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Because of its outstanding transparency as well as its stability in contact with heat or chemicals, glass is relevant for many high-tech applications. However, conventional processes for shaping glass are often tedious, energy-intensive and quickly reach their limits for small and complicated components. The Freiburg materials scientists Dr. Frederik Kotz-Helmer and Prof. Dr. Bastian E. Rapp, in cooperation with the University of California at Berkeley in the US, have developed a novel process that can be used to produce very small components from transparent glass quickly and precisely using micro 3D printing. Possible applications include components for sensors and microscopes, but also for lab-on-a-chip systems. The researchers were able to publish their results in the current issue of the journal Science.
Glass powder in plastic binder
The new technology is based on so-called Glassomer materials, which Kotz-Helmer and Rapp developed at the Department of Microsystems Engineering (IMTEK) at the University of Freiburg. “Glassomer materials consist of glass powder in a special plastic binder,” says Kotz-Helmer, “allowing to process glass like a plastic.” The resulting components are then placed in a furnace, which causes the plastic to burn and the glass to be sintered, i.e. densified. “In the end, the components consist of one hundred percent highly transparent fused silica glass,” says Kotz-Helmer.
Component is created in a single step
The Freiburg scientists have now combined Glassomer materials with a new 3D printing process developed by a research team led by Prof. Dr. Hayden Taylor from the University of California, Berkeley. Conventional 3D printers print their objects layer by layer. However, in the new process, called Computed Axial Lithography (CAL), the component is created in a single step. A vessel containing liquid, light-sensitive material is exposed to two-dimensional light images of the object to be printed from many different angles. Where the images overlap and the amount of light absorbed thus locally exceeds a certain threshold, the material hardens abruptly — within a few minutes, the component is formed. The excess, still liquid material can be washed off.
Structures with the thickness of a single hair
“In principle, this process also works with Glassomer material,” says Kotz-Helmer. For this purpose, the Freiburg scientists developed a material made of glass powder and plastic that is both highly transparent and hardens quickly at a suitable threshold value. “The devil was in the chemical details here,” says the materials scientist. Previously, moreover, the CAL process had only been suitable for relatively coarse structures. By combining the materials science expertise at the University of Freiburg and the project partner Glassomer GmbH, a Freiburg spin-off, as well as the further development of the system technology at the University of California, it has now been possible to combine and improve these technologies. “For the first time, we were able to print glass with structures in the range of 50 micrometers in just a few minutes, which corresponds roughly to the thickness of a hair,” says Kotz-Helmer. “In addition, the surfaces of the components are smoother than with conventional 3D printing processes.”
Glass as a substitute for vulnerable plastic
Kotz-Helmer sees possible applications for the innovative manufacturing process, for example, in micro-optical components of sensors, virtual reality headsets and modern microscopes: “The ability to manufacture such components at high speed and with great geometric freedom will enable new functions and more cost-effective products in the future.”
Microfluidic channels are also needed for so-called lab-on-a-chip systems for research and medical diagnostics. Until now, these have mostly been made of plastics, but they often cannot withstand high temperatures and aggressive chemicals. Thanks to the new process technology, complex channel systems can now be manufactured in glass, says Kotz-Helmer: “Thanks to the thermal and chemical stability of glass, many new fields of application are opening up, especially in the area of chemistry on-a-chip synthesis.”
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If you become ill with COVID-19, your smartwatch can track the progression of your symptoms, and could even show how sick you become.
That’s according to a University of Michigan study that examined the effects of COVID-19 with six factors derived from heart rate data. The same method could be used to detect other diseases such as influenza, and the researchers say the approach could be used to track disease at home or when medical resources are scarce, such as during a pandemic or in developing countries. Their results are published in the journal Cell Reports Medicine.
Following U-M students and medical interns throughout the country, the researchers discovered new signals embedded in heart rate indicating when individuals were infected with COVID and how sick they became. The researchers found that individuals with COVID experienced an increase in heart rate per step after symptom onset, and those with a cough had a much higher heart rate per step than those without a cough.
