Bibliographic record
Abstract
PURPOSE OF REVIEW: Current immunosuppressive regimens used in kidney transplantation are sometimes ineffective and carry significant risks of morbidity and mortality. Cellular therapies are a promising alternative to prolong graft survival while minimizing treatment toxicity. We review the recently published breakthrough studies using cell therapies in kidney transplantation. RECENT FINDINGS: The reviewed phase I and II trials showed that cell therapies are feasible and safe in kidney transplantation, sometimes associated with less infectious complications than traditional regimens. Regulatory T cells and macrophages were added to the induction regimen, allowing for lower immunosuppressive drug doses without higher rejection risk. Regulatory T cells are also a treatment for subclinical rejection on the 6 months biopsy. Other strategies, like bone marrow-derived mesenchymal cells, genetically modified regulatory T cells, and chimerism-based tolerance are also really promising. In addition, to improve graft tolerance, cell therapy could be used to prevent or treat viral infection after transplantation. SUMMARY: Emerging data underline that cell therapy is a feasible and safe treatment in kidney transplantation. Although the evidence points to a benefit for transplant recipients, studies with standardized protocols, representative control groups, and longer follow-up are needed to answer the question definitively and guide future research.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".