Nanoparticles and CNS Delivery of Therapeutic Agents in the Treatment of Primary Brain Tumors
Bibliographic record
Abstract
Patients affected by malignant brain tumor present an extremely poor prognosis, notwithstanding improvements in surgery techniques and therapeutic protocols. Late diagnosis and the limitation of conventional therapies are major reasons for this unsolved clinical problem. The blood-brain barrier formed by a complex of endothelial cells, astrocyte and pericytes reduces notably the diffusion of a large number of therapeutic agents. Nanotechnology involves the design, synthesis, and characterization of materials and devices that have a functional organization in at least one dimension on the nanometer scale. The nanoparticles have emerged as potential vectorsfor brain delivery able to overcome the difficulties of modern strategies. Nanoparticles drug delivery systems can be, also, used to provide targeted delivery of drugs, improve bioavailability, sustains release of drugs for systemic delivery.Moreover, multi-functionality can be engineered into a single nanoplatform so that it can provide tumor-specific detection, treatment, and follow-up monitoring. In this study we will focus on the blood-brain barrier role and possibilities of its therapeutic overcoming. Recent studies of some kinds of nanoparticles systems in brain tumors treatment are summarized.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".