Insight to Biofabrication of Liver Microtissues for Disease Modeling: Challenges and Opportunities
Bibliographic record
Abstract
: In the last decade, liver diseases with high mortality rates have become one of the most important health problems in the world. Organ transplantation is currently considered the most effective treatment for compensatory liver failure. An increasing number of patients and shortage of donors has led to the attention of reconstructive medicine methods researchers. The biggest challenge in the development of drugs effective in chronic liver disease is the lack of a suitable preclinical model that can mimic the microenvironment of liver problems. Organoid technology is a rapidly evolving field that enables researchers to reconstruct, evaluate, and manipulate intricate biological processes in vitro. These systems provide a biomimetic model for studying the intercellular interactions necessary for proper organ function and architecture in vivo. Liver organoids, formed by the self-assembly of hepatocytes, are microtissues and can exhibit specific liver characteristics for a long time in vitro. Hepatic organoids are identified as an impressive tool for evaluating potential cures and modeling liver diseases. Modeling various liver diseases, including tumors, fibrosis, non-alcoholic fatty liver, etc., allows the study of the effects of various drugs on these diseases in personalized medicine. Here, we summarize the literature relating to the hepatic stem cell microenvironment and the formation of liver Organoids
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".