DDEL-01. ENHANCING DRUG DELIVERY WITH MRgFUS FOR DIFFUSE INTRINSIC PONTINE GLIOMA MODEL
Bibliographic record
Abstract
Abstract Diffuse intrinsic pontine glioma (DIPG) is a surgically unresectable and devasting tumor in children. To date, there have been no effective chemotherapeutics despite a myriad of clinical trials. The intact blood-brain barrier (BBB) in part is responsible for the limited clinical response to chemotherapy. MRI guided focused ultrasound (MRgFUS) is a promising non-invasive tissue ablative method for CNS tumors. Moreover, MRgFUS allows for the temporary disruption of BBB. Our first objective was to determine the feasibility and safety of temporary BBB disruption within the brainstem using MRgFUS following intravenous (IV) administration of microbubbles in vivo. Our second objective was to select effective chemotherapeutics against DIPG cell lines, and to examine their therapeutic effects with MRgFUS in a mouse model of DIPG which exhibits an intact BBB. The non-invasive opening of the BBB was determined in the brainstem of normal rodents using physiological monitoring and histological analysis. Doxorubicin was selected from a drug screen consisting of conventional chemotherapeutics using SU-DIPG4 and SU-DIPG17 cell lines. We established SU-DIPG17 xenografts which demonstrated diffusely infiltrative tumor growth similar to human DIPG. By LC-MS/MS analysis, MRgFUS led to a 4-fold increase in doxorubicin concentrations within the brainstem tumors following IV administration when compared to IV administration alone, We demonstrated feasibility and safety of MRgFUS in the rodent brainstem and have shown that MRgFUS increases doxorubicin uptake in the brainstem of a rodent model of DIPG. These preclinical data will be helpful in designing clinical trials of BBB disruption using MRgFUS for DIPG in children.
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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 | 0.001 |
| 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".