High-content cell imaging for chemical toxicity screening in the model organism Caenorhabditis elegans
Bibliographic record
Abstract
The use of animal models for screening environmental chemicals for toxicity is an important step towards determining potential hazards to humans. Due to the large number of environmental chemicals with unknown biological activity, high-throughput screening has served as the primary method in toxicity testing for the past decades. However, with the emergence of diverse cellular targets that have been shown to be adversely affected by chemicals, a transition towards high-throughput screening that incorporates high-content analysis provides an array of cutting-edge experimental advantages. Here, we utilized the genetic model organism Caenorhabditis elegans to demonstrate how high-content screening can be utilized to identify new chemical modifiers of RNA splicing with the U.S. ToxCast chemical library. Through this semi-automated workflow, we highlight areas where modern high-content screening platforms provide advantages that improves on traditional methodology in high-throughput screening assays to maximize quantitative and qualitative data types collected.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".