Expressão da progranulina durante os primeiros estágios de desenvolvimento hepático em ratos Fischer 344
Bibliographic record
Abstract
Transplants are the only effective therapy for the treatment of advanced liver diseases such as cirrhosis. Given the limited number of organ donors, regenerative medicine has sought for sources of cells and tissues for replacement therapy. Embryonic stem cells are a promising source of material for transplantation because of their exclusive property of being expanded indefinitely in culture, thus, they are a source of replacement tissue. Moreover, they are capable of differentiating into practically all cell types, and may be utilized in replacement therapy in various diseases. The liver bud has bipotent stem cells that have not yet differentiated into hepatocytes or biliary duct cells; however, they have great potential of proliferation and differentiation. Thus, the challenge is to identify methods that promote their differentiation in specific and functional strains. This study aimed to evaluate the role of the progranulin growth factor PGRN during the liver development of rats F344, since this growth factor could be utilized in protocols of differentiation of stem cells of the liver bud in functional hepatocytes. The results showed that PGRN is present during different periods of hepatogenesis in F344 rats, and that this growth factor should be involved in the process of differentiation of hepatoblasts into hepatocytes after activation by HNF4α , however, PGRN seems not to exert a cellular proliferation function during the hepatogenesis. Thus, PGRN can be used in future protocols of liver cell differentiation directed toward cellular therapy in Regenerative Medicine.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".