The Duke University researchers recently developed a unique nanoparticle type known as "nanorattle" that greatly improves light emitted from within its external shell.

Loaded with Raman reporters, light scattering dyes commonly used to detect biomarkers of disease in organic samples can amplify and identify signals from separate nanoprobe types minus the need for a costly "machine or medical professional to read the results," a Phys.org report said.

 

In small "proof-of-concept" research, the nanorattles precisely identified neck and head cancers through an artificial intelligence-enabled point-of-care device that could revolutionize how such cancers and other illnesses are detected in low-resource sites to enhance global health.

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Nanoparticles
(Photo: Wikimedia Commons/ Nandiyanto)
Researchers recently developed a unique nanoparticle type known as ‘nanorattle’ that greatly improves light emitted from within its external shell.


Nanorattles

Results of the study appeared early this month in the Journal of Raman Spectroscopy. According to R. Eugene and Susie E. Goodson, Distinguished Professor of Biomedical Engineering at Duke Tuan Vo-Dinh, the notion of trapping Raman reporters in these nanorattles has been done in the past.

He explained that their group had developed a new probe type with an accurately tunable gap between the interior core and outer shell, which enables the researchers to load multiple Raman reporter types and amplify their light emission, known as surface-enhanced Raman scattering.

To develop nanorattles, the study authors began with a solid gold sphere about a sphere or cube; they are using a corrosion process known as a galvanic replacement that's hallowing out the silver, making a cage-like shell around the core.

Essentially, the structure is soaked in a solution containing positively charged Raman reporters, drawn into the outer cage by the gold core that's negatively charged.

'Lab-on-a-Stick Device'

Vo-Dinh added once they had the nanorattles working, they wanted to make biosensing devices to identify infectious diseases or cancers before an individual even knew he was sick.

With how powerful the nanorattles' signal enhancement is, the team thought they could make a simple test that any person could easily read at the point of care.

In this new research, Vo-Dinh and his collaborators used the nanorattle technology to a lab-on-a-stick device capable of detecting neck and head cancers, appearing anywhere within the shoulders and the brain, usually in the mount, throat, and nose.

The rate of survival for such cancers has hovered from 40 to 60 percent for decades. While such statistics have improved previously in the United States, they have worsened in low-resource settings, where risk factors like drinking, smoking, and chewing, among others, are much more prevalent.

Use of Machine Learning Algorithm

A related ScienceDaily report said, in the experiments, the test was able to identify whether or not the specimens came from patients that had neck and head cancer with 100 percent preciseness.

Moreover, the experiments also showed that the nanorattle platform could handle multiple nanoprobe types because of a machine learning algorithm that can tease apart the distinctive signals.

This means that they can target multiple biomarkers at once. This is the objective of the current project, financially backed by the National Institutes of Health.

Many mRNA biomarkers are abundant in many cancer types, while other biomarkers can be utilized to evaluate the risk for patients and future treatment outcomes.

Detecting multiple biomarkers at once would contribute to the differentiation between cancers and look for other prognostic markers, like Human Papillomavirus or HPV, and both positive and negative controls.

Incorporating mRNA detection with novel nanorattle biosensing will lead to a paradigm shift in achieving a diagnostic mechanism that could revolutionize how such cancers, as well as other illnesses, are detected in low-resource sites.

Related information about nanotechnology for disease diagnosis is shown on UofTPharmacy's YouTube video below:

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