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Record W4309245369 · doi:10.1002/cbic.202200564

Salt‐Toggled Capture Selection of Uric Acid Binding Aptamers

2022· article· en· W4309245369 on OpenAlexaff
Yibo Liu, Juewen Liu

Bibliographic record

VenueChemBioChem · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAptamerHypoxanthineUric acidIsothermal titration calorimetryChemistryBiochemistryXanthineRasburicaseCombinatorial chemistrySystematic evolution of ligands by exponential enrichmentEnzymeMolecular biologyBiologyRNAHyperuricemiaGene

Abstract

fetched live from OpenAlex

Abstract Uric acid is the end‐product of purine metabolism in humans and an important biomarker for many diseases. To achieve the detection of uric acid without using enzymes, we previously selected a DNA aptamer for uric acid with a K d of 1 μM but the aptamer required multiple Na + ions for binding. Saturated binding was achieved with around 700 mM Na + and the binding at the physiological condition was much weaker. In this work, a new selection was performed by alternating Mg 2+ ‐containing buffers with Na + and Li + . After 13 rounds of selection, a new aptamer sequence named UA‐Mg‐1 was obtained. Isothermal titration calorimetry confirmed aptamer binding in both selection buffers, and the K d was around 8 μM. The binding of UA‐Mg‐1 to UA required only Mg 2+ . This is an indicator of successful switching of metal dependency via the salt‐toggled selection method. The UA‐Mg‐1 aptamer was engineered into a fluorescent biosensor based on the strand‐displacement assay with a limit of detection of 0.5 μM uric acid in the selection buffer. Finally, comparison with the previously reported Na + ‐dependent aptamer and a xanthine/uric acid riboswitch was also made.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.007
GPT teacher head0.242
Teacher spread0.235 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2022
Admission routes1
Has abstractyes

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