VAT photopolymerization of native extracellular matrix and patient-derived ipsc recellularization for personalized 4d bioprinting
Bibliographic record
Abstract
Organ transplantation is the last resort for patients suffering end-stage organ failure [1,2]. Acknowledged as the most effective treatment concerning life expectancy and quality of life, this life-saving medical intervention grapples with persisting organ scarcity [1–5]. According to the Canadian Organ Replacement Register’s 2023 annual summary statistics, the transplant waitlist mortality rate was 31%, leaving 3,427 individuals still awaiting organ transplantation in Canada [3]. Furthermore, the ongoing challenges of organ compatibility persist; lifelong close monitoring and immunosuppression are standard for recipients [4]. In light of these challenges, researchers are exploring the application of the extracellular matrix to help relieve transplantation demands, through organ decellularization, followed by recellularization in succession [5]. This viewpoint proposes the replication of native extracellular matrices through vat photopolymerization as a feasible and novel avenue to producing personalized 4D bioprinted organs, for addressing current issues regarding transplantation.
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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 | 0.001 |
| 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".