Wednesday, December 22, 2021

Machine-Learning and Mass Spectrometer at the Heart of UC Davis COVID-19 Rapid Test

UC Davis Health, in partnership with SpectraPass, is evaluating a new type of rapid COVID-19 test. The research will involve about 2,000 people in Sacramento and Las Vegas.

The idea behind the new platform is a scalable system that can quickly and accurately perform on-site tests for hundreds or potentially thousands of people.

Nam Tran is a professor of clinical pathology in the UC Davis School of Medicine and a co-developer of the novel testing platform with SpectraPass, a Las Vegas-based startup.

Tran explained that the system doesn’t look for the SARS-CoV-2 virus like a PCR test does. Instead, it detects an infection by analyzing the body’s response to it. When ill, the body produces differing protein profiles in response to infection. These profiles may indicate different types of infection, which can be detected by machine learning.

“The goal of this study is to have enough COVID-19 positive and negative individuals to train our machine learning algorithm to identify patients infected by SARS-CoV-2,” said Tran.

A study published by Tran and his colleagues earlier this year in Nature Scientific Reports found the novel method to be 98.3% accurate for positive COVID-19 tests and 96% for negative tests. 

In addition to identifying positive cases of COVID-19, the platform also uses next-generation sequencing to confirm multiple respiratory pathogens like the flu and the common cold.

The sequencing panel at UC Davis Health can detect over 280 respiratory pathogens, including SARS-CoV-2 and related variants — allowing the study to train the machine-learning algorithms to differentiate COVID-19 from other respiratory diseases.

So far, the study has not seen any participants with the new omicron variant.

“Our team has tested the system with samples from patients infected with delta and other variants of the SARS-CoV-2 virus. We are fairly certain that omicron will be detected as well, but we won’t know for sure until we encounter a study participant with the variant,” Tran said.

The Emergency Department (ED) at the UC Davis Medical Center is conducting the testing in Sacramento. Collection for testing in Las Vegas is conducted at multiple businesses and locations.

The team expects the study will continue until the end of winter. The results from the new study will be used to seek emergency use authorization (EUA) from the Food and Drug Administration.

Testing system builds on MILO

The novel testing system uses an analytical instrument known as a mass spectrometer. It’s paired with machine learning algorithms produced by software called the Machine Intelligence Learning Optimizer or MILO. MILO was developed by Tran, Hooman Rashidi, a professor in the Department of Pathology and Laboratory Medicine, and Samer Albahra, assistant professor and medical director of pathology artificial intelligence in the Department of Pathology and Laboratory Medicine.

As with many other COVID-19 tests, a nasal swab is used to collect a sample. Proteins from the nasal sample are ionized with the mass spectrometer’s laser, then measured and analyzed by the MILO machine learning algorithms to generate a positive or negative result.

In addition to conducting the mass spectrometry testing, UC Davis serves as a reference site for the study, performing droplet digital PCR (ddPCR) tests, the “gold standard” for COVID-19 testing, to assess the accuracy of the mass spectrometry tests.

University-industry partnership formed in 2020

The project originated with Maurice J. Gallagher, Jr., chairman and CEO of Allegiant Travel Company and founder of SpectraPass. Gallagher is also a UC Davis alumnus and a longtime supporter of innovation and entrepreneurship at UC Davis.  

In 2020, when the COVID-19 pandemic brought the travel and hospitality industries almost to a standstill, Gallagher began conceptualizing approaches to allow people to gather again safely. He teamed with researchers at UC Davis Health to develop the new platform and launched SpectraPass.

In addition to the novel testing solution, SpectraPass is also developing digital systems to accompany the testing technology. Those include tools to authenticate and track verified test results from the system so an individual can access and use them. The goal is to facilitate accurate, large-scale rapid testing that will help keep businesses and the economy open through the current and any future pandemics.

“The official start of our multi-center study across multiple locations marks an important milestone in our journey at SpectraPass. We are excited to test and generate data on a broader scale. Our goal is to move the platform from a promising new technology to a proven solution that can ultimately benefit the broader population,” said Greg Ourednik, president of SpectraPass.

