A 3D Self‐Assembled In Vitro Model to Simulate Direct and Indirect Interactions between Adipocytes and Skeletal Muscle Cells
Bibliographic record
Abstract
The molecular mechanisms of the development and progression of diabetes and obesity involve complex interactions between adipocytes and skeletal muscle cells. Although 2D in-vitro models are the gold standard for the mechanistic study of such behaviors, they do not recreate the complexity and dynamics of the interactions between the cell types involved. Alternatively, animal models are used but are expensive, difficult to visualize or analyze, are not completely representative of human physiology or genetic background, and have associated ethical considerations. 3D co-culture systems can be complementary to these approaches. Here, using a newly developed 3D biofabrication method, adipocytes and myoblasts are positioned precisely either in direct physical contact or in close proximity such that the paracrine effects could be systematically studied. Suitable protocols for growth and differentiation of both cells in the co-culture system is also developed. Cells show more restrained lipid and protein production in 3D systems compared to 2D ones and adipocytes show more lipolysis in indirect contact with myoblasts as response to drug treatment. These findings emphasize importance of physical contact between cells that have been overlooked in co-culture systems using transwell inserts and can be used in studies for the development of anti-obesity drugs.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".