What’s not to love about a good flexible health sensor? Someday technology based on such bendable electronic tech might well replace some of those chunky wearables in the marketplace today with sleek, golden skin patches.
Now, a team at the UC San Diego Center for Wearable Sensors has created a stretchy skin patch that combines electrochemical sensors for alcohol, caffeine, glucose, and lactate with an ultrasound-based sensor that monitors blood pressure deep inside the body. Described in the journal Nature Biomedical Engineering, it’s the first wearable device that tracks heart signals and biochemical levels at the same time, the authors said.
“Conventionally, those two types of signals are monitored separately by different devices,” said study co-author Sheng Xu, a UCSD nanoengineer. “By bridging the gap between those two, we can get a more comprehensive view of what’s going on in the human body.”
3D printing living tissue—including corneas, blood vessels and skin—is no easy task. But at least it’s all living tissue. Bone, by contrast, is a mixture of living and inorganic compounds in a highly structured mineral matrix.
3D printing bone, in other words, is a challenge within a challenge.
Which is why bioengineers have tried so many different materials for their synthetic bones—including hydrogels, thermoplastics, and bioceramics. Now, a team at the University of New South Wales in Sydney, Australia, has developed a ceramic ink that can be 3D-printed at room temperature with live cells and without harsh chemicals—a notable improvement over earlier technologies. The new technique could eventually be used to print bone directly into a patient’s body, the researchers say.
There’s a new player in the effort to quickly and effectively screen populations for COVID-19: This week, a Princeton spin-off company launches a coronavirus-screening app for businesses that takes two minutes and uses data from commercial wearable devices.
In early clinical tests, the tool was 90% accurate in predicting if a person was positive or negative for the virus—even if they had no noticeable symptoms. Current rapid diagnostic tools, such as temperature checks, are not effective at detecting and preventing the spread of COVID, according to federal authorities.
,As the nasal swab probed higher into my nose, as if straining to pierce my brain, time slowed like molasses. Time stretched even longer as I waited three days for the COVID-19 test results.
That was in early August. Now it’s late December, and I’m currently on day four waiting for COVID-19 results for my three-year-old (who enjoyed the nasal swab even less than I did).
Both of us received a PCR test, the gold-standard for COVID-19 testing with a less-than-golden turnaround time of days to weeks. That’s far too slow to use testing as a method to contain the virus as we each wait our turn for a COVID-19 vaccine. Antigen tests, like the one approved by the FDA this week for at-home use, are fast but have sensitivity issues, so health authorities continue to emphasize the need for rapid, reliable tests.
Now, we may be on the cusp of their arrival.
A new wave of rapid molecular tests—which promise the sensitivity of a PCR test with the speed of an antigen test—have recently been validated and are moving from prototype toward FDA approval. In the last two weeks, studies published in the journals Science Advances and Cell detail ultrasensitive molecular COVID-19 tests based on gene-editing CRISPR technology.
In March, organizers of the COVID Moonshot initative crowdsourced chemical designs for COVID-19 antivirals. They received over 14,000 submissions from chemists around the world.
PostEra, a machine-learning company leading the Moonshot initiative, triaged those submissions for how quickly and easily each chemical compound could be synthesized. One looked particularly promising, and PostEra sent data about the compound back to an online volunteer crowd of medicinal chemists.
The crowd and PostEra’s machine-learning algorithms iterated back and forth, designing and testing tweaks on the chemical structure. Soon, the compound’s potency had increased by two orders of magnitude. Then, the chemical compound successfully killed live coronavirus in human cells without harming the cells. Now, that drug candidate and three more promising compounds are headed to animal testing in preparation for human clinical trials.
For the last 25 years, researchers have sought to teach computers to measure, understand, and react to human emotion. Technologies developed in this wave of emotion AI—alternately called affective computing or artificial emotional intelligence—have been applied in a variety of ways: capturing consumer reactions to advertisements; measuring student engagement in class; and detecting customer moods over the phone at call centers, among others.
Emotion AI has been less widely applied in healthcare, as can be seen in a recent literature review. In a meticulous analysis of 156 papers on AI in pregnancy health, a team in Spain found only two papers in which emotions were used as inputs. Their review, published in the journal IEEE Access, concluded that expanded use of affective computing could help improve health outcomes for pregnant women and their infants.
