IMG-35. Targeted blood-nerve-barrier opening for the delivery of MEK inhibitor selumetinib in peripheral nerve sheath tumours using focused ultrasound
Bibliographic record
Abstract
Abstract Neurofibromatosis-1 (NF1) is a genetic disorder affecting 1 in 2500 births and is characterized by the development of multiple peripheral nerve sheath tumours (PNSTs). Treatment involves surgical resection and radiation therapy however both are invasive and associated with morbidity, pain, neurological complications, and long-term risks associated with ionizing radiation. Clinical trials in NF1 patients have identified selumetinib as a promising chemotherapeutic as it can reduce tumour size and inhibit tumour progression. However, only 68% of patients showed a partial response with 10% discontinuing treatment due to toxicities; adverse events occur in 50% of patients. Focused ultrasound (FUS) is a non-invasive, non-ionizing technique that uses ultrasonic waves to manipulate deep tissue without affecting overlying or surrounding structures. Through stable cavitation of intravenous microbubbles, FUS can disrupt the blood-nerve-barrier (BNB) and could offer a novel strategy in targeted selumetinib delivery to PNSTs, enabling lower systemic doses and reducing off-target toxicities. We first wanted to confirm the feasibility of non-invasive BNB disruption in the sciatic nerve. In an acute experiment with 6 C57BL/6J mice, the right sciatic nerve was sonicated at 0.3 MPa. Evan’s blue dye penetration suggested successful BNB disruption. Half the animals were sacrificed immediately and both the right and left sciatic nerves were collected. The other half were kept for acute clinical assessments at 24 hours post FUS. No change in hind limb function was observed with a slight reduction in avoiding response, however further studies are required to elucidate these changes. The sciatic nerves were collected after. H&E stains were performed on the nerves to assess for tissue damage. Based on images taken, there was no evidence of tissue damage between the sonicated and un-sonicated nerves. This suggests this technique may be feasible. Future work will be to perform FUS in a tumour model with and without drug.
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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".