Genomic instability and shear stress influence quantum dot-induced endothelial cell responses and gene expression
Bibliographic record
Abstract
Little is known about endothelial cell responses to nanoparticles under conditions simulating dysfunctional endothelium, a hallmark of vascular diseases, cancer, and aging. Endothelial genomic abnormalities and shear stress on the endothelial cells due to blood flow are key components of this microenvironment. Using organ-on-a-chip technologies and transcriptomics, we investigated the effects of genomic instability and shear stress on endothelial cell-level and RNA-level responses to model nanoparticles, CdSe/ZnS and InP/ZnS quantum dots (QDs). QDs were selected for their diagnostic potential, photostability enabling cellular tracking, and high uptake attributed to their ultrasmall size (13.9 and 3.9 nm). To model genomic instability, HUVEC cells were treated with monastrol (mt-HUVECs), and both control and mt-HUVEC models were exposed to 5 nM QD concentration. Transcriptomic analysis showed that Cdc20 gene was more downregulated in mt-HUVECs under dynamic flow (−5.68 vs dynamic HUVECs; −6.4 vs static mt-HUVECs), indicating a synergistic effect of flow and genomic instability on cell cycle suppression. Exposure to CdSe/ZnS QDs under dynamic conditions led to downregulation of the adherens junction pathway, which is consistent with the observed higher uptake and upregulation of heat shock and inflammatory response pathways. In contrast, InP/ZnS QDs upregulated tight junctions, explaining their lower uptake. Both QDs induced apoptotic pathway upregulation, with CdSe/ZnS QDs having more detrimental effects on viability. Combining genomic instability and shear stress resulted in different cell phenotypes that led to distinct cell responses and cell uptake of QDs. These findings guide future studies to better characterize endothelial responses to nanoparticles under biologically relevant conditions.
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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 | 0.000 | 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 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".