Aptamers as Promising Molecular Recognition Elements for Diagnostics and Therapeutics in the Central Nervous System
Bibliographic record
Abstract
Oligonucleotide aptamers are short, synthetic, single-stranded DNA or RNA able to recognize and bind to a multitude of targets ranging from small molecules to cells. Aptamers have emerged as valuable tools for fundamental research, clinical diagnosis, and therapy. Due to their small size, strong target affinity, lack of immunogenicity, and ease of chemical modification, aptamers are an attractive alternative to other molecular recognition elements, such as antibodies. Although it is a challenging environment, the central nervous system and related molecular targets present an exciting potential area for aptamer research. Aptamers hold promise for targeted drug delivery, diagnostics, and therapeutics. Here we review recent advances in aptamer research for neurotransmitter and neurotoxin targets, demyelinating disease and spinal cord injury, cerebrovascular disorders, pathologies related to protein aggregation (Alzheimer's, Parkinson's, and prions), brain cancer (glioblastomas and gliomas), and regulation of receptor function. Challenges and limitations posed by the blood brain barrier are described. Future perspectives for the application of aptamers to the central nervous system are also discussed.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.003 |
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".