“There is a lot of evidence that stress, anxiety and negative feelings can make [outcomes] worse for pregnant women,” says study co-author Andreea Oprescu, a PhD student at the University of Seville. An app or wearable that takes these feelings into account could better help detect and monitor certain conditions, she notes.
Again and again, experts have pleaded that we need more and faster testing to control the coronavirus pandemic—and many have suggested that artificial intelligence (AI) can help. Numerous COVID-19 diagnostics in development use AI to quickly analyze X-ray or CT scans, but these techniques require a chest scan at a medical facility.
Since the spring, research teams have been working toward anytime, anywhere apps that could detect coronavirus in the bark of a cough. In June, a team at the University of Oklahoma showed it was possible to distinguish a COVID-19 cough from coughs due to other infections, and now a paper out of MIT, using the largest cough dataset yet, identifies asymptomatic people with a remarkable 100 percent detection rate.
If approved by the FDA and other regulators, COVID-19 cough apps, in which a person records themselves coughing on command, could eventually be used for free, large-scale screening of the population.
The first time Karl Deisseroth used light to control brain cells in a dish, people had a lot of questions, three in particular. Can the technique be used in living animals? Can it target different cell types? Can it work without implanting a light source into the brain?
In the years since that initial groundbreaking 2004 experiment, Deisseroth’s team and others found the answers to the first two questions: yes and yes. This month they answered the third question with another yes, successfully introducing an implant-free version of the technique. It is the first demonstration that optogenetics—which uses a combination of light and genetic engineering to control brain cells—can accurately switch the cells on and off without surgery.
“This is kind of a nice bookend to 16 years of research,” says Deisseroth, a neuroscientist and bioengineer at Stanford University. “It took years and years for us to sort out how to make it work.” The result is described this month in the journal Nature Biotechnology.
It can sound like a soft buzzing in one’s ears. Or a sudden hissing. Or a loud roaring. Tinnitus, the sensation of hearing phantom sounds, ranges from annoying to debilitating, and it affects an estimated 10 to 15 percent of the population. Unfortunately, finding relief from these symptoms can be tough.
Doctors and patients may find themselves attempting many treatments for tinnitus, including sound machines to mask the phantom noise, medications to treat underlying anxiety or depression, and investigational brain implants or vagus nerve stimulation. In the United States, there are currently no clinically approved drugs or devices to treat tinnitus.
Now, in a paper published today in the journal Science Translational Medicine, researchers at Dublin-based biotech Neuromod Devices, along with academic collaborators, present positive results from a year-long, randomized clinical trial of a device that pairs sound with gentle electrical tongue stimulation to treat tinnitus. In a group of 326 adults, 12 weeks of treatment with the device significantly reduced tinnitus symptom severity for up to 12 months after treatment.
Within moments of meeting each other at a conference last year, Nathan Collins and Yann Gaston-Mathé began devising a plan to work together. Gaston-Mathé runs a startup that applies automated software to the design of new drug candidates. Collins leads a team that uses an automated chemistry platform to synthesize new drug candidates.
“There was an obvious synergy between their technology and ours,” recalls Gaston-Mathé, CEO and cofounder of Paris-based Iktos.
In late 2019, the pair launched a project to create a brand-new antiviral drug that would block a specific protein exploited by influenza viruses. Then the COVID-19 pandemic erupted across the world stage, and Gaston-Mathé and Collins learned that the viral culprit, SARS-CoV-2, relied on a protein that was 97 percent similar to their influenza protein. The partners pivoted.
Their companies are just two of hundreds of biotech firms eager to overhaul the drug-discovery process, often with the aid of artificial intelligence (AI) tools. The first set of antiviral drugs to treat COVID-19 will likely come from sifting through existing drugs. Remdesivir, for example, was originally developed to treat Ebola, and it has been shown to speed the recovery of hospitalized COVID-19 patients. But a drug made for one condition often has side effects and limited potency when applied to another. If researchers can produce an antiviral that specifically targets SARS-CoV-2, the drug would likely be safer and more effective than a repurposed drug.
There’s one big problem: Traditional drug discovery is far too slow to react to a pandemic. Designing a drug from scratch typically takes three to five years—and that’s before human clinical trials. “Our goal, with the combination of AI and automation, is to reduce that down to six months or less,” says Collins, who is chief strategy officer at SRI Biosciences, a division of the Silicon Valley research nonprofit SRI International. “We want to get this to be very, very fast.”
