The Effect of Chloroform Exposure on Mitochondrial DNA Copy Number
Bibliographic record
Abstract
Indigenous communities in Canada often struggle with access to clean drinking water. Chlorination is the primary water disinfection method in these communities, but due to high levels of organic carbon in the water pre-disinfection, the likelihood of chloroform formation, a trihalomethane (THM), is significantly increased. In 2019, Eabametoong and Attawapiskat First Nations in Northern Ontario declared a state of emergency due to high levels of THMs, mainly chloroform, in their drinking water. Health Canada supports that the benefits of disinfection through chlorination outweigh the risks of long-term, low-dose chloroform exposure. However, the field is lacking in research on the long-term effects on complex human health outcomes. This study aimed to assess the effects of low-dose chloroform treatment on a biomarker of health, mitochondrial DNA copy number (mtDNA-CN). mtDNA-CN variation is known to be associated with aging, frailty, and mortality. After 48h of low-dose chloroform treatment in HEK293 cells, no dose dependent mtDNA-CN changes were seen. HEK293 cells were then treated with a higher range of chloroform doses for 48hand yielded the same observation of no significant mtDNA-CN variation. Although there were no significant observations, this experiment tested only one biomarker of health in one cell line and was not conducted in a long-term setting, thus conclusions about the long-term health effects of low-dose chloroform exposure require further investigation.
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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 | 0.001 | 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) | 0.002 | 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".