We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
LGC Clinical Diagnostics

Download Mobile App





A Novel Capsule-Based Smell Test for Diagnosis of Neurological and Respiratory Diseases

By LabMedica International staff writers
Posted on 10 May 2021
Print article
Image: Photograph of the novel capsule-based smell test (Photo courtesy of Queen Mary University of London)
Image: Photograph of the novel capsule-based smell test (Photo courtesy of Queen Mary University of London)
To aid in diagnosing diseases where loss of the sense of smell is a symptom, such as in chronic neurological conditions like Parkinson's and Alzheimer's diseases and in acute respiratory infections such as that caused by COVID-19, a team of researchers at Queen Mary University of London (United Kingdom) developed a novel smell testing kit based on capsules of aromatic oils placed between two strips of single-sided tape.

This smelling test was made up of aromatic oil capsules that were prepared by a fabrication technique, which enabled full control over the capsule size, the shell thickness, and the volume of the encapsulated oil. The technique generated capsules by concentrically dripping oil/alginate droplets from a coaxial nozzle into an oppositely charged ionic liquid. After formation, liquid capsules were left to dry and form a solid crust surrounding the oil.

The prototype test used in the current study consisted of placing a standardized number of capsules between adhesive strips that users crushed and pulled apart to release the smell. In addition, a simple mathematical model was developed to predict the volume of encapsulated oil within the capsule in terms of the flow rate ratio and the nozzle size.

In this preliminary study, a small group of eight patients with Parkinson's disease were instructed to crush the capsules between their fingers and then peel back the tape strip to release the aroma contained within the capsules.

The participants reported that the smells from the tests were detectable and remarked on the relative ease of rupturing the capsules, particularly for those with tremors, compared to the standard scratch and sniff smell test available on the market.

First author Dr. Ahmed Ismail, a lecturer of fluid dynamics at Queen Mary University of London, said, "Most of the smell tests on the market depend on using paperboard items treated with a fragrant coating called scratch and sniff, in which you need to scratch a card to release the odor. The problem with this approach is that the amount of odor released depends on the extent to which the individual scratches, something that might affect the outcome of the test. Our capsule-based smell test does not have this problem because the amount of odor released is controlled by the amount of oil precisely encapsulated. The mass-production of our new test would also be cheaper than a scratch and sniff test.

Dr. Ismail said, "Our capsule-based smell test can assist in the rapid diagnostic of various diseases linked to the loss of smell. These include chronic neurological conditions such as Parkinson's and Alzheimer's disease, as well as COVID-19, which is known to affect the sense of smell. Being non-invasive and less stressful, the capsule-based smell test has benefits over the nose swab in diagnosing COVID-19. This is an advantage for testing children in particular, as they are typically horrified if they need to do a nose swab, and the test can be done in the comfort of their own home."

The capsule-based smell test was described in the April 28, 2021, online edition of the journal Royal Society Interface.

Related Links:
Queen Mary University of London

Gold Member
SARS‑CoV‑2/Flu A/Flu B/RSV Sample-To-Answer Test
SARS‑CoV‑2/Flu A/Flu B/RSV Cartridge (CE-IVD)
Verification Panels for Assay Development & QC
Seroconversion Panels
New
Epstein-Barr Virus Test
Mononucleosis Rapid Test
New
Total Thyroxine Assay
Total Thyroxine CLIA Kit

Print article

Channels

Clinical Chemistry

view channel
Image: The GlycoLocate platform uses multi-omics and advanced computational biology algorithms to diagnose early-stage cancers (Photo courtesy of AOA Dx)

AI-Powered Blood Test Accurately Detects Ovarian Cancer

Ovarian cancer ranks as the fifth leading cause of cancer-related deaths in women, largely due to late-stage diagnoses. Although over 90% of women exhibit symptoms in Stage I, only 20% are diagnosed in... Read more

Immunology

view channel
Image: The cancer stem cell test can accurately choose more effective treatments (Photo courtesy of University of Cincinnati)

Stem Cell Test Predicts Treatment Outcome for Patients with Platinum-Resistant Ovarian Cancer

Epithelial ovarian cancer frequently responds to chemotherapy initially, but eventually, the tumor develops resistance to the therapy, leading to regrowth. This resistance is partially due to the activation... Read more

Technology

view channel
Image: The new algorithms can help predict which patients have undiagnosed cancer (Photo courtesy of Adobe Stock)

Advanced Predictive Algorithms Identify Patients Having Undiagnosed Cancer

Two newly developed advanced predictive algorithms leverage a person’s health conditions and basic blood test results to accurately predict the likelihood of having an undiagnosed cancer, including ch... Read more

Industry

view channel
Image: The collaboration aims to leverage Oxford Nanopore\'s sequencing platform and Cepheid\'s GeneXpert system to advance the field of sequencing for infectious diseases (Photo courtesy of Cepheid)

Cepheid and Oxford Nanopore Technologies Partner on Advancing Automated Sequencing-Based Solutions

Cepheid (Sunnyvale, CA, USA), a leading molecular diagnostics company, and Oxford Nanopore Technologies (Oxford, UK), the company behind a new generation of sequencing-based molecular analysis technologies,... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.