That sentiment is shared by small biotech firms and big pharmaceutical companies alike, many of which are now ramping up automated technologies backed by supercomputing power to predict, design, and test new antivirals—for this pandemic as well as the next—with unprecedented speed and scope.
“The entire industry is embracing these tools,” says Kara Carter, president of the International Society for Antiviral Research and executive vice president of infectious disease at Evotec, a drug-discovery company in Hamburg. “Not only do we need [new antivirals] to treat the SARS-CoV-2 infection in the population, which is probably here to stay, but we’ll also need them to treat future agents that arrive.”
There are currentlyabout 200 known viruses that infect humans. Although viruses represent less than 14 percent of all known human pathogens, they make up two-thirds of all new human pathogens discovered since 1980.
Antiviral drugs are fundamentally different from vaccines, which teach a person’s immune system to mount a defense against a viral invader, and antibody treatments, which enhance the body’s immune response. By contrast, antivirals are chemical compounds that directly block a virus after a person has become infected. They do this by binding to specific proteins and preventing them from functioning, so that the virus cannot copy itself or enter or exit a cell.
The SARS-CoV-2 virus has an estimated 25 to 29 proteins, but not all of them are suitable drug targets. Researchers are investigating, among other targets, the virus’s exterior spike protein, which binds to a receptor on a human cell; two scissorlike enzymes, called proteases, that cut up long strings of viral proteins into functional pieces inside the cell; and a polymerase complex that makes the cell churn out copies of the virus’s genetic material, in the form of single-stranded RNA.
But it’s not enough for a drug candidate to simply attach to a target protein. Chemists also consider how tightly the compound binds to its target, whether it binds to other things as well, how quickly it metabolizes in the body, and so on. A drug candidate may have 10 to 20 such objectives. “Very often those objectives can appear to be anticorrelated or contradictory with each other,” says Gaston-Mathé.
Compared with antibiotics, antiviral drug discovery has proceeded at a snail’s pace. Scientists advanced from isolating the first antibacterial molecules in 1910 to developing an arsenal of powerful antibiotics by 1944. By contrast, it took until 1951 for researchers to be able to routinely grow large amounts of virus particles in cells in a dish, a breakthrough that earned the inventors a Nobel Prize in Medicine in 1954.
And the lag between the discovery of a virus and the creation of a treatment can be heartbreaking. According to the World Health Organization, 71 million people worldwide have chronic hepatitis C, a major cause of liver cancer. The virus that causes the infection was discovered in 1989, but effective antiviral drugs didn’t hit the market until 2014.
While many antibiotics work on a range of microbes, most antivirals are highly specific to a single virus—what those in the business call “one bug, one drug.” It takes a detailed understanding of a virus to develop an antiviral against it, says Che Colpitts, a virologist at Queen’s University, in Canada, who works on antivirals against RNA viruses. “When a new virus emerges, like SARS-CoV-2, we’re at a big disadvantage.”
Making drugs to stop viruses is hard for three main reasons. First, viruses are the Spartans of the pathogen world: They’re frugal, brutal, and expert at evading the human immune system. About 20 to 250 nanometers in diameter, viruses rely on just a few parts to operate, hijacking host cells to reproduce and often destroying those cells upon departure. They employ tricks to camouflage their presence from the host’s immune system, including preventing infected cells from sending out molecular distress beacons. “Viruses are really small, so they only have a few components, so there’s not that many drug targets available to start with,” says Colpitts.
Second, viruses replicate quickly, typically doubling in number in hours or days. This constant copying of their genetic material enables viruses to evolve quickly, producing mutations able to sidestep drug effects. The virus that causes AIDS soon develops resistance when exposed to a single drug. That’s why a cocktail of antiviral drugs is used to treat HIV infection.
Finally, unlike bacteria, which can exist independently outside human cells, viruses invade human cells to propagate, so any drug designed to eliminate a virus needs to spare the host cell. A drug that fails to distinguish between a virus and a cell can cause serious side effects. “Discriminating between the two is really quite difficult,” says Evotec’s Carter, who has worked in antiviral drug discovery for over three decades.