“We found that COVID dampened biological timekeeping signals, changed how your heart rate responds to activity, altered basal heart rate and caused stress signals,” said Daniel Forger, professor of mathematics and research professor of computational medicine and bioinformatics. “What we realized was knowledge of physiology, how the body works and mathematics can help us get more information from these wearables.”
The researchers found that these measures were significantly altered and could show symptomatic vs. healthy periods in the wearers’ lives.
“There’s been some previous work on understanding disease through wearable heart rate data, but I think we really take a different approach by focusing on decomposing the heart rate signal into multiple different components to take a multidimensional view of heart rate,” said Caleb Mayer, a doctoral student in mathematics. More

A scientific team supported by the National Institutes of Health has created a tiny, bioengineered 3-D model that mimics the biology of chronic inflammatory demyelinating polyneuropathy and multifocal motor neuropathy, a pair of rare, devastating neuromuscular diseases. The researchers used the organ-on-a-chip, or “tissue chip,” model to show how a drug could potentially treat the diseases. They provided key preclinical data for a drug company to submit to the U.S. Food and Drug Administration to get authorization for testing in a clinical trial.
This work provides one of the first examples of scientists using primarily tissue chip data for an FDA Investigational New Drug application to test the efficacy of a candidate drug in people with rare diseases. The drug company Sanofi started recruiting participants into a Phase 2 clinical trial in April 2021. The drug was tested for safety previously and approved by the FDA for a different indication.
The tissue chip research was led by Hesperos, Inc., an Orlando-based company partially funded by a Small Business Innovation Research grant from NIH’s National Center for Advancing Translational Sciences (NCATS). This study could open the door to studying and developing new therapies for other rare diseases by establishing a new avenue for repurposing existing drugs for rare diseases. Most of the known 7,000 rare diseases do not have effective treatments. Researchers often lack animal models for studying rare disease biology and testing potential drugs.
“This marks an important milestone in the evolution of the use of tissue chips,” said Lucie Low, Ph.D., scientific program manager for the NCATS Tissue Chip for Drug Screening initiative. “We know that pharmaceutical companies are using tissue chips internally. Submitting data to regulatory agencies generated from tissue chip platforms is a powerful indicator of their growing promise.”
James Hickman, Ph.D., chief scientist at Hesperos, and his colleagues described the development of the model and their research results in Advanced Therapeutics. In these diseases, the immune system makes proteins called antibodies that damage nerve cells and slow down messages moving from the brain to the muscles. This can make it hard for people to move their arms, hands and legs. Current treatments can help, but often are inconsistent.
The researchers developed a tissue chip model consisting of two cell types: motoneurons and Schwann cells. Motoneurons transmit messages from the brain to muscles. Schwann cells help the signals move more quickly. The model could mimic functional characteristics of the diseases, allowing the scientists to see how a drug was working by determining whether the brain’s messages to muscles were slowing down or not.
The researchers showed that exposing the cells to blood serum from people with these rare diseases caused a shower of immune system antibodies against the cells. This made the motoneuron signals move more slowly. After treatment with TNT005, a drug that blocks the immune system reaction, the cells and the message speed returned to normal.
“We’re confident that our system can reproduce what happens to a patient, including the disease symptoms and disease progression,” said Hickman. “It’s important to create functionally relevant patient models that will mimic what is seen in clinical trials.”
Approximately 90% of promising therapies fail in clinical trials because animal models used in preclinical testing are not good at predicting how people will respond. To improve that success rate and help get more treatments to people who have few options, scientists are exploring the uses of tissue chips. Designed to support living human tissues and cells, tissue chips mimic the structure and function of human organs and systems, such as the lungs, heart and liver. Researchers are studying their uses in many areas, including for testing the safety and effectiveness of candidate drugs and modeling diseases.
The potential clinical uses of tissue chip data are growing. Recently, an NIH-supported research team at Harvard University’s Wyss Institute reported using a tissue chip model to generate data on the effectiveness of a repurposed drug for treating lung damage from COVID-19 infection. In the NCATS-funded Clinical Trials on a Chip program, several projects examine how tissue chip data can help researchers design more useful clinical trials. This might include using such data to predict which patients in a trial are most likely to respond to a therapy.
“Creating a platform that can predict human responses to a drug in a rare disease could lead to exciting new opportunities in research,” said Low. “If tissue chip data can be generated that inform the decisions made before early human trials, this could reduce the risks to vulnerable populations.”
