Organ-on-a-chip platforms for evaluation of environmental nanoparticle toxicity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it