Development of a bioprinting approach for automated manufacturing of multi-cell type biocomposite TRACER strips using contact capillary-wicking
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
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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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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