Frameless Stereotactic Targeting of the Cerebellar Dentate Nucleus in Nonhuman Primates: Translatable Model for the Surgical Delivery of Gene Therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it