Frameless Stereotactic Targeting of the Cerebellar Dentate Nucleus in Nonhuman Primates: Translatable Model for the Surgical Delivery of Gene Therapy
Bibliographic record
Abstract
BACKGROUND: Stereotactic targeting techniques in nonhuman primate (NHP) models are often utilized in the preclinical investigation of new drug therapies with the goal of demonstrating accurate and reliable delivery of a therapy to the target tissue. However, targeting certain neuroanatomical structures can be challenging. The deep cerebellar nuclei, specifically the dentate nucleus, are potential stereotactic targets for the treatment of certain ataxias. Currently, there are no detailed techniques describing frameless targeting of these structures in a NHP model. A well-defined, accurate, and reliable stereotactic surgical approach to the dentate in these animal models is critical to prove the feasibility and safety of drug delivery in order to develop clinical protocols. METHODS: Frameless stereotactic neuronavigation was employed to target the bilateral dentate nuclei of the cerebellum in four healthy juvenile Cynomolgus monkeys via a suboccipital, transcerebellar approach. The precision and accuracy of the targeting were evaluated radiologically and histologically. RESULTS: Using the described surgical methodology, we were successful in hitting the target deep cerebellar nuclei seven out of eight times. CONCLUSION: Frameless stereotactic targeting of the cerebellar dentate nuclei in NHPs for future investigational drug delivery is feasible, safe, and accurate as described by this report. Potential areas for improving the technique are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".