Tyler O'Neal, Staff Editor BIG DATA

Danish researchers develop algo to find possible misdiagnosis

It does not happen often. But on rare occasions, physicians make mistakes and may make a wrong diagnosis. Patients may have many diseases all at once, where it can be difficult to distinguish the symptoms of one illness from the other, or there may be a lack of symptoms.

Errors in diagnosis may lead to incorrect treatment or a lack of treatment. Therefore, everyone in the healthcare system tries to minimize errors as much as possible.

Now, researchers at the University of Copenhagen have developed an algorithm that can help with just that.

'Our new algorithm can find the patients who have such an unusual disease trajectory that they may indeed not suffer from the disease they were diagnosed with. It can hopefully end up being a support tool for physicians', says Isabella Friis Jørgensen, Postdoc at the Novo Nordisk Foundation Center for Protein Research. Professor Søren Brunak hopes that the new algorithm could become a support tool for physicians (photo: Peter Hove Olesen){module INSIDE STORY}

The algorithm revealed possible lung cancer

The researchers have developed the algorithm based on disease trajectories for 284,000 patients with chronic obstructive pulmonary disease (COPD), from 1994 to 2015. Based on these data, they came up with approximately 69,000 typical disease trajectories.

"In the National Patient Registry, we have been able to map what you could call typical disease trajectory. And if a patient shows up with a very unusual disease trajectory, then it might be that the patient is simply suffering from a different disease. Our tool can help to detect this," explains Søren Brunak, Professor at the Novo Nordisk Foundation Center for Protein Research.

For example, the researchers found a small group of 2,185 COPD patients who died very shortly after being diagnosed with COPD. According to the researchers, it was a sign that something else might have been wrong, maybe something even more serious.

"When we studied the laboratory values from these patients more closely, we saw that they deviated from normal values for COPD patients. Instead, the values resembled something that is seen in lung cancer patients. Only 10 percent of these patients were diagnosed with lung cancer, but we are reasonably convinced that most, if not all of these patients actually had lung cancer," explains Søren Brunak.

Data that will provide an immediate benefit

Although the algorithm was validated through data from COPD patients, it may be used for many other diseases. The principle is the same: the algorithm uses registry data to map the typical disease trajectories and can detect if some patients' disease trajectories stand out so much that something may be wrong.

"Naturally, our most important goal is for the patients to get their money's worth with respect to their health care. And we believe that in the future, this algorithm may end up becoming a support tool for physicians. Once the algorithm has mapped the typical disease trajectories, it only takes 10 seconds to match a single patient against everyone else," says Søren Brunak.

He emphasizes that the algorithm must be further validated and tested in clinical trials before it can be implemented in Danish hospitals. But he hopes it is something that can be started soon.

 

"In Denmark, we often praise our good health registries because they contain valuable data for researchers. We use them in our research because it may benefit other people in the future in the form of better treatment. But this is actually an example of how your own health data can benefit yourself right away," says Søren Brunak.

Read more about the researchers' new results in the scientific journal NPJ Digital Medicine: ‘Time-ordered comorbidity correlations identify patients at risk of mis- and overdiagnosis’.

KIST produces simulations for the high-efficiency production of Hydrogen peroxide

Tyler O'Neal, Staff Editor BIG DATA

Supercomputer simulation-based catalyst development for hydrogen peroxide production with the selectivity of 95%. Development of the platinum-gold alloy catalyst facilitating hydrogen peroxide direct synthesis from hydrogen and oxygen at room temperature and atmo

Hydrogen peroxide is used as a disinfectant, after dilution in water, to treat wounds. It is widely used across the industry as an eco-friendly oxidizing agent for impurity removal from semiconductors, waste treatment, etc. Currently, it is mainly produced by the sequential hydrogenation and oxidation of anthraquinone (AQ). However, this process is not only energy-intensive and requires large-scale facilities, but AQ is also toxic.

As an alternative to the AQ process, hydrogen peroxide direct synthesis from hydrogen (H2) and oxygen (O2) using a palladium (Pd) catalyst were proposed. However, the commercialization of the technology has been challenging because the amount of water (H2O) formed is more than hydrogen peroxide (H2O2) during the process.

The Korea Institute of Science and Technology (KIST) announced that a joint research team of Dr. Sang Soo Han and Dr. Donghun Kim (Computational Science Research Center), Dr. Seung Yong Lee (Materials Architecture Research Center), and Professor Kwan-Young Lee at Korea University (Korea University, President Jin Taek Chung) developed a platinum-gold alloy catalyst for hydrogen peroxide production based on a computer simulation. Hydrogen peroxide selectivity can be increased to 95% by using this catalyst, compared with only 30-40% for a palladium catalyst, which indicates that mostly hydrogen peroxide on the developed Pt-Au catalyst can be produced with a small amount of water. Dr. Sang Soo Han, Computational Science Research Center, KIST{module INSIDE STORY}

The joint research team between KIST and Korea University developed a new type of Pt-Au alloyed nanoparticle catalyst. Although it is difficult to homogeneously mix Pt and Au to develop an alloyed catalyst due to the intrinsic immiscibility of the metals, the researchers could successfully synthesize nanoparticles in the form of alloys by forcibly reducing precursors of Pt and Au. Also, using this method, the content of each metal particle could be controlled by adjusting the number of precursors of Pt and Au.

Hydrogen peroxide can be produced anywhere without large equipment by simply injecting both hydrogen gas and oxygen gas into an aqueous solution using the catalyst developed by the researchers. Unlike the Pd catalyst, the catalyst developed by the joint researchers can produce hydrogen peroxide up to 95% even at ambient temperature (10 C) and atmospheric pressure (1 atm). In addition, a catalytic reaction can be maintained for longer than 8 h, resulting from the structural stability of the catalyst.

