pH-Control in Aptamer-Based Diagnostics, Therapeutics, and Analytical Applications
Bibliographic record
Abstract
Aptamer binding has been used effectively for diagnostics, in-vivo targeting of therapeutics, and the construction and control of nanomachines. Nanostructures that respond to pH by releasing or changing affinity to a target have also been used for in vivo delivery, and in the construction of sensors and re-usable nanomachines. There are many applications that use aptamers together with pH-responsive materials, notably the targeted delivery of chemotherapeutics. However, the number of reported applications that directly use pH to control aptamer binding is small. In this review, we first discuss the use of aptamers with pH-responsive nanostructures for chemotherapeutic and other applications. We then discuss applications that use pH to denature or otherwise disrupt the binding of aptamers. Finally, we discuss motifs using non-canonical nucleic acid base pairing that can shift conformation in response to pH, followed by an overview of engineered pH-controlled aptamers designed using those motifs.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".