Vascularized Liver Tissue Embedded Bioprinting Utilizing GelMA/Nanoclay-based Composite hydrogels
Bibliographic record
Abstract
Abstract As the aging population grows, the need to regenerate non-self-repairing tissues becomes increasingly crucial for enhancing our quality of life. Tissue engineering offers a promising solution, particularly in recreating the intricate networks of blood vessels crucial for tissue vitality. These tissues rely on effective nutrient and oxygen circulation, with an optimal oxygen diffusion range of 100–200 µm. Yet, crafting vascularized in vitro tissues remains a significant challenge. This study addresses the challenge by using GelMA-based hydrogels as a photocrosslinkable support bath, a biocompatible and versatile choice for biological applications. To enhance the rheological properties for in vitro tissue engineering, Laponite (LPN) is introduced as a rheology modifier. The study optimizes the GelMA-LPN nanocomposite hydrogel composition, ensuring the desired physical, mechanical, and rheological properties, including recovery. The research also explores the biological implications, encapsulating liver cells within the nanocomposite hydrogel, and studying their behavior under perfusion conditions. This research presents a promising avenue for creating vascularized in vitro tissues, potentially advancing tissue engineering and regenerative 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 | 0.000 |
| 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".