HIGH-THROUGHPUT, OPTIMIZED PLATFORMS FOR 3D BIOPRINTING OF KIDNEY ORGANOIDS
Bibliographic record
Abstract
Three-dimensional bioprinting has emerged as a transformative technology in tissue engineering, offering promising solutions for organ regeneration, drug screening, and disease modeling. However, challenges such as labor-intensive procedures, limited scalability, and inconsistencies in bioprinted tissue quality remain significant barriers to widespread application. This thesis addresses these challenges through three interrelated studies focused on advancing bioprinting systems. The first study addressed the critical need for organ-specific bioinks tailored to support tissue-specific functions. A photocrosslinkable bioink derived from decellularized porcine kidney extracellular matrix was developed, preserving native biochemical components and demonstrating favorable rheological properties optimized for digital light processing-based stereolithography and piston drive extrusion based bioprinting. This bioink supported high cell viability, enhanced human embryonic kidney cell proliferation, and promoted tissue-specific maturation, establishing its suitability for engineering functional renal tissue constructs. The second study developed a machine learning-assisted, high-throughput bioprinting platform with optimized control of critical parameters, including bioink viscosity, nozzle size, printing time, printing pressure, and cell concentration. A custom-designed bioprinter capable of producing multiple cellular droplets simultaneously was built, generating extensive image datasets used to train predictive models. Among the machine learning algorithms evaluated, the multilayer perceptron model achieved the highest prediction accuracy for droplet volume, while the decision tree model exhibited the fastest computational performance. This integrated system significantly improved precision and efficiency, laying a foundation for scalable bioprint. Finally, the third study demonstrated the practical application of the automated kidney organoid production. A custom-modified, low-cost extrusion based three-dimensional bioprinter was utilized to fabricate kidney organoids from human-induced pluripotent stem cell-derived nephron progenitor cells. These bioprinted organoids expressed key renal markers, including podocytes, tubular structures, and vascular components. Quantitative analysis of nephron-like structures further underscored the system's scalability and efficiency. Notably, the capability to produce organoids using as few as 8,000 cells highlights the platform’s suitability for high-throughput bioprinting applications. Collectively, these studies present a comprehensive framework integrating customized bioprinting platforms and developed bioinks, addressing critical challenges in the field. By significantly enhancing efficiency, scalability, and precision in organoid production, these advancements provide solutions and pave the way for impactful applications in tissue engineering and precision medicine.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".