Innovative aptamer approaches in glial tumor diagnostics and therapy: Progress and future directions
Bibliographic record
Abstract
Glial tumors, particularly glioblastomas, remain among the most challenging cancers to diagnose and treat due to their heterogeneity, infiltrative nature, and the protective blood-brain barrier that impedes drug delivery. Aptamers-short, single-stranded nucleic acids selected for high-affinity target binding-have emerged as promising agents in neuro-oncology, offering advantages such as high specificity, low immunogenicity, and superior tissue penetration compared to conventional antibodies. This review outlines recent advancements in aptamer-based technologies for the diagnosis and treatment of glial brain tumors. We describe the use of aptamers in molecular imaging, liquid biopsy platforms, intraoperative tumor visualization, and the targeted delivery of therapeutic agents, including small molecules, small interfering RNAs (siRNAs), and immunomodulators. The integration of aptamer systems with nanotechnology and AI is accelerating the development of sensitive, non-invasive diagnostic tools and multifunctional theranostic platforms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".