Key parameters for designing robust <scp>2D</scp> and <scp>3D</scp> spheroid models for <i>in vitro</i> atherosclerosis research
Bibliographic record
Abstract
Abstract Atherosclerosis is a chronic, systemic, inflammatory disease associated with the build‐up of fatty deposits (“plaques”) in the arteries. A major global health burden, severe atherosclerosis progresses to ischemic heart disease, an underlying condition which can exacerbate the occurrence of fatal events such as heart attack and stroke. Over the past two decades, the use of in vitro models to study atherosclerotic phenomena has increased, with the goal of complementing clinical research for drug and therapy development. In particular, 2D co‐culture models, and in the last decade, 3D spheroid models have been developed to improve our understanding of the atherosclerotic disease mechanism. However, the existing literature lacks information on the relevant parameters which should be considered prior and during the design of these models to promote model robustness and enhance their biomimetic capacities. This review provides an overview of such key parameters, as well as future perspectives on how existing limitations in the field of cell‐based in vitro model design can be improved. It is expected that by carefully considering these parameters, researchers will be better equipped with the required knowledge to develop biomedically and clinically relevant in vitro models.
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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 | 0.002 | 0.001 |
| 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 | 0.002 |
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".