The use of liposomes functionalized with the NFL-TBS.40–63 peptide as a targeting agent to cross the in vitro blood–brain barrier and target glioblastoma cells
Bibliographic record
Abstract
Glioblastoma is the most common and aggressive brain tumor. Current treatments do not allow to cure the patients. This is partly due to the blood-brain barrier (BBB), which limits the delivery of drugs to the pathological site. To overcome this, we developed liposomes functionalized with a neurofilament-derived peptide, NFL-TBS.40-63 (NFL), known for its highly selective targeting of glioblastoma cells. First, in vitro BBB model was developed to check whether the NFL can also promote barrier crossing in addition to its active targeting capacity. Permeability experiments showed that the NFL peptide was able to cross the BBB. Moreover, when the BBB was in a pathological situation, i.e., an in vitro blood-brain tumor barrier (BBTB), the passage of the NFL peptide was greater while maintaining its glioblastoma targeting capacity. When the NFL peptide was associated to liposomes, it enhanced their ability to be internalized into glioblastoma cells after passage through the BBTB, compared to liposomes without NFL. The cellular uptake of liposomes was limited in the endothelial cell monolayer in comparison to the glioblastoma one. These data indicated that the NFL peptide is a promising cell-penetrating peptide tool when combined with drug delivery systems for the treatment of glioblastoma.
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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".