High-frequency ultrasound-guided intrathecal injections in a young mouse model: Targeting the central nervous system in drug delivery
Bibliographic record
Abstract
BACKGROUND: Intrathecal injections provide important access to the central nervous system for delivery of anesthetic, analgesic or chemotherapeutic drugs that do not otherwise cross the blood-brain barrier. The administration of drugs via this route in animal models is challenging due to an inability to visualize the small target space during injection. Successful drug delivery therefore requires expertise in indirectly assessing vertebral and spinal cord anatomy and gaining advanced procedural skills. These factors are especially compounded in small animals such as mice (the most common mammalian model) and in investigations modeling pediatric drug delivery, where the animal is even smaller. NEW METHOD: To address these issues, we have developed a method in which high-frequency ultrasound imaging is used to visualize and target the lumbar intrathecal space for injections. The technique is demonstrated in mice as young as postnatal day 16. To evaluate the method, a gadolinium-based magnetic resonance imaging (MRI) contrast agent was injected intrathecally, and subsequent brain delivery was verified post-injection by MRI. RESULTS: Successful intrathecal injections of the MRI contrast agent showed distribution to the brain. In this study, we achieved a targeting success rate of 80% in 20 animals. COMPARISON WITH EXISTING METHODS AND CONCLUSION: We expect that the new method will be convenient for drug delivery to the central nervous system in rodent research and provide higher reliability than unguided approaches, an essential contribution that will enable intrathecal delivery in pediatric mouse models.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".