Source: UC Davis Newsroom 

Thursday, December 02, 2021

SMART Researchers Develop Method for Early Detection of Bacterial Infection in Crops

Researchers from the Disruptive and Sustainable Technologies for Agricultural Precision (DiSTAP) Interdisciplinary Research Group (IRG) of Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, and their local collaborators from Temasek Life Sciences Laboratory (TLL), have developed a rapid Raman spectroscopy-based method for detecting and quantifying early bacterial infection in crops. The Raman spectral biomarkers and diagnostic algorithm enable the noninvasive and early diagnosis of bacterial infections in crop plants, which can be critical for the progress of plant disease management and agricultural productivity.

Due to the increasing demand for global food supply and security, there is a growing need to improve agricultural production systems and increase crop productivity. Globally, bacterial pathogen infection in crop plants is one of the major contributors to agricultural yield losses. Climate change also adds to the problem by accelerating the spread of plant diseases. Hence, developing methods for rapid and early detection of pathogen-infected crops is important to improve plant disease management and reduce crop loss.

The breakthrough by SMART and TLL researchers offers a faster and more accurate method to detect bacterial infection in crop plants at an earlier stage, as compared to existing techniques. The new results appear in a paper titled “Rapid detection and quantification of plant innate immunity response using Raman spectroscopy” published in the journal Frontiers in Plant Science.

“The early detection of pathogen-infected crop plants is a significant step to improve plant disease management,” says Chua Nam Hai, DiSTAP co-lead principal investigator, professor, TLL deputy chair, and co-corresponding author. “It will allow the fast and selective removal of pathogen load and curb the further spread of disease to other neighboring crops.”

Traditionally, plant disease diagnosis involves a simple visual inspection of plants for disease symptoms and severity. “Visual inspection methods are often ineffective, as disease symptoms usually manifest only at relatively later stages of infection, when the pathogen load is already high and reparative measures are limited. Hence, new methods are required for rapid and early detection of bacterial infection. The idea would be akin to having medical tests to identify human diseases at an early stage, instead of waiting for visual symptoms to show, so that early intervention or treatment can be applied,” says MIT Professor Rajeev Ram, who is a DiSTAP principal investigator and co-corresponding author on the paper.

While existing techniques, such as current molecular detection methods, can detect bacterial infection in plants, they are often limited in their use. Molecular detection methods largely depend on the availability of pathogen-specific gene sequences or antibodies to identify bacterial infection in crops; the implementation is also time-consuming and nonadaptable for on-site field application due to the high cost and bulky equipment required, making it impractical for use in agricultural farms.

“At DiSTAP, we have developed a quantitative Raman spectroscopy-based algorithm that can help farmers to identify bacterial infection rapidly. The developed diagnostic algorithm makes use of Raman spectral biomarkers and can be easily implemented in cloud-based computing and prediction platforms. It is more effective than existing techniques as it enables accurate identification and early detection of bacterial infection, both of which are crucial to saving crop plants that would otherwise be destroyed,” explains Gajendra Pratap Singh, scientific director and principal investigator at DiSTAP and co-lead author.

A portable Raman system can be used on farms and provides farmers with an accurate and simple yes-or-no response when used to test for the presence of bacterial infections in crops. The development of this rapid and noninvasive method could improve plant disease management and have a transformative impact on agricultural farms by efficiently reducing agricultural yield loss and increasing productivity.

“Using the diagnostic algorithm method, we experimented on several edible plants such as choy sum,” says DiSTAP and TLL principal investigator and co-corresponding author Rajani Sarojam. “The results showed that the Raman spectroscopy-based method can swiftly detect and quantify innate immunity response in plants infected with bacterial pathogens. We believe that this technology will be beneficial for agricultural farms to increase their productivity by reducing their yield loss due to plant diseases.”

The researchers are currently working on the development of high-throughput, custom-made portable or hand-held Raman spectrometers that will allow Raman spectral analysis to be quickly and easily performed on field-grown crops.

SMART and TLL developed and discovered the diagnostic algorithm and Raman spectral biomarkers. TLL also confirmed and validated the detection method through mutant plants. The research is carried out by SMART and supported by the National Research Foundation of Singapore under its Campus for Research Excellence And Technological Enterprise (CREATE) program.

