MétaCan
Menu
Back to cohort
Record W2888002058 · doi:10.3390/ph11030080

pH-Control in Aptamer-Based Diagnostics, Therapeutics, and Analytical Applications

2018· review· en· W2888002058 on OpenAlex
Micaela Belleperche, Maria C. DeRosa

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePharmaceuticals · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsAptamerNanotechnologyUSableNucleic acidDrug deliveryChemistryComputational biologyComputer scienceBiologyMaterials scienceMolecular biologyBiochemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.069
GPT teacher head0.436
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it