3D bioprinting of human iPSC-Derived kidney organoids using a low-cost, high-throughput customizable 3D bioprinting system
Bibliographic record
Abstract
The generation of kidney organoids derived from human induced pluripotent stem cells offers various applications such as tissue regeneration, drug screening, and disease modeling. The traditional methodology for generating organoids presents challenges, including labor-intensive procedures, limited scalability, and batch-to-batch variability in organoid quality. To address these obstacles, we have developed a low-cost and readily accessible automated three-dimensional bioprinting platform capable of printing nephron progenitor cells derived from induced pluripotent stem cells to form kidney organoids. Bioprinted organoids expressed markers for major cell types of the kidney including podocytes, proximal tubules, distal tubules, and endothelial cells. Quantification of nephron-like structures in varying sizes of the organoids was also conducted. This study demonstrates the ability to efficiently generate kidney organoids with as few as 8000 cells. Our low-cost, high-throughput bioprinter holds the potential for fabricating various other organoids and tissue.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".