Heart‐on‐a‐Chip Platform for Assessing Toxicity of Air Pollution Related Nanoparticles
Bibliographic record
Abstract
Abstract Accumulating evidence indicates that air pollution contributes to serious and fatal damage to the cardiovascular system, yet the mechanisms that drive air pollution associated cardiovascular disease and dysfunction remain unclear. In an effort to create a more predictive in vitro model, a 3D platform, known as integrated vasculature for assessing dynamic events is used, that supports the combination of dense human induced pluripotent stem cell derived cardiac tissue and vascular interface, to unravel the impact of nanoscale air pollution on endothelial cells and cardiac tissue. Air pollution relevant nanoparticles (CuO, SiO2) and a control (Au) are used to predict the toxic effects on the cardiovascular system under perfusion. It is demonstrated that CuO nanoparticles are highly toxic, as they are able to translocate into the cardiac tissue and induce electrical and contractile dysfunction through generation of reactive oxygen species and subsequently lead to disruption of cardiac troponin T and secretion of biomarkers associated with cardiac injury (B‐type natriuretic peptide, N‐terminated pro‐hormone BNP, and Troponin I). SiO2, on the other hand, causes the secretion of pro‐inflammatory cytokines, and modulates the intracellular Ca2+ handling. This microengineering approach may offer new opportunities to more accurately model cardiovascular responses to nm‐sized air pollution.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".