Volume and Infusion Rate Dynamics of Intraparenchymal Central Nervous System Infusion in a Large Animal Model
Bibliographic record
Abstract
Thalamic infusion of adeno-associated viral (AAV) vectors has been shown to have therapeutic effects in neuronopathic lysosomal storage diseases. Preclinical studies in sheep model of Tay-Sachs disease demonstrated that bilateral thalamic injections of AAV gene therapy are required for maximal benefit. Translation of thalamic injection to patients carries risks in that (1) it has never been done in humans, and (2) dosing scale-up based on brain weight from animals to humans requires injection of larger volumes. To increase the safety margin of this infusion, a flexible cannula was selected to enable simultaneous bilateral thalamic infusion in infants while monitoring by imaging and/or to enable awake infusions for injection of large volumes at low infusion rates. In this study, we tested various infusion volumes (200-800 μL) and rates (0.5-5 μL/min) to determine the maximum tolerated combination of injection parameters. Animals were followed for ∼1 month postinjection with magnetic resonance imaging (MRI) performed at 14 and 28 days. T1-weighted MRI was used to quantify thalamic damage followed by histopathological assessment of the brain. Trends in data show that infusion volumes of 800 μL (2 × the volume required in sheep based on thalamic size) resulted in larger lesions than lower volumes, where the long infusion times (between 13 and 26 h) could have contributed to the generation of larger lesions. The target volume (400 μL, projected to be sufficient to cover most of the sheep thalamus) created the smallest lesion size. Cannula placement alone did result in damage, but this is likely associated with an inherent limitation of its use in a small brain due to the length of the distal rigid portion and lack of stable fixation. An injection rate of 5 μL/min at a volume ∼1/3 of the thalamus (400-600 μL) appears to be well tolerated in sheep both clinically and histopathologically.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".