And then there’s the money barrier. Developing antivirals is rarely profitable. Health-policy researchers at the London School of Economics recently estimated that the average cost of developing a new drug is US $1 billion, and up to $2.8 billion for cancer and other specialty drugs. Because antivirals are usually taken for only short periods of time or during short outbreaks of disease, companies rarely recoup what they spent developing the drug, much less turn a profit, says Carter.
To change the status quo, drug discovery needs fresh approaches that leverage new technologies, rather than incremental improvements, says Christian Tidona, managing director of BioMed X, an independent research institute in Heidelberg, Germany. “We need breakthroughs.”
Iktos’s AI platform was created by a medicinal chemist and an AI expert. To tackle SARS-CoV-2, the company used generative models—deep-learning algorithms that generate new data—to “imagine” molecular structures with a good chance of disabling a key coronavirus protein.
For a new drug target, the software proposes and evaluates roughly 1 million compounds, says Gaston-Mathé. It’s an iterative process: At each step, the system generates 100 virtual compounds, which are tested in silico with predictive models to see how closely they meet the objectives. The test results are then used to design the next batch of compounds. “It’s like we have a very, very fast chemist who is designing compounds, testing compounds, getting back the data, then designing another batch of compounds,” he says.
The computer isn’t as smart as a human chemist, Gaston-Mathé notes, but it’s much faster, so it can explore far more of what people in the field call “chemical space”—the set of all possible organic compounds. Unexplored chemical space is huge: Biochemists estimate that there are at least 1063possible druglike molecules, and that 99.9 percent of all possible small molecules or compounds have never been synthesized.
Still, designing a chemical compound isn’t the hardest part of creating a new drug. After a drug candidate is designed, it must be synthesized, and the highly manual process for synthesizing a new chemical hasn’t changed much in 200 years. It can take days to plan a synthesis process and then months to years to optimize it for manufacture.
That’s why Gaston-Mathé was eager to send Iktos’s AI-generated designs to Collins’s team at SRI Biosciences. With $13.8 million from the Defense Advanced Research Projects Agency, SRI Biosciences spent the last four years automating the synthesis process. The company’s automated suite of three technologies, called SynFini, can produce new chemical compounds in just hours or days, says Collins.
First, machine-learning software devises possible routes for making a desired molecule. Next, an inkjet printer platform tests the routes by printing out and mixing tiny quantities of chemical ingredients to see how they react with one another; if the right compound is produced, the platform runs tests on it. Finally, a tabletop chemical plant synthesizes milligrams to grams of the desired compound.
Less than four months after Iktos and SRI Biosciences announced their collaboration, they had designed and synthesized a first round of antiviral candidates for SARS-CoV-2. Now they’re testing how well the compounds work on actual samples of the virus.
Theirs isn’t the only collaborationapplying new tools to drug discovery. In late March, Alex Zhavoronkov, CEO of Hong Kong–based Insilico Medicine, came across a YouTube video showing three virtual-reality avatars positioning colorful, sticklike fragments in the side of a bulbous blue protein. The three researchers were using VR to explore how compounds might bind to a SARS-CoV-2 enzyme. Zhavoronkov contacted the startup that created the simulation—Nanome, in San Diego—and invited it to examine Insilico’s AI-generated molecules in virtual reality.
Insilico runs an AI platform that uses biological data to train deep-learning algorithms, then uses those algorithms to identify molecules with druglike features that will likely bind to a protein target. A four-day training sprint in late January yielded 100 molecules that appear to bind to an important SARS-CoV-2 protease. The company recently began synthesizing some of those molecules for laboratory testing.
Nanome’s VR software, meanwhile, allows researchers to import a molecular structure, then view and manipulate it on the scale of individual atoms. Like human chess players who use computer programs to explore potential moves, chemists can use VR to predict how to make molecules more druglike, says Nanome CEO Steve McCloskey. “The tighter the interface between the human and the computer, the more information goes both ways,” he says.
Zhavoronkov sent data about several of Insilico’s compounds to Nanome, which re-created them in VR. Nanome’s chemist demonstrated chemical tweaks to potentially improve each compound. “It was a very good experience,” says Zhavoronkov.
Meanwhile, in March, Takeda Pharmaceutical Co., of Japan, invited Schrödinger, a New York–based company that develops chemical-simulation software, to join an alliance working on antivirals. Schrödinger’s AI focuses on the physics of how proteins interact with small molecules and one another.
