We also used gene expression analysis to investigate the molecular impacts of iAs on hMSCs over the course of short (3 days total) and long (30 days total) experiments. Both iAs exposures activated biomarkers associated with oxidative stress, but not biomarkers for direct DNA damage, providing support for an indirect mode of action for iAs genotoxicity. Overall, this study establishes the utility of hMSCs as a new model for toxicity screening and provides mechanistic information underlying iAs toxicity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".