ExCeL: combining extrusion printing on cellulose scaffolds with lamination to create<i>in vitro</i>biological models
Bibliographic record
Abstract
Bioprinting is rapidly developing into a powerful tool in tissue engineering, for both organ printing and the development of in vitro models that can be used in drug discovery, toxicology and in vitro bioreactors. Nevertheless, the ability to create complex 3D culture systems with different types of cells and extracellular matrices integrated with perfusable channels has been a challenge. Here we develop an approach that combines the xurography of a scaffold material (cellulose) with extrusion printing of bioinks onto it, followed by assembly in a layer-by-layer fashion to create complex 3D culture systems that could be used as in vitro models of biological processes. This new method, termed ExCeL, can recapitulate the complexities of natural tissues with a proper 3D distribution of cells, extracellular matrices, and different molecules, while providing the whole structure with mechanical stability, and direct and easy access to the cells, even the ones that are positioned deep in the bulk of the structure, without the need to fix or section the samples. Briefly, the bioprinting of predefined patterns with a feature size of ∼1 mm has been made possible by treating paper with the hydrogel's crosslinker and printing cell-embedded hydrogel that will solidify immediately upon contact with the paper. These impregnated layers can be used as single layers or in a layer-by-layer approach by stacking them (here up to four layers) for applications such as cell migration and proliferation in 3D structures composed of collagen or alginate. Cells are generally sensitive to the amount of FBS in their culture media and we have shown how FBS amount will effect cell migration. By cutting the paper in certain patterns, printing hydrogel on the remaining parts of it, and stacking the paper in layers, features like embedded channels are formed that will provide cells will better mass transfer in thick structures. This technique provides biologists with a unique tool to perform sophisticated in vitro assays.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".