Comparative analysis of delivery methods for stem cell therapy in liver diseases
Bibliographic record
Abstract
Background: Mesenchymal stem cell transplantation is an emerging therapy for treating acute and chronic liver diseases as an alternative for patients who are not transplant candidates. The potential of this treatment depends on its therapeutic efficiency and safety, which have been investigated and evaluated in both preclinical and clinical settings. However, there are still some risks associated with the delivery methods, such as low long-term retention rate and the possibility of arousing cancer or rejection. Methods: During the review process, previous papers mentioning “stem cell treatment” and “liver diseases” were searched. We described different up-to-date approaches for stem cells from various origins to be delivered to the liver and compared respectively their pros and cons for clinical applications. We also proposed several potential techniques for future studies. Summary: An efficient and safe stem cell delivery could be enabled via Alginate-Polylysine-Alginate (APA) microencapsulation, lipid-conjugated coating or the use of nanoparticles. Their efficacy will be improved through tissue engineering and microrobots as the delivery is sustained and targeted with fewer rejection responses.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".