Engineers improve electrochemical sensing by incorporating machine learning

Aida Ebrahimi, Thomas and Sheila Roell Early Career Assistant Professor of Electrical Engineering and assistant professor of biomedical engineering, and Vinay Kammarchedu, 2022-23 Milton and Albertha Langdon Memorial Graduate Fellowship in Electrical Engineering, developed a new approach to improve the performance of electrochemical biosensors by combining machine learning with multimodal measurement.
Photo Credit: Kate Myers | Pennsylvania State University

Combining machine learning with multimodal electrochemical sensing can significantly improve the analytical performance of biosensors, according to new findings from a Penn State research team. These improvements may benefit noninvasive health monitoring, such as testing that involves saliva or sweat. The findings were published this month in Analytica Chimica Acta.

The researchers developed a novel analytical platform that enabled them to selectively measure multiple biomolecules using a single sensor, saving space and reducing complexity as compared to the usual route of using multi-sensor systems. In particular, they showed that their sensor can simultaneously detect small quantities of uric acid and tyrosine — two important biomarkers associated with kidney and cardiovascular diseases, diabetes, metabolic disorders, and neuropsychiatric and eating disorders — in sweat and saliva, making the developed method suitable for personalized health monitoring and intervention.

Many biomarkers have similar molecular structures or overlapping electrochemical signatures, making it difficult to detect them simultaneously. Leveraging machine learning for measuring multiple biomarkers can improve the accuracy and reliability of diagnostics and as a result improve patient outcomes, according to the researchers. Further, sensing using the same device saves resources and biological sample volumes needed for tests, which is critical with clinical samples with scarce amounts.

Study sheds new light on the link between oral bacteria and diseases

Photo Credit: Quang Tri NGUYEN

Researchers at Karolinska Institutet have identified the bacteria most commonly found in severe oral infections. Few such studies have been done before, and the team now hopes that the study can provide deeper insight into the association between oral bacteria and other diseases. The study is published in Microbiology Spectrum.

Researchers at Karolinska Institutet have now analyzed samples collected between 2010 and 2020 at the Karolinska University Hospital in Sweden from patients with severe oral infections and produced a list of the most common bacteria.

This was a collaborative study that was performed by Professor Margaret Sällberg Chen and adjunct Professor Volkan Özenci’s research groups.

“We’re reporting here, for the first time, the microbial composition of bacterial infections from samples collected over a ten-year period in Stockholm County,” says Professor Sällberg Chen of the Department of Dental Medicine at Karolinska Institutet. “The results show that several bacterial infections with link to systemic diseases are constantly present and some have even increased over the past decade in Stockholm.”

A warmer Arctic Ocean leads to more snowfall further south

An increasingly warm and ice-free Arctic Ocean has, in recent decades, led to more moisture in higher latitudes. This moisture is transported south by cyclonic weather systems where it precipitates as snow, influencing the global hydrological cycle and many terrestrial systems that depend on it
Illustration Credit: Tomonori Sato

A new model explains that water evaporating from the Arctic Ocean due to a warming climate is transported south and can lead to increased snowfall in northern Eurasia in late autumn and early winter. This information will allow for more accurate predictions of severe weather events.

Rising air temperatures due to global warming melt glaciers and polar ice caps. Seemingly paradoxically, snow cover in some areas in northern Eurasia has increased over the past decades. However, snow is a form of water; global warming increases the quantity of moisture in the atmosphere, and thus the quantity and likelihood of rain and snow. Understanding where exactly the moisture comes from, how it is produced and how it is transported south is relevant for better predictions of extreme weather and the evolution of the climate.

SARS-CoV-2 detection in 30 minutes using gene scissors

Multiplex chip of a Freiburg research team: On this chip, the viral load in the nasal swab and, if necessary, the antibiotic concentration in the blood of COVID-19 patients could be measured simultaneously.
Photo Credit: AG Disposable Microsystems/University of Freiburg

Researchers of the University of Freiburg introduce biosensor for the nucleic acid amplification-free detection of SARS-CoV-2 RNA

CRISPR-Cas is versatile: Besides the controversial genetically modified organisms (GMOs), created through gene editing, various new scientific studies use different orthologues of the effector protein ‘Cas’ to detect nucleic acids such as DNA or RNA.

