Research

�� Boulder Chemical, Biological Engineering Professor Heinz named Amazon Scholar

Anonymous — Tue, 23 Aug 2022 18:23:19 +0000

�� Boulder Chemical, Biological Engineering Professor Heinz named Amazon Scholar Anonymous (not verified) Tue, 08/23/2022 - 12:23

Professor Hendrik Heinz is the second �� Boulder faculty member chosen for the highly selective Amazon Scholar program.

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Spike protein mapping could lead to more effective COVID-19 vaccine boosters and therapies

Anonymous — Thu, 25 Mar 2021 15:38:11 +0000

Spike protein mapping could lead to more effective COVID-19 vaccine boosters and therapies Anonymous (not verified) Thu, 03/25/2021 - 09:38

Jonathan Raab

This research is in pre-print online:

As millions of people around the world receive vaccines to halt the spread of COVID-19, mutated variants of the virus continue to appear, challenging the efficacy of mass vaccination programs and social distancing.

New research from the Sprenger and Whitehead groups aims to identify and map common mutations in “Spike” proteins—the proteins that allow the virus to enter and infect cells. This would provide researchers with a roadmap to anticipate and counteract the development of future SARS-CoV-2 strains with effective vaccines and vaccine boosters.

The collaborative research combined the Sprenger group’s expertise in computational methods to study how antibodies interact with viral proteins with the unique technological capabilities of the Whitehead group.

“We identified common Spike mutations for certain antibodies that are elicited during natural infection from the virus,” said Associate Professor Timothy Whitehead. “These mutations may emerge in lineages after population vaccination, and a prospective knowledge of these mutations may allow us to develop better vaccine boosters and therapies against SARS-CoV-2.”

The researchers utilized a genetically engineered strain of yeast, which expressed portions of the viral Spike proteins along its surface. They created mutant variations of the Spike proteins and studied their ability to go unrecognized by antibodies—essentially modeling the potential mutations of SARS-CoV-2.

Irene Francino Urdaniz

"Molecular simulations can provide unique insight into the mechanisms by which the identified Spike mutations allow SARS-CoV-2 to escape pressure by the immune system,” said Assistant Professor Kayla Sprenger. “We observed common escape mechanisms from multiple neutralizing antibodies with the same germline gene origins, which may have important implications for future SARS-CoV-2 immunotherapeutics."

Whitehead credits one of his graduate students, Irene Francino Urdaniz, with leading the effort in his lab.

“When the pandemic started, we saw the opportunity to apply techniques mastered by the Whitehead lab to make a contribution,” Francino Urdaniz said.

“We set up a system to test how well antibodies neutralize SARS-CoV-2 by displaying the S RBD on the yeast cell’s surface. Early in the process, we had the extraordinary opportunity to collaborate with Brandon Dekosky’s lab and the Vaccine Research Center to characterize a newly discovered neutralizing antibody with our platform.”

“Francino Urdaniz developed the genetically engineered yeast strain and discovered how to screen for mutations on the Spike protein that result in loss of antibody efficacy,” Whitehead said. “She is a Balsells fellow and represents the fantastic students we are able to recruit from the best universities in Europe.”

Other institutions involved in the work include University of Kansas, The Scripps Research Institute, the International AIDS Vaccine Initiative, Columbia University and the NIH Vaccine Research Center.

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Growing a better, more affordable solar cell from perovskite

Anonymous — Tue, 02 Mar 2021 16:36:28 +0000

Growing a better, more affordable solar cell from perovskite Anonymous (not verified) Tue, 03/02/2021 - 09:36

Jonathan Raab

Cross-sectional SEM image of the spin-coated MAPbI₃ film processed from DMF precursor solution (annealed for 5 s at 100 °C) on a PTAA-covered ITO glass substrate.

While solar panels have traditionally used silicon-based cells, researchers are increasingly looking to perovskite-based solar cells to create panels that are more efficient, less expensive to produce and can be manufactured at the scale needed to power the world.

Perovskite materials have properties that indicate they may be well-suited for energy applications like batteries and solar cells. They are synthetically “grown” in films for such applications. One of the fundamental questions related to their production, however, is whether they can be grown from the top down or bottom up. Each has significant impacts on how the films function.

In “Crystallization in One-step Solution Deposition of Perovskite Films: Upward or Downward?” published in Science Advances in January, Professor Michael F. Toney and his research partners describe a process of top-down growth, leading to a broader understanding of how to produce cells that are more efficient and stable while being less expensive than traditional silicon-based cells.

“The potential exists for the perovskite family to supplant silicon as the primary material involved in solar energy production,” Toney said. “We’re attempting to understand how to make good, quality films for perovskite solar cells. But it was unclear whether starting at the top and going down or starting at the bottom and going up was the best method.”

Toney credits his collaborator on this project Professor Jinsong Huang of the University of North Carolina at Chapel Hill as one of the key researchers behind this effort. This collaboration was made possible from the Department of Energy-funded Center for Hybrid Organic Inorganic Semiconductors for Energy (CHOISE).

Perovskite solar cells are on the way to commercialization after tremendous demonstration of excellent efficiencies and stabilities, Huang said.

“One critical question to be answered next is whether the lab-scale, nail-sized cells can be scaled up to one-to-two-square-meter modules while still keeping their efficiency and stability,” he said. “To answer this, the critical step to take is to understand how perovskite films are grown so that its uniformity can be well controlled, which results in better module efficiency and stability.”

Huang believes this research will help engineers refine the process to increase efficiency and drive down costs of perovskite modules.

