Liposomal Formulations of Tacrolimus and Rapamycin Increase Graft Survival and Fiber Outgrowth of Dopaminergic Grafts
Bibliographic record
Abstract
The immunosuppressive drugs tacrolimus (TAC) and rapamycin (RAPA) have both been found to have neuroprotective effects on dopaminergic neurons. The purpose of the present study was to investigate whether liposomal formulations of these drugs administered directly into the brain improve cell survival and fiber outgrowth. Rats with unilateral 6-hydroxydopamine lesions were transplanted with 800,000 fetal rat ventral mesencephalic cells and randomly divided to one of four groups. Group 1 received a transplant containing cells only; group 2 received a cell suspension containing 0.68 microM liposomal RAPA (LRAPA); group 3 received a cell suspension containing 2.0 microM liposomal TAC (LTAC); and group 4 received a cell suspension containing a liposomal formulation of both 0.68 microM RAPA and 2.0 microM TAC (LRAPATAC). Rats were sacrificed after 6 weeks, and cell survival and fiber outgrowth were assessed using tyrosine hydroxylase (TH) immunohistochemistry. The animals receiving a cell suspension containing either LTAC or LRAPATAC were found to have significantly more surviving TH-immunoreactive (TH-ir) cells than the control group receiving cells only. The group receiving LTAC had significantly longer fibers, the group receiving LRAPA had significantly more fibers close to the graft, and the group receiving LRAPATAC had significantly more fibers at all distances. This study shows the feasibility of using liposomal formulations of neuroimmunophilins directly in the brain at the time of implantation to improve graft survival and fiber outgrowth. Furthermore, we have shown that the combination of LTAC and LRAPA has a synergistic effect. These compounds may play an important role in optimizing graft survival and host reinnervation in cell-mediated brain repair strategies for the treatment of neurological conditions.
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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".