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Specific Nucleic Acid Detection Using a Nanoparticle Hybridization Assay

2024-09-06 · A. A. Aldakheel, C. B. Raub, H. T. Bui

Simple methods to detect biomolecules including specific nucleic acid sequences have received renewed attention since the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus pandemic. Notably, biomolecule detection that uses some form of signal amplification will have some form of amplification-related error, which in the polymerase chain reaction involves mispriming and subsequent signal amplification in the no template control, ultimately providing a limit of detection. To demonstrate the feasibility of the detection of a DNA target sequence without molecular or chemical signal amplification that avoids amplification errors, a gold nanoparticle aggregation assay was developed and tested. Two primers bracketing a 94 base pair target sequence from SARS-CoV-2 were conjugated to 10 nm diameter gold nanoparticles by the salt aging method, with conjugation and primer-target hybridization confirmed by agarose gel electrophoresis and nanospectrophotometry. Upon mixing of both conjugated nanoparticles with target, a surface plasmon resonance shift of 6 nm was observed, and lower electrophoretic mobility of a band containing both DNA fluorescence and gold absorption signals. This did not occur in the presence of a control DNA molecule of the same size and composition as the target but with a randomly scrambled base position. Nanoparticle tracking at 30 frames per second using a sensitive darkfield microscope revealed a lower measured diffusion coefficient of scattering objects in the target mixture than in the control mixture or with bare gold nanoparticles.

📄 PDF Abstract BibTeX arXiv:2409.03983

Code (2)

Aldakheelarwa88/ABT_Diffusion_Videos 공식 구현
Aldakheelarwa88/AuNPs_Diffusion_Videos 공식 구현

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Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
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Diffusion Diffusion models generate samples by gradually removing noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound…
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