Assessing Porcine Liver-Derived Biomatrix for Hepatic Tissue Engineering
Bibliographic record
Abstract
Acellular, biologically derived matrices such as small intestinal submucosa have been extensively utilized to induce tissue regeneration and remodeling of connective tissue, vascular grafts, and urinary bladder; however, decellularized scaffolds have not been explored for their potential utility in hepatic tissue engineering. In the case of both extracorporeal hepatocyte-based devices and implantable hepatocyte-scaffold tissue-engineered constructs, maintenance of hepatocellular function is of prime importance. In this study, we specifically explored decellularized, porcine, liver-derived biomatrix (LBM) as a bioresorbable scaffold for primary hepatocytes. Primary rat hepatocytes were cultured on LBM and compared with well-characterized hepatocyte culture models--double-gel cultures that promote maintenance of liver-specific functions for many weeks, and adsorbed collagen monolayers that lead to the rapid decline of hepatocellular function and viability. Hepatocytes were maintained for up to 45 days on LBM and liver-specific functions such as albumin synthesis, urea production, and P-450 IA1 activity were found to be significantly improved over adsorbed collagen cultures. Our data indicate that LBM may be a favorable alternative to existing scaffolds for tissue engineering in that it is bioresorbable, can be easily manipulated, and supports long-term hepatocellular functions in vitro.
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".