Self-assembled human osseous cell sheets as living biopapers for the laser-assisted bioprinting of human endothelial cells
Bibliographic record
Abstract
A major challenge during the engineering of voluminous bone tissues is to maintain cell viability in the central regions of the construct. In vitro prevascularization of bone substitutes relying on endothelial cell bioprinting has the potential to resolve this issue and to replicate the native bone microvasculature. Laser-assisted bioprinting (LAB) commonly uses biological layers of hydrogel, called 'biopapers', to support patterns of printed cells and constitute the basic units of the construct. The self-assembly approach of tissue engineering allows the production of biomimetic cell-derived bone extracellular matrix including living cells. We hypothesized that self-assembled osseous sheets can serve as living biopapers to support the LAB of human endothelial cells and thus guide tubule-like structure formation. Human umbilical vein endothelial cells were bioprinted on the surface of the biopapers following a predefined pattern of lines. The osseous biopapers showed relevant matrix mineralization and pro-angiogenic hallmarks. Our results revealed that formation of tubule-like structures was favored when the cellular orientation within the biopaper was parallel to the printed lines. Altogether, we validated that human osseous cell sheets can be used as biopapers for LAB, allowing the production of human prevascularized cell-based osseous constructs that can be relevant for autologous bone repair applications.
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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 | 0.000 |
| 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 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".