3D bioprinting of Liver Microenvironment Model Using Photocrosslinkable Decellularized Extracellular Matrix based Hydrogel
Bibliographic record
Abstract
Abstract The liver, as one of the vital organs in the body, plays a crucial role in various bodily functions. Numerous factors can cause liver damage, that the sole remedy for severe liver conditions is the transplantation of healthy liver tissue. In response to the transplantation challenges, innovative approaches involving hydrogel-based technologies have emerged, leading to the creation of highly functionalized tissues. The development of three-dimensional printing and patterning of cell-laden biomaterial matrices offers promising advances for creating tissue-specific structures in tissue engineering and bioprinting. However, the matrix materials currently employed in bioprinting liver microtissue often fail to capture the complexity of the natural extracellular matrix (ECM), hindering their ability to restore innate cellular shapes and functions. Liver ECM-based hydrogels are increasingly recognized for their potential as biomimetic 3D cell culture systems that facilitate the exploration of liver disease, metabolism, and toxicity mechanisms. Yet, the conventional production of these hydrogels relies on slow thermal gelation processes, which restrict the manipulation of their mechanical characteristics. In this research, we introduce a novel approach with a functionalized photocrosslinkable liver decellularized extracellular matrix (dECM). By combining liver dECM methacrylate (LdECMMA) with gelatin methacrylate (GelMA), we achieved accelerated crosslinking under visible light irradiation and the ability to tune the mechanical, rheological, and physiological properties of the material. We encapsulated human hepatocellular carcinoma cells within an optimal concentration of the GelMA-LdECMMA hybrid hydrogel and examined cell proliferation and function over an extended period. The findings revealed that the GelMA-LdECMMA hybrid hydrogel enhances liver cell proliferation and function, holding significant promise for applications in drug screening and liver cancer metastasis research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".