Reduced xenograft rejection in rat striatum after pretransplant photodynamic therapy of murine neural xenografts
Bibliographic record
Abstract
OBJECT: The goal of this study was to develop a method of reducing neural xenograft rejection by pretreating the graft with photodynamic therapy (PDT). METHODS: Xenograft cell suspensions were prepared from fetal mouse mesencephalon, after which they were incubated for 30 minutes with various concentrations of a photosensitizer, verteporfin for injection, and light exposure. The xenograft cell suspensions were injected into the dopamine-depleted striata of 40 hemiparkinsonian rats assigned to different treatment groups. Four weeks after transplantation, xenograft function (determined by methamphetamine-induced rotation) and survival (determined by immunohistochemical staining for murine neurons) were compared. Group 1 animals (xenografts pretreated with 25 ng/ml verteporfin) and Group 3 animals (no verteporfin pretreatment, but daily administration of cyclosporin A) had significantly better xenograft survival and function compared with control animals (no pretreatment with verteporfin). Group 2 animals (xenografts pretreated with 250 ng/ml verteporfin) had no significant improvement. CONCLUSIONS: This work demonstrates improved neural xenograft survival and function when using pretransplant PDT of the graft in a rodent model. The potential benefits of this new therapy are its convenience (one pretransplant treatment) and its compatibility with host 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 | 0.000 | 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 | 0.000 |
| 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".