Development of a bioprinting approach for automated manufacturing of multi-cell type biocomposite TRACER strips using contact capillary-wicking
Bibliographic record
Abstract
In many types of solid cancer, interactions between tumour cells and their surrounding microenvironment significantly impact disease progression, and patient prognosis. Tissue engineered models permit investigations of cellular behaviour and interactions in the context of this diseased microenvironment. Previously our group developed the tissue roll for analysis of cellular environment and response (TRACER) platform. To improve the manufacturing robustness of the TRACER platform and to enhance its use for studies involving multiple cell types, we have developed a bioprinting process that deposits cell-laden collagen hydrogel into a thin cellulose scaffolding sheet though a contact-wicking printing process. Printed scaffolds can then be assembled into layered 3D cultures where the location of cells in 3D is dependent on their printed position in the 2D sheet. After a desired culture time 3D TRACERs can be disassembled to easily assess printed cell re-location and phenotype within the heterogeneous microenvironments of the 3D tissue. In our bioprinting manufacturing process, cells are printed into scaffolding sheets, using a modified 3D bioprinter to extrude cells encapsulated in unmodified collagen hydrogel through a polydimethylsiloxane (PDMS) printer extrusion nozzle. This nozzle design reproducibly generated bioink deposition profiles in the scaffold without causing significant cellular damage or compromising scaffold integrity. We assessed print pattern quality and reproducibility and demonstrated printing of co-culture strips containing tumour cells and fibroblasts in separate compartments (i.e. separate TRACER layers). This printing approach will potentially enable adoption of the TRACER platform to the broader community to better understand multi-cell type interactions in 3D tumours and tissues.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".