Human induced pluripotent stem cell-derived vessels as dynamic atherosclerosis model on a chip
Bibliographic record
Abstract
Abstract Atherosclerosis is an arterial disease characterized by intravascular plaques. Disease hallmarks are vessel stenosis and hyperplasia, eventually escalating into plaque rupture and acute clinical presentations. Innate immune cells and local variations in hemodynamics are core players in the pathology, but their mutual relationship has never been investigated before due to the lack of modeling systems with adequate degree of complexity. Here, we combined computational fluid dynamics and tissue-engineering to achieve, for the first time in vitro , full atherosclerotic plaque development. Our model incorporates induced pluripotent stem cell-derived populations into small-caliber arteries that are cultured in atheroprone conditions. Using machine-learning-aided immunophenotyping, molecular and nanoprobe-based tensile analyses, we found that immune cells, extracellular matrix components and tensional state were comparable between in vitro and ex vivo human lesions. Our results provide further insights into the relation between hemodynamics and inflammation, introducing a versatile, scalable modeling tool to study atherosclerosis onset and progression.
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.
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".