SMART was established by MIT and the NRF in 2007. The first entity in CREATE developed by NRF, SMART serves as an intellectual and innovation hub for research interactions between MIT and Singapore, undertaking cutting-edge research projects in areas of interest to both Singapore and MIT. SMART currently comprises an Innovation Center and five IRGs: Antimicrobial Resistance, Critical Analytics for Manufacturing Personalized-Medicine, DiSTAP, Future Urban Mobility, and Low Energy Electronic Systems. SMART research is funded by the NRF under the CREATE program.

Led by Professor Michael Strano of MIT and Professor Chua Nam Hai of Temasek Lifesciences Laboratory, the DiSTAP program addresses deep problems in food production in Singapore and the world by developing a suite of impactful and novel analytical, genetic, and biomaterial technologies. The goal is to fundamentally change how plant biosynthetic pathways are discovered, monitored, engineered, and ultimately translated to meet the global demand for food and nutrients. Scientists from MIT, TTL, Nanyang Technological University, and National University of Singapore are collaboratively developing new tools for the continuous measurement of important plant metabolites and hormones for novel discovery, deeper understanding and control of plant biosynthetic pathways in ways not yet possible, especially in the context of green leafy vegetables; leveraging these new techniques to engineer plants with highly desirable properties for global food security, including high-yield density production, and drought and pathogen resistance; and applying these technologies to improve urban farming.

Source: MIT News 

Sunday, November 14, 2021

New Carbon Nanotube-Based Sensor Can Detect SARS-CoV-2 Proteins

As part of a sponsored research collaboration with InnoTech Precision Medicine, researchers from MIT have developed novel nanosensors for the detection of nucleocapsid and spike protein of the SARS-CoV-2 virus in an unprecedented short timeframe, within 10 days. This work was supported by a National Institute of Health, RADx-rad award to Dr. Roya Khosravi-Far, CEO and co-founder of InnoTech Precision Medicine and MIT research team, led by Dr. Michael Strano, the Carbon P. Dubbs Professor of Chemical Engineering.

Work published on October 26, 2021 in Analytical Chemistry describes the optimized and rapid work-flow for development of these innovative antibody-free sensors. Using single-walled carbon nanotubes and polymers, the team has developed Corona Phase Molecular Recognition (CoPhMoRe) sensors for SARS-CoV-2 proteins. These carbon nanotube sensors provide the platform for fast development of rapid and accurate diagnostic, monitoring and surveillance tests for detection of current pathogens as well as for our preparedness for detection of emergent pathogens.

A major drawback of current diagnostic technologies is long development time for antibody-based sensors, which is especially problematic in the case of a new emergent pathogen like COVID-19. “New technologies using innovative material and strategies are key for quick and efficient diagnosis and disease control. Conventional diagnostics are expensive, specialized, and slow to develop; we need to modernize our diagnostic tests to drive robust public health response to existing and emerging threats,” said Dr. Roya Khosravi-Far, Chief Executive Officer and co-founder of InnoTech Precision Medicine.

Reference:

Antibody-Free Rapid Detection of SARS-CoV-2 Proteins Using Corona Phase Molecular Recognition to Accelerate Development Time. Soo-Yeon Cho, Xiaojia Jin,  Xun Gong, Sungyun Yang, Jianqiao Cui, Michael S Strano. Anal Chem. 2021 Nov 9;93(44):14685-14693.  doi: 10.1021/acs.analchem.1c02889. Epub 2021 Oct 26.

LINK: https://pubmed.ncbi.nlm.nih.gov/34698489/

Abstract:

To develop better analytical approaches for future global pandemics, it is widely recognized that sensing materials are necessary that enable molecular recognition and sensor assay development on a much faster scale than currently possible. Previously developed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) point-of-care devices are based on the specific molecular recognition using subunit protein antibodies and protein receptors that selectively capture the viral proteins. However, these necessarily involve complex and lengthy development and processing times and are notoriously prone to a loss of biological activity upon sensor immobilization and device interfacing, potentially limiting their use in applications at scale. Here, we report a synthetic strategy for nanoparticle corona interfaces that enables the molecular recognition of SARS-CoV-2 proteins without any antibody and receptor design. Our nanosensor constructs consist of poly(ethylene glycol) (PEG)─phospholipid heteropolymers adsorbed onto near-infrared (nIR) fluorescent single-walled carbon nanotubes (SWCNTs) that recognize the nucleocapsid (N) and spike (S) protein of SARS-CoV-2 using unique three-dimensional (3D) nanosensor interfaces. This results in rapid and label-free nIR fluorescence detection. This antibody-free nanosensor shows up to 50% sensor responses within 5 min of viral protein injections with limit of detection (LOD) values of 48 fM and 350 pM for N and S proteins, respectively. Finally, we demonstrate instrumentation based on a fiber-optic platform that interfaces the advantages of antibody-free molecular recognition and biofluid compatibility in human saliva conditions.


