Tumor targeted delivery of doxorubicin in malignant peripheral nerve sheath tumors
Bibliographic record
Abstract
Peripheral nerve sheath tumors are benign tumors that have the potential to transform into malignant peripheral nerve sheath tumors (MPNSTs). Interleukin-13 receptor alpha 2 (IL13Rα2) is a cancer associated receptor expressed in glioblastoma and other invasive cancers. We analyzed IL13Rα2 expression in several MPNST cell lines including the STS26T cell line, as well as in several peripheral nerve sheath tumors to utilize the IL13Rα2 receptor as a target for therapy. In our studies, we demonstrated the selective expression of IL13Rα2 in several peripheral nerve sheath tumors by immunohistochemistry (IHC) and immunoblots. We established a sciatic nerve MPNST mouse model in NIH III nude mice using a luciferase transfected STS26T MPNST cell line. Similarly, analysis of the mouse sciatic nerves after tumor induction revealed significant expression of IL13Rα2 by IHC when compared to a normal sciatic nerve. IL13 conjugated liposomal doxorubicin was formulated and shown to bind and internalized in the MPNST cell culture model demonstrating cytotoxic effect. Our subsequent in vivo investigation in the STS26T MPNST sciatic nerve tumor model indicated that IL13 conjugated liposomal doxorubicin (IL13LIPDXR) was more effective in inhibiting tumor progression compared to unconjugated liposomal doxorubicin (LIPDXR). This further supports that IL13 receptor targeted nanoliposomes is a potential approach for treating MPNSTs.
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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".