From cell-SELEX to tissue-SELEX for targeted drug delivery and aptamer nanomedicine
Bibliographic record
Abstract
Aptamers are nucleic acid-based ligands that can selectively bind to target molecules. Because of their unique target-binding properties, the use of aptamers for targeting cell surface molecules has attracted broad research interest. The field has evolved from selecting aptamers against purified surface proteins to using whole cells (cell-SELEX) as targets. To further advance the field, the concept of tissue-SELEX was later proposed to ensure that selected aptamers possess optimal binding properties in more native in vivo environments. In this article, we review recent progress made for tissue-SELEX, covering methods including tissue slide-based SELEX, morph-X-SELEX, ex vivo-SELEX, and microfluidic tissue-SELEX. The target tissues include cornea, breast, ovary, lung, cardiac and thyroid tissues. For the diseases targeted, cancer is the most extensively studied followed by cardiomyopathies and vascular conditions. The advantages of each method are discussed and potential limitations are also critically reviewed. Applications of tissue- or in vivo-SELEX-derived aptamers in drug delivery include local administration for ocular diseases and systemic administration for lung cancer. Finally, future directions are discussed, emphasizing the need for systematic comparative studies to evaluate cell-SELEX and tissue-SELEX derived aptamers, using antibodies as benchmarks to guide the development of clinically relevant therapeutic applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.004 | 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".