Cytotoxic and molecular effects of soil extracts from the Agbogbloshie electronic-waste site on fish and human cell lines
Bibliographic record
Abstract
Effect-based methods (EBM) are of growing interest in environmental monitoring programs. Few EBM have incorporated transcriptomics even though these provide a wealth of biological information and can be modeled to yield transcriptomic points of departure (tPODs). The study objectives were to: A) characterize cytotoxic effects of soil extracts on the rainbow trout RTgill-W1 and the human Caco-2 cell lines; B) measure gene expression changes and calculate tPODs; and C) compare in vitro responses to available measures of plastic-related compounds and metals. Extracts were prepared from 35 soil samples collected at the Agbogbloshie E-waste site (Accra, Ghana). Cells were exposed to six soil concentrations (0.3 to 9.4 mg dry weight of extract (eQsed)/ml). Many samples caused cytotoxicity with RTgill cells being more sensitive than Caco-2 cells. Eleven samples were analyzed for transcriptomics in both cell lines, with responses measured in all samples (52 to 5925 differentially expressed genes) even in the absence of cytotoxicity. In RTgill cells there was concordance between cytotoxic measures in tPOD values (spearman = 0.85). Though trends between in vitro measures and contaminant data were observed, more work is needed in this area before definitive conclusions are drawn. Nonetheless, this study helps support ongoing efforts in establishing alternative testing strategies (e.g., alternative to animal methods; toxicogenomics) for the assessment of complex environmental samples.
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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".