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Duncan Graham

Publications and source records attributed to Duncan Graham.

2 recordsLinked to original sources

Ultrasensitive Alzheimer's Disease Biomarker Detection with Nanopillar Photonic Crystal Biosensors

The recent development of drugs able to mitigate neurodegenerative diseases has created an urgent need for novel diagnostics. Although biomarker detection directly in patients' blood is now possible, low-cost point-of-care tests remain a challenge, because relevant biomarkers, especially amyloid-\beta (A$\beta$) peptides, are small, they occur at very low concentrations, and detecting a single marker is insufficient. Here, we introduce an optical biosensor based on a nanopillar structure that employs a gold nanoparticle amplification strategy. The sensor is able to detect 20 pg/ml of A$\beta$42 and A$\beta$40 in undiluted serum, which is the clinically required level. We also show the detection of the A$\beta$42 and A$\beta$40 peptides in the same channel, which is highly relevant for assessing disease progress and opens a route towards multiplexing. Alongside their simplicity and portability, these nanotechnology innovations make a major contribution to the ability to detect and monitor the progression of neurodegenerative diseases such as Alzheimer's.

physics.optics

Bayesian modelling and quantification of Raman spectroscopy

Raman spectroscopy can be used to identify molecules such as DNA by the characteristic scattering of light from a laser. It is sensitive at very low concentrations and can accurately quantify the amount of a given molecule in a sample. The presence of a large, nonuniform background presents a major challenge to analysis of these spectra. To overcome this challenge, we introduce a sequential Monte Carlo (SMC) algorithm to separate the observed spectrum into a series of peaks plus a smoothly-varying baseline, corrupted by additive white noise. The peaks are modelled using Lorentzian or Gaussian broadening functions, while the baseline is estimated using a penalised cubic spline. This latent continuous representation accounts for differences in resolution between measurements. By incorporating this representation in a Bayesian model, we can quantify the relationship between molecular concentration and peak intensity, thereby providing an improved estimate of the limit of detection (LOD), which is of major importance in analytical chemistry.

stat.AP