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May 11, 2018

Disease analysing tool improves diagnostic accuracy

A research team at the University at Buffalo (UB) in the US has developed a new protein analysis tool called IonStar which can enhance the speed and accuracy of disease diagnosis and the analysis of drug effects.

A research team at the University at Buffalo (UB) in the US has developed a new protein analysis tool called IonStar which can enhance the speed and accuracy of disease diagnosis and the analysis of drug effects.

The tool is designed to quantify and compare the abundance of proteins in patients with those of healthy people. This measurement is considered important to detect a disease or pharmaceutical reaction and for making new drugs, as it has the potential to reveal new biomarkers.

Existing protein analysis approaches are said to be time-consuming or lack accuracy, leading to the false identification of biomarkers.

“The researcher concluded that IonStar can improve the quality and precision of medical diagnosis and speed up pharmaceutical development.”

The new tool is said to address these issues and reduce missing data by improving on sample preparation techniques and alignment, while integrating designs for mass spectrometry analysis.

UB School of Pharmacy and Pharmaceutical Sciences lead investigator and professor Jun Qu said: “For example, in clinical trials, comparing a handful of patients gets you nowhere.

“If you can analyse a large number of patients with high-quality data, you can discover and track biomarkers much more accurately and reliably. The same is true for pharmaceutical investigations.”

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When compared to current standard MaxQuant, the tool demonstrated consistency in protein measurement and decreased the proportion of missing data in results from 17% to 0.1%.

Based on these results, the researcher concluded that IonStar can improve the quality and precision of medical diagnosis and speed up pharmaceutical development.

The tool was tested in rats with traumatic brain injury, where it detected 7,000 proteins from 100 tissue samples.

Furthermore, it was used to analyse protein variation in diabetes, cancer, cardiovascular disease, neuro and retinal degeneration. The researchers now plan to work on increasing the number of samples that IonStar can analyse.

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