Funding for this research was provided by True North Therapeutics (now Sanofi), NCATS (SBIR 2R44TR001326-03) and internal Hesperos development funds. More

Georgia State University researchers have successfully designed a new type of artificial vision device that incorporates a novel vertical stacking architecture and allows for greater depth of color recognition and scalability on a micro-level. The new research is published in the top journal ACS Nano.
“This work is the first step toward our final destination-to develop a micro-scale camera for microrobots,” says assistant professor of Physics Sidong Lei, who led the research. “We illustrate the fundamental principle and feasibility to construct this new type of image sensor with emphasis on miniaturization.”
Lei’s team was able to lay the groundwork for the biomimetic artificial vision device, which uses synthetic methods to mimic biochemical processes, using nanotechnology.
“It is well-known that more than 80 percent of the information is captured by vision in research, industry, medication, and our daily life,” he says. “The ultimate purpose of our research is to develop a micro-scale camera for microrobots that can enter narrow spaces that are intangible by current means, and open up new horizons in medical diagnosis, environmental study, manufacturing, archaeology, and more.”
This biomimetic “electric eye” advances color recognition, the most critical vision function, which is missed in the current research due to the difficulty of downscaling the prevailing color sensing devices. Conventional color sensors typically adopt a lateral color sensing channel layout and consume a large amount of physical space and offer less accurate color detection.
Researchers developed the unique stacking technique which offers a novel approach to the hardware design. He says the van der Waals semiconductor-empowered vertical color sensing structure offers precise color recognition capability which can simplify the design of the optical lens system for the downscaling of the artificial vision systems. More

Superconductors are materials that conduct electrical current with practically no electrical resistance at all. This ability makes them extremely interesting and attractive for a plethora of applications such as loss-less power cables, electric motors and generators, as well as powerful electromagnets that can be used for MRI imaging and for magnetic levitating trains. Now, researchers from Nagoya University have detailed the superconducting nature of a new class of superconducting material, magic-angle twisted bilayer graphene.
For a material to behave as a superconductor, low temperatures are required. Most materials only enter the superconducting phase at extremely low temperatures, such as -270°C, lower than those measured in outer space! This severely limits their practical applications because such extensive cooling requires very expensive and specialized liquid helium cooling equipment. This is the main reason superconducting technologies are still in their infancy. High temperature superconductors (HTS), such as some iron and copper-based ones, enter the superconducting phase above -200°C, a temperature that is more readily achievable using liquid nitrogen which cools down a system to ?195.8°C. However, the industrial and commercial applications of HTS have been thus far limited. Currently known and available HTS materials are brittle ceramic materials that are not malleable into useful shapes like wires. In addition, they are notoriously difficult and expensive to manufacture. This makes the search for new superconducting materials critical, and a strong focus of research for physicists like Prof. Hiroshi Kontani and Dr. Seiichiro Onari from the Department of Physics, Nagoya University.
Recently, a new material has been proposed as a potential superconductor called magic-angle twisted bilayer graphene (MATBG). In MATBG, two layers of graphene, essentially single two-dimensional layers of carbon arranged in a honeycomb lattice, are offset by a magic angle (about 1.1 degrees) that leads to the breakage of rotational symmetry and the formation of a high-order symmetry known as SU(4). As temperature changes, the system experiences quantum fluctuations, like water ripples in the atomic structure, that lead to a novel spontaneous change in the electronic structure and a reduction in symmetry. This rotational symmetry breaking is known as the nematic state and has been closely associated with superconducting properties in other materials.
In their work published recently in Physical Review Letters, Prof. Kontani and Dr. Onari use theoretical methods to better understand and shine light on the source of this nematic state in MATBG. “Since we know that high temperature superconductivity can be induced by nematic fluctuations in strongly correlated electron systems such as iron-based superconductors, clarifying the mechanism and origin of this nematic order can lead to the design and emergence of higher temperature superconductors,” explains Dr. Onari.
