Organ-on-a-chip platforms for evaluation of environmental nanoparticle toxicity
Bibliographic record
Abstract
Despite showing a great promise in the field of nanomedicine, nanoparticles have gained a significant attention from regulatory agencies regarding their possible adverse health effects upon environmental exposure. Whether those nanoparticles are generated through intentional or unintentional means, the constant exposure to nanomaterials can inevitably lead to unintended consequences based on epidemiological data, yet the current understanding of nanotoxicity is insufficient relative to the rate of their emission in the environment and the lack of predictive platforms that mimic the human physiology. This calls for a development of more physiologically relevant models, which permit the comprehensive and systematic examination of toxic properties of nanoparticles. With the advancement in microfabrication techniques, scientists have shifted their focus on the development of an engineered system that acts as an intermediate between a well-plate system and animal models, known as organ-on-a-chips. The ability of organ-on-a-chip models to recapitulate in vivo like microenvironment and responses offers a new avenue for nanotoxicological research. In this review, we aim to provide overview of assessing potential risks of nanoparticle exposure using organ-on-a-chip systems and their potential to delineate biological mechanisms of epidemiological findings.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".