Influence of 3D design in microscaffolds mechanical properties.
Bibliographic record
Abstract
Tissue engineering is a growing field and one of its objectives is tissue regeneration. Within this field there are two strategies, scaffold-based and scaffold-free. The research group led by Ovsianikov et al.is committed to a third strategy that aims to merge together the scaffold-free and scaffold-based strategies. This requires to be able to encage spheroids within single micro-size scaffolds. They have recently shown the possibility to produce in high throughput manner multiple spheroid-loaded microscaffolds called buckyballs (BB).Those BB of 300 um in diameter are produced using photosensitive polycaprolactonebased resin with a multiphoton polymerization 3D printing technique. The design of the BB influences directly the mechanical properties of the spheroid encapsulated inside, which consequently, impacts the spheroid behaviour. He main objective of this project is to carry out a mechanical study of these structures in order to study their mechanical properties. Firstly, a microtester (Microsquisher form CellScale, Canada) was used to carry out compression tests to obtain the bulk properties of the material used to print the BBs, UPCL-6. Once the properties of the material had been obtained, an experimental study was carried out by calculating the stresses and deformations sufferedby the BBs when a certain deformation was imposed, using the microtester. At the same time, a computational study has been carried out using the commercial finite element software ABAQUS. Different compression tests have been simulated in ABAQUS, in which different geometric parameters have been changed to see how they affect the mechanical properties of the BB.As cells sense their environment in terms of mechanical stimuli, it is important to be able to control the mechanical properties of cellular spheroids. This could then enable to control their behaviour, for instance in terms of differentiation. This work will permit to decipher to which degree we can control the mechanical properties of the BB encaging spheroids, which will then open further opportunity in understanding the cell mechnotransduction.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".