Tailored Lipoprotein‐Like miRNA Delivery Nanostructure Suppresses Glioma Stemness and Drug Resistance through Receptor‐Stimulated Macropinocytosis
Bibliographic record
Abstract
Glioma initiating cells (GICs) function as the seed for the propagation and relapse of glioma. Designing a smart and efficient strategy to target the GICs and to suppress the multiple signaling pathways associated with stemness and chemoresistance is essential to achieving a cancer cure. Inspired by the metabolic difference in endocytosis between GICs, differentiated glioma cells, and normal cells, a tailored lipoprotein-like nanostructure is developed to amplify their internalization into GICs through receptor-stimulated macropinocytosis. As CXCR4 is highly expressed on GICs and glioma tumor sites, meanwhile, the activation of CXCR4 induces the receptor-stimulated macropinocytosis pathway in GICs, this CXCR4 receptor-stimulated lipoprotein-like nanoparticle (SLNP) achieves efficient accumulation in GICs in vitro and in vivo. By carrying microRNA-34a in the core, this tailored SLNP reduces sex-determining region Y-box 2 and Notch1 expression, powerfully inhibits GICs stemness and chemoresistance, and significantly prolongs the survival of GICs-bearing mice. Taken together, a tailored lipoprotein-based nanostructure realizes efficient GICs accumulation and therapeutic effect through receptor-stimulated macropinocytosis, providing a powerful nanoplatform for RNA interference drugs to combat glioma.
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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) | 0.000 | 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 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".