Immunoengineering in glioblastoma imaging and therapy
Bibliographic record
Abstract
Patients diagnosed with glioblastoma have poor prognosis. Conventional treatment strategies such as surgery, chemotherapy, and radiation therapy demonstrated limited clinical success and have considerable side effects on healthy tissues. A central challenge in treating brain tumors is the poor permeability of the blood-brain barrier (BBB) to therapeutics. Recently, various methods based on immunotherapy and nanotechnology have demonstrated potential in addressing these obstacles by enabling precise targeting of brain tumors to minimize adverse effects, while increasing targeted drug delivery across the BBB. In addition to treating the tumors, these approaches may be used in conjunction with imaging modalities, such as magnetic resonance imaging and positron emission tomography to enhance the prognosis procedures. This review aims to provide mechanistic understanding of immune system regulation in the central nervous system and the benefits of nanoparticles in the prognosis of brain tumors. This article is characterized under: Diagnostic Tools > in vivo Nanodiagnostics and Imaging Nanotechnology Approaches to Biology > Cells at the Nanoscale Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".