Monday, October 11, 2021

HKBU Scientists Develop Barcode Cell Sensor

Research scientists at Hong Kong Baptist University (HKBU) have developed a cell sensor with barcode -like micro-channel structure that allows rapid and low-cost screening of drug-resistant bacteria.

The barcode cell sensor could potentially be used on a large-scale in resource-limited situations such as frequent safety screenings of water, food and public facilities, as well as urgent surveys of massive samples during an infectious disease outbreak, particularly in developing countries.

"Our barcode testing system is a promising new tool in the fight against antimicrobial resistance. We hope
that it will benefit the routine screening of drug-resistant bacteria in the food industry, public areas and healthcare facilities as it does not require advanced clinical facilities or professional testing skills," said Dr. Ren Kangning, associate professor of the Department of Chemistry at HKBU.

Dr. Ren led the research team that designed a fully automatic, microscope-free antimicrobial susceptibility testing (AST) system.  Apart from researchers from HKBU's Department of Chemistry, the research team of the "barcode" cell sensor also included scientists from the Department of Computer Science at HKBU and the School of Medicine at Stanford University.

The team has applied a patent for their invention.

Rapid yet low-cost approach to identifying drug-resistant bacteria

The  overuse and misuse of antibiotics have resulted to drug-resistant bacteria. AST is used to determine which antibiotics can effectively inhibit the growth of a certain type of bacteria effectively.

However, conventional AST methods are too slow, as they require 16 to 24 hours for results, while modern rapid ASTs are expensive and require elaborated laboratory equipment. A rapid and cost-effective strategy is therefore needed to screen bacterial samples onsite, with advanced laboratory testing arranged only for those suspected of containing drug-resistant bacteria.

The barcode cell sensor developed by HKBU enables rapid and low-cost screening of drug-resistant bacteria by scanning the "barcode" on the cell sensor with a mobile app. It is a fully automatic, microscope-free AST system comprising of  two main parts: a cell culture zone and a "barcode" cell sensor.

The cell culture zone consists of a set of micro-channels filled with fluids that contain cell culture media as well as different concentrations of the antibiotic. The "barcode" cell sensor contains an array of "adaptive linear filters" arranged in parallel that resembles a "barcode" structure.

Users can finish the onsite screening within three hours by scanning the "barcode" with a mobile app. Furthermore,  the barcode cell sensor has a  low production cost, estimated at under US$1 per piece.

“We plan to develop our invention into a portable AST instrument, and ultimately, we hope it can be used in resource-limited regions," said Dr. Ren.

How the barcode cell sensor works

When conducting AST with the system, bacterial samples will be injected into and incubated in the cell culture zone. Bacteria in the test sample inside the micro-channels show different proliferation rates depending on different concentrations of the antibiotic.

After completion of the culture period, the bacterial cells will flow through the "adaptive linear filters". The cells will not accumulate around the nanopores on the sidewalls of the micro-channels, instead they will be driven down by the fluid and be collected from the end of the micro-channels. The accumulated cells will then form visible vertical bars, the lengths of which are proportional to the quantity of bacteria cells cultured under the different concentrations of the antibiotic.

A cell phone equipped with a macro-lens can then be used to photograph the "barcode" created by the AST. The image will be analysed automatically by the mobile app.

After the culture period, if all the "bars" of the cell sensor have similar lengths, it means the tested antibiotic cannot inhibit the growth of the bacteria, and thus the bacterial sample is resistant to the tested antibiotic. If the length of the "bars" is in general inversely proportional to the concentration of the antibiotic in the micro-channels, it shows that the tested antibiotic is generally effective at prohibiting the growth of the bacteria, and thus the bacteria is not drug-resistant. When two adjacent "bars" show a sharp difference in terms of length, it indicates that the antimicrobial effect of the antibiotic leaps when its concentration reaches a particular level.

