Costimulatory blockade with belatacept in clinical and experimental transplantation – a review
Bibliographic record
Abstract
BACKGROUND: Current maintenance immunosuppression agents have been critical to the improved graft and patient survival rates in solid organ transplantation observed over the past decade. However, long-term follow-up has revealed that these agents are associated with troublesome side effects and chronic toxicity, contributing to graft loss and death. OBJECTIVES: Costimulation blockade has long been recognized as an important target for immunomodulation in solid organ transplantation. Belatacept, a high-affinity chimeric fusion protein that binds to CD80/CD86 on antigen-presenting cells, has shown great promise in renal transplantation and is now in Phase III trials. METHODS: This review explores the development and efficacy of belatacept, compared with currently approved immunosuppressive agents used in transplantation. RESULTS: Belatacept seems to be an effective alternative to current maintenance immunosuppressive therapies, with no apparent end organ toxicity and a minimal side-effect profile. This agent works best when used in combination with therapies that target different pathways of T-cell activation, but the optimal regimen has not yet been identified. Data generated in ongoing clinical trials will be essential in validating previous studies and for further development of belatacept-based combinatorial strategies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".