Optimization of a rapid, sensitive, and high throughput molecular sensor to measure canola protoplast respiratory metabolism as a means of screening nanomaterial cytotoxicity
Bibliographic record
Abstract
Nanomaterial-mediated plant genetic engineering holds promise for developing new crop cultivars but can be hindered by nanomaterial toxicity to protoplasts. We present a fast, high-throughput method for assessing protoplast viability using resazurin, a non-toxic dye converted to highly fluorescent resorufin during respiration. Protoplasts isolated from hypocotyl canola (Brassica napus L.) were evaluated at varying temperatures (4, 10, 20, 30 ˚C) and time intervals (1-24 h). Optimal conditions for detecting protoplast viability were identified as 20,000 cells incubated with 40 µM resazurin at room temperature for 3 h. The assay was applied to evaluate the cytotoxicity of silver nanospheres, silica nanospheres, cholesteryl-butyrate nanoemulsion, and lipid nanoparticles. The cholesteryl-butyrate nanoemulsion and lipid nanoparticles exhibited toxicity across all tested concentrations (5-500 ng/ml), except at 5 ng/ml. Silver nanospheres were toxic across all tested concentrations (5-500 ng/ml) and sizes (20-100 nm), except for the larger size (100 nm) at 5 ng/ml. Silica nanospheres showed no toxicity at 5 ng/ml across all tested sizes (12-230 nm). Our results highlight that nanoparticle size and concentration significantly impact protoplast toxicity. Overall, the results showed that the resazurin assay is a precise, rapid, and scalable tool for screening nanomaterial cytotoxicity, enabling more accurate evaluations before using nanomaterials in genetic engineering.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".