The researchers clearly established the crystal structure of Pt-Au alloy nanoparticles by performing additional supercomputer simulations, which is difficult to solve using general material analysis techniques. Furthermore, the catalytic reaction mechanism via supercomputer simulations was proposed at the atomic level in which the reason why the catalytic performance for hydrogen peroxide production is increased with increasing Au content was also clarified.

Sang-Soo Han, Head of the Center at KIST, said, "it is important that the developed catalysts provide an eco-friendly hydrogen peroxide production option that can be applied without any limitation of manufacturing sites. Therefore, commercialization for the hydrogen peroxide direct synthesis would be greatly accelerated by overcoming the limitation of Pd catalysts with the low selectivity" and "the time and cost for the development of novel catalysts, mainly explored through trial and error, could be considerably reduced through computer simulations." 

The research results were published in the latest issue of the academic journal 'Acta Materialia' in the field of materials science.

Pitt team develops cheap, potent pathway to pandemic therapeutics

Tyler O'Neal, Staff Editor BIG DATA

By capitalizing on a convergence of chemical, biological, and artificial intelligence advances, the University of Pittsburgh School of Medicine scientists have developed an unusually fast and efficient method for discovering tiny antibody fragments with big potential for development into therapeutics against deadly diseases. 

The technique, published today in the journal Cell Systems, is the same process the Pitt team used to extract tiny SARS-CoV-2 antibody fragments from llamas, which could become an inhalable COVID-19 treatment for humans. This approach has the potential to quickly identify multiple potent nanobodies that target different parts of a pathogen—thwarting variants.

Yi Shi, Ph.D., assistant professor of cell biology, University of Pittsburgh.“Most of the vaccines and treatments against SARS-CoV-2 target the spike protein, but if that part of the virus mutates, which we know it is, those vaccines and treatments may be less effective,” said senior author Yi Shi, Ph.D., assistant professor of cell biology at Pitt. “Our approach is an efficient way to develop therapeutic cocktails consisting of multiple nanobodies that can launch a multipronged attack to neutralize the pathogen.” {module INSIDE STORY} 

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Shi and his team specialize in finding nanobodies—which are small, highly specific fragments of antibodies produced by llamas and other camelids. Nanobodies are particularly attractive for development into therapeutics because they are easy to produce and bioengineer. In addition, they feature high stability and solubility, and can be aerosolized and inhaled, rather than administered through intravenous infusions, like traditional antibodies. 

By immunizing a llama with a piece of a pathogen, the animal’s immune system produces a plethora of mature nanobodies in about two months. Then it’s a matter of teasing out which nanobodies are best at neutralizing the pathogen—and most promising for development into therapies for humans. 

That’s where Shi’s “high-throughput proteomics strategy” comes into play. 

“Using this new technique, in a matter of days we’re typically able to identify tens of thousands of distinct, highly potent nanobodies from the immunized llama serum and survey them for certain characteristics, such as where they bind to the pathogen,” Shi said. “Prior to this approach, it has been extremely challenging to identify high-affinity nanobodies.”

After drawing a llama blood sample rich in mature nanobodies, the researchers isolate those nanobodies that bind specifically to the target of interest on the pathogen. The nanobodies are then broken down to release small “fingerprint” peptides that are unique to each nanobody. These fingerprint peptides are placed into a mass spectrometer, which is a machine that measures their mass. By knowing their mass, the scientists can figure out their amino acid sequence—the protein building blocks that determine the nanobody’s structure. Then, from the amino acids, the researchers can work backward to DNA—the directions for building more nanobodies. 

Simultaneously, the amino acid sequence is uploaded to a supercomputer outfitted with artificial intelligence software. By rapidly sifting through mountains of data, the program “learns” which nanobodies bind the tightest to the pathogen and where on the pathogen they bind. In the case of most of the currently available COVID-19 therapeutics, this is the spike protein, but recently it has become clear that some sites on the spike are prone to mutations that change its shape and allow for antibody “escape.” Shi’s approach can select for binding sites on the spike that are evolutionarily stable, and therefore less likely to allow new variants to slip past.

Finally, the directions for building the most potent and diverse nanobodies can then be fed into vats of bacterial cells, which act as mini-factories, churning out orders of magnitude more nanobodies compared to the human cells required to produce traditional antibodies. Bacterial cells double in 10 minutes, effectively doubling the nanobodies with them, whereas human cells take 24 hours to do the same.

“This drastically reduces the cost of producing these therapeutics,” said Shi. 

Shi and his team believe their technology could be beneficial for more than just developing therapeutics against COVID-19—or even the next pandemic. 

Yi Shi, Ph.D., operates his mass spectrometry machine to analyze 10's of millions of nanobodies pulled from llama blood.MassSpecYiShi release“The possible uses of highly potent and specific nanobodies that can be identified quickly and inexpensively are tremendous,” said Shi. “We’re exploring their use in treating cancer and neurodegenerative diseases. Our technique could even be used in personalized medicine, developing specific treatments for mutated superbugs for which every other antibiotic has failed.”

Additional researchers on this publication are Yufei Xiang and Jianquan Xu, Ph.D., both of Pitt; Zhe Sang of Pitt and Carnegie Mellon University; and Lirane Bitton and Dina Schneidman-Duhovny, Ph.D., both of the Hebrew University of Jerusalem.

This research was supported by the UPMC Aging Institute, National Institutes of Health grant 1R35GM137905-01, Israel Science Foundation grant 1466/18, the Ministry of Science and Technology of Israel, and the Hebrew University of Jerusalem Center for Interdisciplinary Data Science Research.