Treatment for Glioblastoma Multiforme Using Solid Lipid Nanoparticles with Grafted Melanotransferrin Antibody
Bibliographic record
Abstract
Solid lipid nanoparticles (SLNs) comprising complex internal lipids were conjugated with melanotransferrin antibody (MA) to carry anticancer etoposide across the blood-brain barrier (BBB) for managing glioblastoma multiforme (GBM).MA was crosslinked on the surface of etoposide-loaded SLNs (ETP-SLNs) to target human brain-microvascular endothelial cells (HBMECs) and U87MG cells.The experimental evidences showed that an increase in the tripalmitin weight percentage in lipids enhanced the particle size and viability of U87MG cells, however decreased the etoposide loading efficiency, MA conjugation efficiency, and permeability coefficient for etoposide across the BBB.A high level of MA on the particle surface increased the atomic ratio of nitrogen to phosphorus and permeability coefficient for propidium iodide and etoposide across the BBB, however reduced the MA conjugation efficiency, transendothelial electrical resistance, and viability of U87MG cells.Based on immunochemical staining, we found that MA on ETP-SLNs triggered the melanotransferrin-mediated transcytosis and promoted the growth-inhibitory efficacy to U87MG cells.MAconjugated ETP-SLNs can be a promising colloidal delivery system for malignant GBM pharmacotherapy.
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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".