3D Bioprinting of Kidney Tissue Using a Photocrosslinkable Hydrogel Derived from Decellularized Extracellular Matrix
Bibliographic record
Abstract
Abstract Three-dimensional bioprinting has emerged as a promising strategy in tissue engineering, aiming to fabricate functional tissue constructs for organ regeneration. A critical challenge in this field is the development of organ-specific bioinks that can provide a microenvironment conducive to cellular growth and differentiation. In this study, we successfully developed a photocrosslinkable bioink by methacrylating decellularized porcine kidney extracellular matrix. The decellularization process effectively removed all cellular components while preserving the native kidney extracellular matrix composition. The resulting methacrylated decellularized extracellular matrix bioink exhibited optimal rheological properties, making it well-suited for digital light processing based stereolithography and piston-driven extrusion bioprinting. Human embryonic kidney cells encapsulated in the bioink showed high viability and a strong proliferative capacity, indicating potential for tissue-specific maturation over time. This work demonstrates the feasibility of utilizing kidney-specific decellularized extracellular matrix-based bioinks, providing a platform for engineering renal tissue constructs for therapeutic 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".