Photocrosslinkable Kidney Decellularized Extracellular Matrix‐Based Bioink for 3D Bioprinting
Bibliographic record
Abstract
Three-dimensional bioprinting has emerged as a promising strategy in tissue engineering, enabling the fabrication of biomimetic tissue constructs for regenerative medicine, disease modeling, and drug screening. A key challenge in this field is the development of organ-specific bioinks capable of recapitulating native microenvironments to support cell viability, proliferation, and tissue-specific maturation. In this study, a novel photocrosslinkable bioink derived from methacrylated decellularized porcine kidney extracellular matrix (KdMA) is reported. The decellularization process effectively removed cellular components while preserving key extracellular matrix constituents. The resulting KdMA bioink exhibited favorable rheological properties, including tunable stiffness and rapid photocuring kinetics, making it compatible with both digital light processing-based stereolithography and extrusion-based bioprinting platforms. Encapsulated human embryonic kidney cells maintained high viability and formed multicellular spheroids, demonstrating the bioink's cytocompatibility and structural support. Additionally, the KdMA bioink enabled stable multilayer bioprinting with preserved structural integrity and tunable mechanical properties. These results underscore the utility of KdMA as a kidney-specific bioink and its promise as a versatile platform for advancing renal tissue engineering and organoid maturation.
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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) | 0.001 | 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".