The HKBU  research team tested E. coli and S. aureus with the "barcode" cell sensor and the results were consistent with those of the conventional AST. The test can be completed in three hours, which is much faster than the conventional AST. Microfluidic approaches developed by other researchers can also attain comparable speed, but they rely on expensive instruments for analysis in general. 

Penn State Researchers Developing Genomic Resources to Identify Novel Pathogens

To enhance the early detection of novel infectious bacteria that could cause outbreaks of infectious disease and public health emergencies, a team of researchers in Penn State's College of Agricultural Sciences will sequence the genomes of 700 Bacilli bacteria — near relatives of the biothreat pathogen that causes anthrax.

Funded by a $1.2 million grant from the U.S. Centers for Disease Control and Prevention, the research will support the development of genomic resources and DNA sequence databases for the federal agency to increase its capacity for rapidly detecting novel pathogens, according to team leader Jasna Kovac, assistant professor of food science and Lester Earl and Veronica Casida Career Development Professor of Food Safety.

"You may have heard of the 2001 bioterrorist attacks in which spores of the bacteria Bacillus anthracis that cause anthrax were circulated in the mail," she said. "People who inhale these spores can get sick with anthrax, which is often fatal."

From a biodefense standpoint, it is important to understand the diversity of environmental Bacilli that could become novel biothreats such as anthrax, added Kovac, who has extensive experience with the genomics of Bacilli.

"There are known examples among Bacillus cereus group bacteria where 'benign' environmental strains have acquired anthrax-causing capabilities," she said. "We are interested in detecting and characterizing similar strains of Bacilli that have both the characteristics of known biothreats and harmless environmental microorganisms."

If emerging pathogens or biothreats are detected early on, they are more likely to be contained effectively to prevent a public health emergency, Kovac noted. "We are partnering with the CDC to create a large database of Bacilli to support its development of rapid laboratory methods for the detection of novel, naturally occurring or engineered pathogens and potential emerging biothreats," she said.

The databases will enhance and strengthen existing genomics approaches and bioinformatics pipelines developed by the CDC's Division of Preparedness and Emerging Infections group. This will allow for the rapid detection of genomic markers associated with increased biothreat risk, Kovac pointed out.

"We are uniquely positioned to complete the proposed work and support CDC's expansion of reference databases for the detection of novel, emerging infectious diseases," she said. "Here in Penn State's Department of Food Science, we have microbiology and genomic expertise and access to a large number of unique, environmental and food Bacilli, deposited in the Food Microbe Tracker culture collection and database curated by our collaborators at Cornell University."

Also on the research team are Xiaoyuan Wei, postdoctoral scholar; Taejung Chung, doctoral student; Jared Pavlock, research assistant; and Grant Harm, undergraduate research assistant.

AAD, BARDA Partner to Seek Earlier Detection of Sepsis

AAD, developer of rapid diagnostic and data systems, announced that it has been awarded a federal contract for the development of an innovative system for the earlier detection of severe infection, including sepsis. The easy-to-use system is designed for use at point of care in urgent care clinics, doctors' offices and other prehospital settings, and could result in critical earlier intervention and improved patient outcomes.

AAD's QScout® RLD+ system is being developed as an easy-to-use, rapid-result hematology analyzer to capture a 7-part leukocyte differential, including quantification of band neutrophils and other immature granulocytes (IG). In serious infections, bands are released after mature neutrophils are depleted, and then IGs are released. Having automated band counts and the simultaneous availability of IG counts would be a first in medicine and will enable earlier identification of infection, including sepsis.

The QScout RLD+ system is designed to deliver laboratory-grade results in two minutes, in almost any setting, compared to the approximately two hours required of a traditional manual hospital testing procedure. Each hour delayed for the onset of antibiotic treatment of septic shock can increase mortality nearly 8%.

"According to the CDC, the vast majority of sepsis cases — 87 percent — begin outside of a hospital," said Joy Parr Drach, CEO of AAD. "Having a test system that in about two minutes can give results patient-side that are typically only available in a hospital setting would provide critical information and allow faster intervention for the patient. This new test system represents AAD doing things not before possible in places not before possible."

