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Record W4408849657 · doi:10.70477/fief5112

MULTIVALENT APTAMER GENERATION THROUGH ROLLING CIRCLE AMPLIFICATION ON A NANOSTRUCTURED SURFACE FOR BIOSENSING APPLICATIONS

2024· article· en· W4408849657 on OpenAlexaff
Seyed Vahid Hamidi, Arash Khorrami, I. Imman, Roozbeh Siavash, Sara Mahshid

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicDiatoms and Algae Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsAptamerBiosensorRolling circle replicationNanotechnologyMaterials scienceSurface (topology)ChemistryDNABiologyMathematics

Abstract

fetched live from OpenAlex

Interest toward multivalent aptamers have been increased due their increased binding strength to target molecules, as they can attach to multiple sites at once, creating more stable interactions.This study introduces a method for creating and modifying multivalent aptamers on a surface using room temperature rolling circle amplification (RCA) technique and chemically modified primers.This technique involves attaching the primers to both flat screen-printed gold electrodes and nano-/microisland (NMI) electrodes where multivalent aptamers are then bio-synthesized and organized.These engineered aptamers were used to detect a specific target which is the severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) spike protein (SP) in this study. KEYWORDS: Multimeric aptamers • rolling circle amplification • electrochemical impedance spectroscopy (EIS) • nano-/microislands structures. INTRODUCTION:Multivalency is in a bio-inspired approach which plays a crucial role in various natural interactions.Particularly, in cellular, bacterial, and viral contexts, multivalent interactions contribute significantly to enhanced affinity by clustering receptors on surfaces.Analytical methods targeting low-abundance substances can particularly benefit from multivalent interactions.Among various aptamer-based structures, multimeric aptamers stand out due to their inherent advantages, such as straightforward and cost-effective chemical synthesis, as well as tolerance to diverse chemical modifications 1 .Electrochemical transducers are valued for their simplicity, and portability, offering high sensitivity for point-of-care (POC) applications.They are popular because they are affordable, accurate, require minimal equipment, and being rapid 2 .Notably, label-free affinity-based assays, like multimeric aptamer-based assays, can be easily integrated into these platforms 3 .This study proposes a new biofunctionalization strategy for electrochemical biosensing using gold flat and NMI working electrodes (WEs) and room-temperature RCA.The label-free electrochemical impedance spectroscopy (EIS) multimeric aptasensors can detect SARS-CoV-2 SP in buffer and saliva at femtomolar concentrations within 10 minutes (5 minutes for incubation and 5 minutes for EIS measurement), demonstrating high selectivity and sensitivity.Experimental: Optically transparent ITO SPE from Metrohm, Canada, were used to create gold 3D hierarchical NMIs via electrodeposition.Synthesis of the 3D gold NMIs involved a solution containing HAuCl4 (Sigma Aldrich) at a concentration of 100 mM in an HCl (0.5 M) supporting electrolyte solution, conducted at an applied potential of -600 mV relative to an Ag/AgCl reference electrode (Fig. 1, i).WEs were bio-functionalized with multimeric aptamers by using RCA reaction and modified primers (Fig. 1, ii). RESULTS AND DISCUSSION:In this study we introduced a new approach for generating and biofunctionalizing multimeric aptamers on a surface using RCA.This bioinspired network, featuring multiple capturing domains, demonstrated a high-affinity attachment to the target of interest, leading to higher EIS signals compared to monomeric aptamers.The surface RCA process on both nano-/microislands (NMIs) and flat working electrodes WEs underwent comprehensive characterization using electrochemical, microscopy and gel electrophoresis techniques (Fig. 2).This enabled the determination of the optimal amplification time and surface structure for biosensing applications (Fig. 3, i and ii).The NMI-based multimeric aptasensors demonstrated greater sensitivity within the range of 10 to 10 6 fg/mL of SP in buffer and saliva, with a detection limit of as low as 2 fg/mL (Fig. 3, iii).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.063
GPT teacher head0.346
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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