The researchers found that nematic order in MATBG originates from the interference between the fluctuations of a novel degree-of-freedom that combines the valley degrees of freedom and the spin degrees of freedom, something that has not been reported from conventional strongly correlated electron systems. The superconducting transition temperature of twisted bilayer graphene is very low, at 1K (-272°C), but the nematic state manages to increase it by several degrees. Their results also show that although MATBG behaves in some ways like an iron-based high temperature superconductor, it also has some distinct properties that are quite exciting, such as a net charge loop current giving rise to a magnetic field in a valley polarized state, while the loop current is canceled out by each valley in the nematic state. Besides, the malleability of graphene can also play an important role in increasing the practical applications of these superconductors. With a better understanding of the underlying mechanisms of superconductivity, science and technology inch closer to a conducting future that is indeed super.
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Imagine a future in which you could 3D-print an entire robot or stretchy, electronic medical device with the press of a button — no tedious hours spent assembling parts by hand.
That possibility may be closer than ever thanks to a recent advancement in 3D-printing technology led by engineers at the University of Colorado Boulder. In a new study, the team lays out a strategy for using currently-available printers to create materials that meld solid and liquid components — a tricky feat if you don’t want your robot to collapse.
“I think there’s a future where we could, for example, fabricate a complete system like a robot using this process,” said Robert MacCurdy, senior author of the study and assistant professor in the Paul M. Rady Department of Mechanical Engineering.
MacCurdy, along with doctoral students Brandon Hayes and Travis Hainsworth, published their results April 14 in the journal Additive Manufacturing.
3D printers have long been the province of hobbyists and researchers working in labs. They’re pretty good at making plastic dinosaurs or individual parts for machines, such as gears or joints. But MacCurdy believes that they can do a lot more: By mixing solids and liquids, 3D printers could churn out devices that are more flexible, dynamic and potentially more useful. They include wearable electronic devices with wires made of liquid contained within solid substrates, or even models that mimic the squishiness of real human organs.
The engineer compares the advancement to traditional printers that print in color, not just black-and-white. More

Artificial intelligence reduced by twofold the rate at which precancerous polyps were missed in colorectal cancer screening, reported a team of international researchers led by Mayo Clinic. The study is published in Gastroenterology.
Most colon polyps are harmless, but some over time develop into colon or rectal cancer, which can be fatal if found in its later stages. Colorectal cancer is the second most deadly cancer in the world, with an estimated 1.9 million cases and 916,000 deaths worldwide in 2020, according to the World Health Organization. A colonoscopy is an exam used to detect changes or abnormalities in the large intestine (colon) and rectum.
Between February 2020 and May 2021, 230 study participants each underwent two back-to-back colonoscopies on the same day at eight hospitals and community clinics in the U.S., U.K. and Italy. One colonoscopy used AI; the other, a standard colonoscopy, did not.
The rate at which precancerous colorectal polyps is missed has been estimated to be 25%. In this study, the miss rate was 15.5% in the group that had the AI colonoscopy first. The miss rate was 32.4 % in the group that had standard colonoscopy first. The AI colonoscopy detected more polyps that were smaller, flatter and in the proximal and distal colon.
“Colorectal cancer is almost entirely preventable with proper screening,” says senior author Michael B. Wallace, M.D., division chair of gastroenterology and hepatology at Sheikh Shakhbout Medical City in Abu Dhabi, United Arab Emirates and the Fred C. Andersen Professor of Medicine at Mayo Clinic in Jacksonville, Fla. “Using artificial intelligence to detect colon polyps and potentially save lives is welcome and promising news for patients and their families.”
In addition, false negative rates were 6.8% in the group that had the AI colonoscopy first. It was 29.6% in the group that had standard colonoscopy first. A false-negative result indicates that you do not have a particular condition, when in fact you do.
