A cloaked human stem-cell-derived neural graft capable of functional integration and immune evasion in rodent models
Bibliographic record
Abstract
Human pluripotent stem cell (hPSC)-derived therapies are a realistic possibility for numerous disorders, including Parkinson's disease. While generating replacement neurons is achievable, immunosuppressive drug challenges, to prevent rejection, remain. Here we adopted a hPSC line (termed H1-FS-8IM), engineered to overexpress 8 immunomodulatory transgenes, to enable transplant immune evasion. In co-cultures, H1-FS-8IM PSC-derived midbrain neurons evaded rejection by T lymphocytes, natural killer cells, macrophages, and dendritic cells. In humanized mice, allogeneic H1-FS-8IM neural grafts evaded rejection, while control hPSC-derived neural grafts evoked activation of human immune cells, elevated inflammatory cytokines in blood and cerebrospinal fluid, and caused spleen and lymph node enlargement. H1-FS-8IM neural grafts retained functionality, reversing motor deficits in Parkinsonian rats. Additional incorporation of a suicide gene into the H1-FS-8IM hPSC line enabled proliferative cell elimination within grafts. Findings demonstrate feasibility of generating a population-wide applicable, safe, off-the-shelf cell product, suitable for treating diseases for which cell-based therapies are a viable option.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".