Transgenic zebrafish model for quantification and visualization of tissue toxicity caused by alloying elements in newly developed biodegradable metal
Bibliographic record
Abstract
The cytotoxicity of alloying elements in newly developed biodegradable metals can be assessed through relatively low-cost and rapid in vitro studies using different cell types. However, such approaches have limitations; as such, additional investigations in small mammalian models are required that recapitulate the physiological environment. In this study, we established a zebrafish (Danio rerio) model for cytotoxicity evaluations that combines the physiological aspects of an animal model with the speed and simplicity of a cell-based assay. The model was used to assess the cytotoxicity of five common alloying elements in biodegradable implant materials. Conventional in vitro testing using heart, liver, and endothelial cell lines performed in parallel with zebrafish studies revealed statistically significant differences in toxicity (up to 100-fold), along with distinct changes in the morphology of the heart, liver, and blood vessels that were undetectable in cell cultures. These results indicate that our zebrafish model is a useful alternative to mammalian systems for accurately and rapidly evaluating the in vivo toxicity of newly developed metallic materials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".