The study’s senior author and principal investigator is Michael B. Wallace, M.D., of Sheikh Shakhbout Medical City in Abu Dhabi, UAE and Mayo Clinic in Jacksonville, Fla. Co-authors include Cesare Hassan, M.D., Ph.D, of Nuovo Regina Margherita Hospital in Rome, Italy; James East, M.D., of John Radcliffe Hospital in Oxford, U.K., and Mayo Clinic Healthcare in London; Frank Lukens, M.D., of Mayo Clinic in Jacksonville, Fla.; Genci Babameto, M.D., of Mayo Clinic Health System in La Crosse, Wis.; Daisy Batista, M.D., of Mayo Clinic Health System in La Crosse, Wis.; Davinder Singh, M.D., of Mayo Clinic Health System in La Crosse, Wis.; William Palmer, M.D. of Mayo Clinic in Jacksonville, Fla.; Francisco C. Ramirez, M.D., of Mayo Clinic in Scottsdale, Ariz.; Tisha Lunsford, M.D., of Mayo Clinic in Scottsdale, Ariz.; Kevin Ruff, M.D., of Mayo Clinic in Scottsdale, Ariz.; David Cangemi, M.D., of Mayo Clinic in Jacksonville, Fla.; Gregory Derfus, M.D., of Mayo Clinic Health System in Eau Claire, Wis. Victor Ciofoaia, M.D., another co-author, was affiliated with Mayo during the study, but has since left Mayo.
Cosmo Artificial Intelligence-AI Ltd. funded the study.
Dr. Wallace has financial interests in Verily, Cosmo Pharmaceuticals, Fujifilm, Olympus and Virgo.
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Materials provided by Mayo Clinic. Original written by Rhoda Madson. Note: Content may be edited for style and length. More

The brain is arguably the single most important organ in the human body. It controls how we move, react, think and feel, and enables us to have complex emotions and memories. The brain is composed of approximately 86 billion neurons that form a complex network. These neurons receive, process, and transfer information using chemical and electrical signals.
Learning how neurons respond to different signals can further the understanding of cognition and development and improve the management of disorders of the brain. But experimentally studying neuronal networks is a complex and occasionally invasive procedure. Mathematical models provide a non-invasive means to accomplish the task of understanding neuronal networks, but most current models are either too computationally intensive, or they cannot adequately simulate the different types of complex neuronal responses. In a recent study, published in Nonlinear Theory and Its Applications, IEICE, a research team led by Prof. Tohru Ikeguchi of Tokyo University of Science, has analyzed some of the complex responses of neurons in a computationally simple neuron model, the Izhikevich neuron model. “My laboratory is engaged in research on neuroscience and this study analyzes the basic mathematical properties of a neuron model. While we analyzed a single neuron model in this study, this model is often used in computational neuroscience, and not all of its properties have been clarified. Our study fills that gap,” explains Prof. Ikeguchi. The research team also comprised Mr. Yota Tsukamoto and PhD student Ms. Honami Tsushima, also from Tokyo University of Science.
The responses of a neuron to a sinusoidal input (a signal shaped like a sine wave, which oscillates smoothly and periodically) have been clarified experimentally. These responses can be either periodic, quasi-periodic, or chaotic. Previous work on the Izhikevich neuron model has demonstrated that it can simulate the periodic responses of neurons. “In this work, we analyzed the dynamical behavior of the Izhikevich neuron model in response to a sinusoidal signal and found that it exhibited not only periodic responses, but non-periodic responses as well,” explains Prof. Ikeguchi.
The research team then quantitatively analyzed how many different types of ‘inter-spike intervals’ there were in the dataset and then used it to distinguish between periodic and non-periodic responses. When a neuron receives a sufficient amount of stimulus, it emits ‘spikes,’ thereby conducting a signal to the next neuron. The inter-spike interval refers to the interval time between two consecutive spikes.
They found that neurons provided periodic responses to signals that had larger amplitudes than a certain threshold value and that signals below this value induced non-periodic responses. They also analyzed the response of the Izhikevich neuron model in detail using a technique called ‘stroboscopic observation points,’ which helped them identify that the non-periodic responses of the Izhikevich neuron model were actually quasi-periodic responses.
When asked about the future implications of this study, Prof. Ikeguchi says, “This study was limited to the model of a single neuron. In the future, we will prepare many such models and combine them to clarify how a neural network works. We will also prepare two types of neurons, excitatory and inhibitory neurons, and use them to mimic the actual brain, which will help us understand principles of information processing in our brain.”
The use of a simple model for accurate simulations of neuronal response is a significant step forward in this exciting field of research and illuminates the way towards the future understanding of cognitive and developmental disorders.
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