Inexpensive and rapid testing of drugs for resistant infections possible


Date:
October 15, 2020
Source:
Penn State
Summary:
A rapid and simple method for testing the efficacy of antibacterial
drugs on infectious microbes has been developed and validated.



FULL STORY ==========================================================================
A rapid and simple method for testing the efficacy of antibacterial
drugs on infectious microbes has been developed and validated by a team
of Penn State researchers.


========================================================================== Antimicrobial resistant infection is one of the major threats to human
health globally, causing 2.5 million infections and 35,000 deaths
annually, with the potential to grow to 10 million deaths annually by
2050 without improved techniques for detection and treatment.

Several rapid testing techniques have been developed, but they do
not live up to the reliability of the gold standard technology, which
requires 18 to 24 hours for reliable results. In many cases, patients
need to be treated with antibiotics in a crisis, leading clinicians to prescribe broad-spectrum antibiotics that may actually lead to greater
drug resistance or unacceptable side effects.

"Compared to other methods of detection, our method does not require
complex systems and measurement setups," says Aida Ebrahimi, assistant professor of electrical engineering and a senior author on a paper
recently posted online in the journal ACS Sensors. "Its simplicity and
low cost are among the advantages and coupling our technology to machine learning makes the accuracy of our method comparable to the gold standard method and much better than other rapid methods." The team tested their
method against three strains of bacteria, including a resistant strain,
to prove its effectiveness in the lab. Upon further development and
validation with a broader range of pathogens and antibiotics, their
method can allow physicians to prescribe the minimum dosage of the
necessary drug, called the minimum inhibitory concentration (MIC) in a
timely fashion.

A phenomenon that other tests fail to account for is that bacteria may initially appear to be dead, but then can revive and multiply after
many hours.

The team's technology, augmented by machine learning, can predict whether
the bacteria will revive or are actually dead, which is critical for
accurate determination of the MIC value.

Their technique is called dynamic laser speckle imaging.

"The main advantages of our method are the speed and simplicity,"
explained Zhiwen Liu, professor of electrical engineering and the second corresponding author. You shine a laser beam on the sample and get all
of these light scattering speckles. We can then capture these images
and subject them to machine learning analysis. We capture a series of
images over time, which is the dynamic part. If the bacteria are alive,
you are going to get some motion, such as a small vibration or a little movement. You can get reliable, predictive results quickly, for example
within one hour." In addition to the immediate benefits provided to
the patient, the lower concentration of drugs entering the water supply translates to less pollution to the environment, he says.

"One of the exciting aspects of this research has been its
multidisciplinary nature. As an electrical engineer, I find it quite fascinating to work on designing and developing an optical diagnostic
system as well as performing microbiology assays," said Keren Zhou, the
co-lead first author on the paper and a doctoral student in electrical engineering.

His co-lead author, doctoral student Chen Zhou, added, "We plan to
further develop our technique to a low-cost and portable platform,
which would be especially beneficial for resource-limited settings."

========================================================================== Story Source: Materials provided by Penn_State. Original written by Walt
Mills. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Keren Zhou, Chen Zhou, Anjali Sapre, Jared Henry Pavlock, Ashley
Weaver,
Ritvik Muralidharan, Josh Noble, Taejung Chung, Jasna Kovac, Zhiwen
Liu, Aida Ebrahimi. Dynamic Laser Speckle Imaging Meets Machine
Learning to Enable Rapid Antibacterial Susceptibility Testing
(DyRAST). ACS Sensors, 2020; DOI: 10.1021/acssensors.0c01238 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/10/201015111733.htm

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