Spectroscopic molecular-fingerprint profiling of saliva

Buchan, Emma; Kelleher, Liam; Clancy, Michael; Rickard, Jonathan James Stanley; Oppenheimer, Pola Goldberg

doi:10.1016/j.aca.2021.339074

Cited by 18 publications

(10 citation statements)

References 64 publications

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“…[23] Molecular "barcodes" can be constructed using SERS and the combination with advanced machine learning techniques for further signal processing enhances the detection accuracy. [24] Alterations in immunoglobulin and other proteins were identified by attenuated total reflection-Fourier transform infrared spectroscopy which exhibited a significant power of discrimination between SARS-CoV-2 infected patients and healthy individuals. [25] The technique can also generate diagnostic fingerprints from saliva samples in combination with multivariate analysis.…”

Section: Overview Of Salivary Protein Detectionmentioning

confidence: 99%

Advances in Electrochemical Biosensors Based on Nanomaterials for Protein Biomarker Detection in Saliva

et al. 2022

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The focus on precise medicine enhances the need for timely diagnosis and frequent monitoring of chronic diseases. Moreover, the recent pandemic of severe acute respiratory syndrome coronavirus 2 poses a great demand for rapid detection and surveillance of viral infections. The detection of protein biomarkers and antigens in the saliva allows rapid identification of diseases or disease changes in scenarios where and when the test response at the point of care is mandated. While traditional methods of protein testing fail to provide the desired fast results, electrochemical biosensors based on nanomaterials hold perfect characteristics for the detection of biomarkers in point-of-care settings. The recent advances in electrochemical sensors for salivary protein detection are critically reviewed in this work, with emphasis on the role of nanomaterials to boost the biosensor analytical performance and increase the reliability of the test in human saliva samples. Furthermore, this work identifies the critical factors for further modernization of the nanomaterial-based electrochemical sensors, envisaging the development and implementation of next-generation sample-in-answer-out systems.

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Section: Overview Of Salivary Protein Detectionmentioning

confidence: 99%

Advances in Electrochemical Biosensors Based on Nanomaterials for Protein Biomarker Detection in Saliva

et al. 2022

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“…Data Classification and Statistical Analysis: Multi-variate analysis was performed using the self-optimizing Kohonen index network (SKiNET) artificial neural network algorithm. [51,79] SKiNET was based on the separation of data classes in a self-organizing map (SOM) and an the undefining characterization using the self-organizing map discriminant index (SOMDI), which appends a set of label vectors to each neuron and allows to study the most prominent features that cause the activation of a particular neuron to a class label, enabling the rapid subsequent classification of the tested data. SOM defines 2D maps of neurons, typically arranged as a grid of hexagons.…”

Section: Methodsmentioning

confidence: 99%

Advanced Tuneable Micronanoplatforms for Sensitive and Selective Multiplexed Spectroscopic Sensing via Electro‐Hydrodynamic Surface Molecular Lithography

Gomes,

Hin‐Chu,

Rickard

et al. 2024

Advanced Science

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Micro‐ and nanopatterning of materials, one of the cornerstones of emerging technologies, has transformed research capabilities in lab‐on‐a‐chip diagnostics. Herein, a micro‐ and nanolithographic method is developed, enabling structuring materials at the submicron scale, which can, in turn, accelerate the development of miniaturized platform technologies and biomedical sensors. Underpinning it is the advanced electro‐hydrodynamic surface molecular lithography, via inducing interfacial instabilities produces micro‐ and nanostructured substrates, uniquely integrated with synthetic surface recognition. This approach enables the manufacture of design patterns with tuneable feature sizes, which are functionalized via synthetic nanochemistry for highly sensitive, selective, rapid molecular sensing. The development of a high‐precision piezoelectric lithographic rig enables reproducible substrate fabrication with optimum signal enhancement optimized for functionalization with capture molecules on each micro‐ and nanostructured array. This facilitates spatial separation, which during the spectroscopic sensing, enables multiplexed measurement of target molecules, establishing the detection at minute concentrations. Subsequently, this nano‐plasmonic lab‐on‐a‐chip combined with the unconventional computational classification algorithm and surface enhanced Raman spectroscopy, aimed to address the challenges associated with timely point‐of‐care detection of disease‐indicative biomarkers, is utilized in validation assay for multiplex detection of traumatic brain injury indicative glycan biomarkers, demonstrating straightforward and cost‐effective micro‐ and nanoplatforms for accurate detection.

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“…They were able to predict the sex of the donor only when they analyzed saliva samples from donors of similar ages. Buchan et al used Raman spectroscopy and machine learning to study the spectral fingerprint of saliva . Specific characteristics investigated included the sex and age of the donor and the TSD of the stain.…”

Section: Biologymentioning

confidence: 99%

“…Buchan et al used Raman spectroscopy and machine learning to study the spectral fingerprint of saliva. 43 Specific characteristics investigated included the sex and age of the donor and the TSD of the stain. The major causes of spectral differences were associated with changes in the levels of amino acids, proteins, and lipids in saliva.…”

Section: ■ Introductionmentioning

confidence: 99%