Intracerebral xenotransplantation: recent findings and perspectives for local immunosuppression
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cell therapy is a promising strategy for tissue repair in the central nervous system. In this perspective, several cell types are being considered, including allogenic neuroblasts, embryonic stem cells and induced pluripotent stem cells. The use of allogenic neuroblasts as cell source is limited by logistics and ethical problems whereas transplantation of the last two cell types is hampered by their propensity to generate tumour. In this context, transplantation of xenogeneic neural cells appears as an attractive approach for effective neuronal replacement in case of neurodegenerative disorders. RECENT FINDINGS: With the emergence of embryonic and induced pluripotent stem cells as potential cell source in regenerative medicine, little attention has been paid to the possibility of transplanting xenogenic neural cells in the central nervous system. However, recent progress to circumvent the host immune response in the brain has raised encouraging perspectives for intracerebral xenotransplantation as restorative strategy. SUMMARY: To date, most of the immunosuppressive strategies designed for long-term survival of intracerebral neural transplants were based on systemic immunosuppression that has detrimental side-effects. The immunological status of the brain and the presence of the blood-brain barrier raise the possibility of local immunosuppression. This article provides an overview of the strategies recently developed to protect intracerebral neural transplants with special focus on local immunosuppression.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".