In its most recent study, the research group headed by microsystems engineer Dr. Can Dincer of the Department of Microsystems2 Engineering, University of Freiburg introduces a microfluidic multiplexed chip for the simultaneous measurement of the viral load in nasal swabs and (if applicable) the blood antibiotic levels of COVID-19 patients.

Rapid test or PCR?

The market launch of rapid antigen test kits has significantly changed the way in which society handles the effects of the pandemic: Individuals suspecting an infection with SARS-CoV-2 can now test themselves at home with kits that are readily available at most drug stores, pharmacies and supermarkets, instead of making an, oftentimes difficult to acquire, appointment for PCR testing, that requires 1 to 3 additional days to receive the result. This convenience is, however, paid for with test sensitivity. This issue became flagrantly apparent during the wave of infections last winter, when the ‘lateral flow devices’ frequently failed to detect infections with the Omicron-variant until after the onset of symptoms. “The trade-off between sensitivity and sample-to-result time could potentially be bridged using our method,” says Midori Johnston, first author of the study, that is now being published in the journal Materials Today.

Pocket feature shared by deadly coronaviruses could lead to pan-coronavirus antiviral treatment

Spike glycoprotein structure of SARS-CoV, the coronavirus causing the 2002 outbreak. When linoleic acid is bound, the structure is locked in a non-infectious form. The cryo-EM density, calculated by cloud computing, is shown (left) along with the protein structure (middle). Linoleic acid molecules are colored orange. A zoom-in of the pocket (boxed), conserved in all deadly coronaviruses, is shown
 Illustration Credit: Christiane Schaffitzel and Christine Toelzer, University of Bristol

Scientists have discovered why some coronaviruses are more likely to cause severe disease, which has remained a mystery, until now. Researchers of the University of Bristol-led study, published in Science Advances today [23 November], say their findings could lead to the development of a pan-coronavirus treatment to defeat all coronaviruses—from the 2002 SARS-CoV outbreak to Omicron, the current variant of SARS-CoV-2, as well as dangerous variants that may emerge in future.

In this new study, an international team, led by Bristol's Professor Christiane Schaffitzel, scrutinized the spike glycoproteins decorating all coronaviruses. They reveal that a tailor-made pocket feature in the SARS-CoV-2 spike protein, first discovered in 2020, is present in all deadly coronaviruses, including MERS and Omicron. In striking contrast, the pocket feature is not present in coronaviruses which cause mild infection with cold-like symptoms.

The team say their findings suggest that the pocket, which binds a small molecule, linoleic acid—an essential fatty acid indispensable for many cellular functions including inflammation and maintaining cell membranes in the lungs so that we can breathe properly—could now be exploited to treat all deadly coronaviruses, at the same time rendering them vulnerable to a linoleic acid-based treatment targeting this pocket.

Spin correlation between paired electrons demonstrated

Electrons leave a superconductor only as pairs with opposite spins. If both electron paths are blocked for the same type of spin by parallel spin filters, paired electrons from the superconductor are blocked and the currents decrease.
Image Credit: University of Basel, Department of Physics/Scixel

Physicists at the University of Basel have experimentally demonstrated for the first time that there is a negative correlation between the two spins of an entangled pair of electrons from a superconductor. For their study, the researchers used spin filters made of nanomagnets and quantum dots, as they report in the scientific journal Nature.

The entanglement between two particles is among those phenomena in quantum physics that are hard to reconcile with everyday experiences. If entangled, certain properties of the two particles are closely linked, even when far apart. Albert Einstein described entanglement as a “spooky action at a distance”. Research on entanglement between light particles (photons) was awarded this year's Nobel Prize in Physics.

Two electrons can be entangled as well – for example in their spins. In a superconductor, the electrons form so-called Cooper pairs responsible for the lossless electrical currents and in which the individual spins are entangled.

For several years, researchers at the Swiss Nanoscience Institute and the Department of Physics at the University of Basel have been able to extract electron pairs from a superconductor and spatially separate the two electrons. This is achieved by means of two quantum dots – nanoelectronic structures connected in parallel, each of which only allows single electrons to pass.

