Key parameters for designing robust <scp>2D</scp> and <scp>3D</scp> spheroid models for <i>in vitro</i> atherosclerosis research
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
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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