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Wellbeing

New nanosensor to check body’s inflammatory level, diagnose disease in 30 minutes

Currently used techniques for cytokine detection include enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR), which although reliable are highly time-consuming.

IANS

JODHPUR: Researchers at the Indian Institute of Technology (IIT) Jodhpur have developed a novel nanosensor that targets cytokines -- proteins that help control the body’s inflammation levels -- and helps in the rapid diagnosis and progression of various diseases in 30 minutes.

Currently used techniques for cytokine detection include enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR), which although reliable are highly time-consuming. These also require trained personnel and a long sample preparation or analysis time that can take over 6 hours to produce the results.

However, the new sensor takes only 30 minutes in comparison and is also cost-effective, for developing therapeutics for conditions like Multiple Sclerosis, Diabetes, and Alzheimer's disease, among others, said the team. They added that the technique could also detect “trace-level molecules with high precision and selectivity.”

“It uses Surface Enhanced Raman Spectroscopy to detect analytes even at low concentrations, and is based on semiconductor process technology and works on the principle of Surface Enhanced Raman Scattering (SERS),” the Institute said.

“This technique which is currently in its development stage has provided exciting and encouraging results for three biomarkers i.e. interleukin-6 (IL-6), interleukin-beta (IL-beta), and TNF-alpha which are key pro-inflammatory cytokines, released by inflammatory cells,” said Prof. Ajay Agarwal, Department of Electrical Engineering, IIT Jodhpur.

“As of now, the testing is done for controlled samples, but the team aims to take the technology to clinical trials soon. The group is also using this technique to develop detection protocols for the early-stage and quick diagnosis of Sepsis and Fungal infections,” he added.

The findings have been published in the 2023 IEEE Applied Sensing Conference (APSCON).

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