The development of AAD's new system is being funded in part with federal funds from the Biomedical Advanced Research and Development Authority's (BARDA) Division of Research, Innovation and Ventures under contract number 75A50121C00089; BARDA is part of the U.S. Department of Health and Human Services' Office of the Assistant Secretary for Preparedness and Response.

According to the Centers for the Disease Prevention and Control (CDC), sepsis is a life-threatening medical emergency that happens when an infection a person already has triggers a chain reaction throughout the body. Infections that lead to sepsis most often start in the lung, urinary tract, skin, or gastrointestinal tract, although almost any infection can trigger sepsis, in which a localized infection progresses to severe infection throughout the body.

In a typical year, at least 1.7 million adults in the United States develop sepsis, and nearly 270,000 die as a result. A 2020 study estimated that sepsis caused approximately 11 million deaths worldwide in 2017 — or nearly 20 percent of all deaths in that year. Other studies show sepsis is the leading pediatric killer and leading cause of hospital deaths in the U.S.

About Advanced Animal Diagnostics

AAD (Advanced Animal Diagnostics) provides rapid point-of-care diagnostic and data systems for fast health care decisions. The company's QScout® line of rapid diagnostic tests empowers more precise care of animals and humans so they live healthier, more productive lives. Its diagnostic offerings inform real-time decisions that increase productivity, prevent losses, protect the food supply, and improve human and animal health well-being.

Sunday, August 29, 2021

New Device Can Diagnose COVID-19 From Saliva Samples

Engineers at MIT and Harvard University have designed a small tabletop device that can detect SARS-CoV-2 from a saliva sample in about an hour. In a new study, they showed that the diagnostic is just as accurate as the PCR tests now used.

The device can also be used to detect specific viral mutations linked to some of the SARS-CoV-2 variants that are now circulating. This result can also be obtained within an hour, potentially making it much easier to track different variants of the virus, especially in regions that don’t have access to genetic sequencing facilities.

“We demonstrated that our platform can be programmed to detect new variants that emerge, and that we could repurpose it quite quickly,” says James Collins, the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering. “In this study, we targeted the U.K., South African, and Brazilian variants, but you could readily adapt the diagnostic platform to address the Delta variant and other ones that are emerging.”

The new diagnostic, which relies on CRISPR technology, can be assembled for about $15, but those costs could come down significantly if the devices were produced at large scale, the researchers say.

Collins is the senior author of the new study, which appears in Science Advances. The paper’s lead authors are Helena de Puig, a postdoc at Harvard University’s Wyss Institute for Biologically Inspired Engineering; Rose Lee, an instructor in pediatrics at Boston Children’s Hospital and Beth Israel Deaconess Medical Center and a visiting fellow at the Wyss Institute; Devora Najjar, a graduate student in MIT’s Media Lab; and Xiao Tan, a clinical fellow at the Wyss Institute and an instructor in gastroenterology at Massachusetts General Hospital.

A self-contained diagnostic

The new diagnostic is based on SHERLOCK, a CRISPR-based tool that Collins and others first reported in 2017. Components of the system include an RNA guide strand that allows detection of specific target RNA sequences, and Cas enzymes that cleave those sequences and produce a fluorescent signal. All of these molecular components can be freeze-dried for long-term storage and reactivated upon exposure to water.

Last year, Collins’ lab began working on adapting this technology to detect the SARS-CoV-2 virus, hoping that they could design a diagnostic device that could yield rapid results and be operated with little or no expertise. They also wanted it to work with saliva samples, making it even easier for users.

To achieve that, the researchers had to incorporate a critical pre-processing step that disables enzymes called salivary nucleases, which destroy nucleic acids such as RNA. Once the sample goes into the device, the nucleases are inactivated by heat and two chemical reagents. Then, viral RNA is extracted and concentrated by passing the saliva through a membrane.

“That membrane was key to collecting the nucleic acids and concentrating them so that we can get the sensitivity that we are showing with this diagnostic,” Lee says.

This RNA sample is then exposed to freeze-dried CRISPR/Cas components, which are activated by automated puncturing of sealed water packets within the device. The one-pot reaction amplifies the RNA sample and then detects the target RNA sequence, if present.

