The Role of mTOR Inhibitors after Liver Transplantation for Hepatocellular Carcinoma
Bibliographic record
Abstract
Liver transplantation is a treatment option for nonresectable patients with early-stage HCC, with more significant advantages when Milan criteria are fulfilled. An immunosuppressive regimen is required to reduce the risk of graft rejection after transplantation, and CNIs represent the drugs of choice in this setting. However, their inhibitory effect on T-cell activity accounts for a higher risk of tumour regrowth. mTOR inhibitors (mTORi) have been introduced as an alternative immunosuppressive approach to conventional CNI-based regimens to address both immunosuppression and cancer control. The PI3K-AKT-mTOR signalling pathway regulates protein translation, cell growth, and metabolism, and the pathway is frequently deregulated in human tumours. Several studies have suggested the role of mTORi in reducing HCC progression after LT, accounting for a lower recurrence rate. Furthermore, mTOR immunosuppression controls the renal damage associated with CNI exposure. Conversion to mTOR inhibitors is associated with stabilizing and recovering renal dysfunction, suggesting an essential renoprotective effect. Limitations in this therapeutic approach are related to their negative impact on lipid and glucose metabolism as well as on proteinuria development and wound healing. This review aims to summarize the roles of mTORi in managing patients with HCC undergoing LT. Strategies to overcome common adverse effects are also proposed.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".