A Radical New Approach in Synthetic Chemistry

The Laser Electron Accelerator Facility (LEAF) generates intense high-energy electron pulses that allow scientists to add or subtract electrons from molecules to make chemically reactive species and monitor what happens as a reaction proceeds.
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Photo Credit: Courtesy of Brookhaven National Laboratory

Scientists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory helped measure how unpaired electrons in atoms at one end of a molecule can drive chemical reactivity on the molecule’s opposite side. As described in a paper recently published in the Journal of the American Chemical Society, this work, done in collaboration with Princeton University, shows how molecules containing these so-called free radicals could be used in a whole new class of reactions.

“Most reactions involving free radicals take place at the site of the unpaired electron,” explained Brookhaven Lab chemist Matthew Bird, one of the co-corresponding authors on the paper. The Princeton team had become experts in using free radicals for a range of synthetic applications, such as polymer upcycling. But they’ve wondered whether free radicals might influence reactivity on other parts of the molecule as well, by pulling electrons away from those more distant locations.

“Our measurements show that these radicals can exert powerful ‘electron-withdrawing’ effects that make other parts of the molecule more reactive,” Bird said.

The Princeton team demonstrated how that long-distance pull can overcome energy barriers and bring together otherwise unreactive molecules, potentially leading to a new approach to organic molecule synthesis.

Genome studies uncover a new branch in fungal evolution

In a class of their own: The earth tongue is one of 600 “oddball” fungi that were found to share a common ancestor dating back 300 million years, according to U of A researchers.
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Photo Credit: Alan Rockefeller, CC-BY-SA-4.0

About 600 seemingly disparate fungi that never quite found a fit along the fungal family tree have been shown to have a common ancestor, according to a University of Alberta-led research team that used genome sequencing to give these peculiar creatures their own classification home.

“They don't have any particular feature that you can see with the naked eye where you can say they belong to the same group. But when you go to the genome, suddenly this emerges,” says Toby Spribille, principal investigator on the project and associate professor in the Department of Biological Sciences.

“I like to think of these as the platypus and echidna of the fungal world.”

Spribille, Canada Research Chair in Symbiosis, is referring to Australia’s famed Linnaean classification system-defying monotremes — which produce milk and have nipples, but lay eggs — that were the source of debate as to whether they were even real.

Major discovery about mammalian brains surprises researchers

Illustration shows vacuolar-type adenosine triphosphatases (V-ATPases, large blue structures) on a synaptic vesicle from a nerve cell in the mammalian brain.
Illustration Image: C. Kutzner, H. Grubmüller and R. Jahn/Max Planck Institute for Multidisciplinary Sciences.

Major discovery about mammalian brains surprises researchers, University of Copenhagen researchers have made an incredible discovery. Namely, a vital enzyme that enables brain signals is switching on/off at random, even taking hours-long “breaks from work”. These findings may have a major impact on our understanding of the brain and the development of pharmaceuticals. 

Millions of neurons are constantly messaging each other to shape thoughts and memories and let us move our bodies at will. When two neurons meet to exchange a message, neurotransmitters are transported from one neuron to another with the aid of a unique enzyme.

This process is crucial for neuronal communication and the survival of all complex organisms. Until now, researchers worldwide thought that these enzymes were active at all times to convey essential signals continuously. But this is far from the case.

Using a groundbreaking method, researchers from the University of Copenhagen’s Department of Chemistry have closely studied the enzyme and discovered that its activity is switching on and off at random intervals, which contradicts our previous understanding.

Machine learning gives nuanced view of Alzheimer’s stages

A Cornell-led collaboration used machine learning to pinpoint the most accurate means, and timelines, for anticipating the advancement of Alzheimer’s disease in people who are either cognitively normal or experiencing mild cognitive impairment.

The modeling showed that predicting the future decline into dementia for individuals with mild cognitive impairment is easier and more accurate than it is for cognitively normal, or asymptomatic, individuals. At the same time, the researchers found that the predictions for cognitively normal subjects are less accurate for longer time horizons, but for individuals with mild cognitive impairment, the opposite is true.

The modeling also demonstrated that magnetic resonance imaging (MRI) is a useful prognostic tool for people in both stages, whereas tools that track molecular biomarkers, such as positron emission tomography (PET) scans, are more useful for people experiencing mild cognitive impairment.

The team’s paper, “Machine Learning Based Multi-Modal Prediction of Future Decline Toward Alzheimer’s Disease: An Empirical Study,” published in PLOS ONE. The lead author is Batuhan Karaman, a doctoral student in the field of electrical and computer engineering.