“Our goal was to create an entirely self-contained diagnostic that requires no other equipment,” Tan says. “Essentially the patient spits into this device, and then you push down a plunger and you get an answer an hour later.”

The researchers designed the device, which they call minimally instrumented SHERLOCK (miSHERLOCK), so that it can have up to four modules that each look for a different target RNA sequence. The original module contains RNA guide strands that detect any strain of SARS-CoV-2. Other modules are specific to mutations associated with some of the variants that have arisen in the past year, including B.1.1.7, P.1, and B.1.351.

The Delta variant was not yet widespread when the researchers performed this study, but because the system is already built, they say it should be straightforward to design a new module to detect that variant. The system could also be easily programmed to monitor for new mutations that could make the virus more infectious.

“If you want to do more of a broad epidemiological survey, you can design assays before a mutation of concern appears in a population, to monitor for potentially dangerous mutations in the spike protein,” Najjar says.

Tracking variants

The researchers first tested their device with human saliva spiked with synthetic SARS-CoV-2 RNA sequences, and then with about 50 samples from patients who had tested positive for the virus. They found that the device was just as accurate as the gold standard PCR tests now used, which require nasal swabs and take more time and significantly more hardware and sample handling to yield results.

The device produces a fluorescent readout that can be seen with the naked eye, and the researchers also designed a smartphone app that can read the results and send them to public health departments for easier tracking.

The researchers believe their device could be produced at a cost as low as $2 to $3 per device. If approved by the FDA and manufactured at large scale, they envision that this kind of diagnostic could be useful either for people who want to be able to test at home, or in health care centers in areas without widespread access to PCR testing or genetic sequencing of SARS-CoV-2 variants.

“The ability to detect and track these variants is essential to effective public health, but unfortunately, variants are currently diagnosed only by nucleic acid sequencing at specialized epidemiological centers that are scarce even in resource-rich nations,” de Puig says.

Reference: de Puig H, Lee RA, Najjar D, et al. Minimally instrumented SHERLOCK (MiSHERLOCK) for CRISPR-based point-of-care diagnosis of SARS-CoV-2 and emerging variants. Sci Adv. 2021;7(32)

Biosensors Transform the Diagnosis of Infections in Newborns

Sepsis refers to a systemic (body-wide) infection accompanied by inflammation. Newborn infants are particularly susceptible to developing sepsis, given their naïve and under-developed immune systems. The infant immune system reacts to the acquired pathogen by releasing inflammatory factors such as cytokines and free radicals. The heightened immune response mounted against the pathogen, if uncontrolled, can cause severe damage to other organs, which can be fatal for the newborn. The prevalence of neonatal sepsis and associated mortality rates are especially high in developing countries, owing to poor sanitation and the dearth of healthcare resources.

Early diagnosis is thus cardinal for effective management of the infection and decreasing neonatal mortality. Current point-of-care (POC) methods rely on conventional blood culture and molecular techniques that may be time-consuming and often detect a single parameter or biomarker. Hence, development of rapid, sensitive, and integrated diagnostic strategies is crucial to enhance detection and improve the standard of care.

In a new Clinica Chimica Acta article, researchers from Shoolini University, in collaboration with researchers from IIT Hyderabad and Amity University, Rajasthan, India, have reviewed the latest advancements in analytical devices that enable multi-analyte detection with high sensitivity and accuracy. They also describe the limitations of currently used methods and why a combinatorial approach may be better. Speaking of why this caught their attention, lead author of the study, Dr. Anupam Jyoti, says, "Developing countries like India report an increased incidence of neonatal sepsis (50–70/1000 live births) as compared to developed countries (1–5/1000 live births), with a substantial mortality rate of 11–19%. We were thus motivated to review the field of neonatal sepsis detection and propose new directions towards effective diagnosis."

Routinely used blood culture techniques often require two to five days to yield results. Meanwhile, the infection escalates, and the newborn is often pumped with unnecessary antibiotics that can lead to anti-microbial resistance. Techniques such as the polymerase chain reaction, which detects the genetic material of the pathogen, and mass spectrometry, which detects pathogen specific proteins, are more sensitive and require less time. However, they can yield false positive results and do not differentiate between viable and non-viable pathogens in the sample. While tests that detect serum biomarkers and immune factors, expressed in response to infection, may give a broad idea about the presence of sepsis, they cannot differentiate between specific pathogens. Together, the methods may however complement each other for robust diagnosis of sepsis.