Toney’s interest in the subject began about a decade ago, over lunch with �� Boulder Professor Michael McGehee. McGehee convinced Toney to investigate the perovskite class of materials as a possible replacement for silicon in solar cell production.

Toney describes the study of compound metal halide perovskites as being in its infancy, as opposed to silicon, which has been under study and in use for decades.

“For metal halide perovskite, the properties are quite different from silicon, so at a high level we’re trying to understand, what are these properties?” Toney said. “We’re learning more about the constituent atoms and molecules that control those properties. We’re asking: how do we tune the properties to create something that is useful for society?”

This research was completed in collaboration with researchers at �� Boulder, the National Renewable Energy Laboratory, the Department of Energy’s CHOISE program and at Stanford University.

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Velcro-like cellular proteins key to tissue strength

Anonymous — Mon, 01 Mar 2021 23:27:24 +0000

Velcro-like cellular proteins key to tissue strength Anonymous (not verified) Mon, 03/01/2021 - 16:27

Kelsey Simpkins

Where do bodily tissues get their strength? New �� Boulder research provides important new clues to this long-standing mystery, identifying how specialized proteins called cadherins join forces to make cells stick—and stay stuck—together.

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Machine learning technology may help doctors identify and treat infections in newborns faster

Anonymous — Wed, 06 Nov 2019 16:00:00 +0000

Machine learning technology may help doctors identify and treat infections in newborns faster Anonymous (not verified) Wed, 11/06/2019 - 09:00

Jonathan Raab

Machine learning technology evaluates millions of images of blood samples to detect and identify pathogens.

New research using machine learning technology may help doctors identify pathogens in blood samples in a fraction of the time of current methods, leading to faster deployments of life-saving treatments in patients suffering from sepsis, especially newborns.

While researching injectable therapeutic preparations, Gillespie Professor Theodore Randolph, Assistant Professor Adjunct Christopher Calderon and graduate student Austin Daniels developed a machine learning-based technique for identifying and classifying types of particles found in their formulations. They soon realized that they could use this same analysis method to detect and identify invasive bacteria within blood samples.

In cases of sepsis — particularly in premature births — neonatologists often have to do a combination of guesswork and analysis to determine the nature of the illness. It is critically important to identify the right treatment for the right infection, or more harm can come from deploying the wrong antibiotics than from the disease itself.

“If doctors think there’s a blood infection, then they put the infant on antibiotics,” Randolph said. “Because they don’t always know exactly what kind of organism it is, they are basically guessing. If the infant is actually not infected and they are given antibiotics, that’s bad. The potential toxic effects of any antibiotic use are not good.”

After delivery, a newborn’s blood is sampled and analyzed for microbial infection. Currently, that analysis takes two days. If an infection is present, it can be another day or more before current analysis methods can identify the bacteria.

“Waiting three days to get the right treatment for the right infection may be too long,” Randolph said. “Unfortunately, it is a frequent occurrence that the kids die before the bacteria is identified. That’s the motivating factor: can we speed things up to save lives?”

“The combination of microscopy, machine learning and microfluidics enables us to find a needle in a haystack, where the haystack represents the large number of particles that can be resolved with an optical microscope — red and white blood cells, platelets, et cetera — in a blood sample,” Calderon said. “But we don’t just find the infection — we can also identify the bug in the blood sample.”

Reseachers Daniels, Calderon and Randolph.

This machine learning-based technique is similar to facial recognition technology, but it works at the microscopic level. Using a FlowCam Nano microscopy instrument provided by research sponsors Fluid Imaging Technologies Inc., blood samples are sent through microfluidic channels under a microscope and photographs are taken of objects that are larger than one micron in size. This results in over a million photos taken of a relatively small blood sample. These images are then reviewed by the system, which can identify specific microbial organisms in a fraction of the time of traditional tests.

“We are designing the system so we only need a single drop of blood, which is advantageous, especially for premature infants,” Randolph said. “Twenty minutes after the sample is analyzed by the machine, the doctor gets an answer as to which antibiotic to use.”

To ensure their platform has the right impact for medical professionals, the team is working with James L. Wynn, Professor of Pediatrics, Pathology, Immunology, and Experimental Medicine at the University of Florida Medical School. Wynn is serving as a consultant for the project, helping to make the platform’s algorithm clinically relevant, including providing a prioritized list of bacteria for identification and conducting medical research literature reviews.

“A fast approach that can be deployed at a variety of hospitals worldwide for detecting and determining the root cause of sepsis from blood samples would address many issues facing sepsis detection and diagnosis,” Wynn said. “The implementation of this platform should have a major impact on antimicrobial treatment in all areas of the hospital.”

The team is preparing to test this technique with human blood samples in collaboration with physicians working in the field and anticipates two to three more years of FDA review before tests can begin on patients.

Randolph credits his brother David, a ChBE department graduate and medical doctor, with sparking the team’s conversations about the potential applications of their technology. David Randolph currently practices as a neonatologist.

New research adapting facial recognition technology may help identify and treat pathogens in minutes rather than days.

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Research

���������� Boulder Chemical, Biological Engineering Professor Heinz named Amazon Scholar

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Spike protein mapping could lead to more effective COVID-19 vaccine boosters and therapies

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Growing a better, more affordable solar cell from perovskite

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Velcro-like cellular proteins key to tissue strength

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Machine learning technology may help doctors identify and treat infections in newborns faster

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�� Boulder Chemical, Biological Engineering Professor Heinz named Amazon Scholar