Biosensing analytical technologies have emerged as a powerful tool in biomedical devices. Advanced biosensors that promise multi-analyte detection in a single platform are now being increasingly developed for rapid and sensitive diagnosis. Electrochemical sensors can detect various electrolytes and biomarkers based on their specific electrical properties. Given the minute size, stability and high binding affinity of aptamers, or single-stranded nucleic acid probes, are useful for detecting bacterial traces in the blood. Next, sensors based on the surface plasmon resonance technique can detect changes in the optical properties of the sample. They are highly sensitive with low limits of detection, thus enabling the detection of small concentrations of pathogens. Finally, microfluidic devices and chip-based sensors analyze samples based on their flow or size and can thus detect bacterial and blood cells in the samples of patients with sepsis.

In addition to these methods, integrated approaches that combine the principles of multiple techniques on a single platform are gaining popularity. Such hybrid biosensors will be capable of detecting multiple parameters in a short time from small samples at the bedside of the patient. Moreover, their wide applicability, cost-effectiveness, small size, and need for limited resources make them a practical and valuable tool for the diagnosis of neonatal sepsis.

Overall, the review sheds light on modern technologies that can help strengthen, and possibly replace conventional POC approaches in the future. "Integrated POC-based diagnosis will help reduce detection time considerably and thus translate diagnosis from bench to the bedside. An efficient POC sepsis diagnostic platform could expand health care access and impact populations worldwide," says Dr. Jyoti.

Microwave Sensor for Rapid Antibiotic Sensitivity Testing

Researchers on the campus of the University of British Columbia Okanagan have developed a portable and economical microwave sensor that can quickly detect changes in bacterial growth to assess susceptibility to antibiotics. Using a split ring microwave resonator, the device can very significantly measure bacterial growth in the presence of different concentrations of antibiotic before there are visible changes in growth. The technology reduces the time and costs associated with these tests and could pave the way for personalized antibiotic therapy for regions with low or remote resources.

Antibiotics have revolutionized healthcare, allowing routine surgical procedures to continue without the excessive fear of devastating infections and ending a huge variety of nasty diseases that would previously have killed or disabled millions of people each year. . However, these advances are being eliminated slowly but surely by antibiotic resistance, which increases every year.

“Many types of bacteria are constantly evolving to develop antibiotic resistance. This is an urgent issue for hospitals around the world, while diagnostic and sensor technology has been slow to adapt, “Mohammad Zarifi, a researcher involved in the study, said in a press release.

The main problem is the inappropriate use of antibiotics and part of the solution is to choose the right antibiotic for each patient. After all, it is useless to prescribe an antibacterial agent for an infection caused by bacteria that are already resistant to that agent. This is where personalized antibiotic therapy comes in, which is to test a sample of disease-causing bacteria for a specific patient to determine their antibiotic susceptibility before prescribing a suitable antibiotic.

The problem is that this process is time consuming and expensive, often taking 48 hours, which is no joke if you have a serious infection. “Longer waiting times can significantly delay the treatments patients receive, which can lead to medical or even fatal complications. This method demonstrates the requirement for a reliable, fast and cost-effective screening tool,” he said. Zarifi.

This new technology is based on the detection of microwaves as a means to control the growth of the bacterial sample in the presence of different concentrations of antibiotic. The system is sensitive enough that it can detect differences in bacterial growth that are invisible to the human eye, and achieves this through a split-ring microwave resonator. The charged substances released by bacterial cells, when affected by antibiotics, can help with the measurement, but in essence, the resonant response of the split ring is affected by the growth of a bacterial sample on agar.

Ultimately, researchers hope to incorporate an element of artificial intelligence into the technology to help detect and predict personalized antibiotic treatment.

“Our ultimate goal is to reduce the inappropriate use of antibiotics and improve the quality of patient care,” Zarifi said. “The more quality tools health professionals have at their disposal, the greater their ability to fight bacteria and viruses.”