The software sifts through billions of molecules per week to predict a compound’s properties, and it optimizes for multiple desired properties simultaneously, says Karen Akinsanya, chief biomedical scientist and head of discovery R&D at Schrödinger. “There’s a huge sense of urgency here to come up with a potent molecule, but also to come up with molecules that are going to be well tolerated” by the body, she says. Drug developers are seeking compounds that can be broadly used and easily administered, such as an oral drug rather than an intravenous drug, she adds.
Schrödinger evaluated four protein targets and performed virtual screens for two of them, a computing-intensive process. In June, Google Cloud donated the equivalent of 16 million hours of Nvidia GPU time for the company’s calculations. Next, the alliance’s drug companies will synthesize and test the most promising compounds identified by the virtual screens.
Other companies, including Amazon Web Services, IBM, and Intel, as well as several U.S. national labs are also donating time and resources to the Covid-19 High Performance Computing Consortium. The consortium is supporting 87 projects, which now have access to 6.8 million CPU cores, 50,000 GPUs, and 600 petaflops of computational resources.
While advanced technologies could transform early drug discovery, any new drug candidate still has a long road after that. It must be tested in animals, manufactured in large batches for clinical trials, then tested in a series of trials that, for antivirals, lasts an average of seven years.
In May, the BioMed X Institute in Germany launched a five-year project to build a Rapid Antiviral Response Platform, which would speed drug discovery all the way through manufacturing for clinical trials. The €40 million ($47 million) project, backed by drug companies, will identify outside-the-box proposals from young scientists, then provide space and funding to develop their ideas.
“We’ll focus on technologies that allow us to go from identification of a new virus to 10,000 doses of a novel potential therapeutic ready for trials in less than six months,” says BioMed X’s Tidona, who leads the project.
While a vaccine will likely arrive long before a bespoke antiviral does, experts expect COVID-19 to be with us for a long time, so the effort to develop a direct-acting, potent antiviral continues. Plus, having new antivirals—and tools to rapidly create more—can only help us prepare for the next pandemic, whether it comes next month or in another 102 years.
“We’ve got to start thinking differently about how to be more responsive to these kinds of threats,” says Collins. “It’s pushing us out of our comfort zones.”
This article appears in the October 2020 print issue as “Automating Antivirals.”
This week, a California-based company announced it will seek FDA clearance for a first-of-its-kind autism spectrum disorder (ASD) diagnostic tool. Cognoa’s technology uses artificial intelligence to make an ASD diagnosis within weeks of signs of concern—far faster than the current standard of care. If cleared by the FDA, it would be the first tool enabling primary care pediatricians to diagnose autism.
The approach is “innovative,” says Robin Goin-Kochel, a clinical autism researcher at Baylor College of Medicine and associate director for research at Texas Children’s Hospital’s Autism Center, who is not affiliated with Cognoa. The field absolutely needs a way to “minimize the time between first concerns about development or behavior and eventual ASD diagnosis,” she adds.
Cognoa’s tool is the latest application of AI to healthcare, a fast-moving field we’ve been tracking at IEEE Spectrum. In many situations, AI tools seek to replace doctors in the prediction or diagnosis of a condition. In this case, however, the application of AI could enable more doctors to make a diagnosis of autism, thereby opening a critical bottleneck in children’s healthcare.
Throughout the COVID-19 pandemic, now entering its seventh month, a simple piece of personal protective equipment has been in short supply: N95 masks.
N95 and other medical-grade masks rely on two filtration methods: mechanical filtering by mask fibers, and electrostatic filtering, in which stationary electric charges attract and ensnare tiny 0.3-micron particles such fluid droplets containing viruses. The masks are specified for single-use only because even after a day, the electrostatic charges in the mask leak out into the air and the mask becomes less effective at filtering out particles. That gradual loss of efficiency is even worse in countries like India where high humidity speeds the loss of static charge to the air.
Just three months after the start of the pandemic, drugmaker Eli Lilly has announced the first human test of an antibody treatment designed to fight the novel coronavirus.
The potential drug, developed by Lilly, Vancouver-based biotech company AbCellera, and the Vaccine Research Center at the U.S. National Institute of Allergy and Infectious Disease, was identified by screening over 5 million immune cells in the blood of one of the first people in North America to recover after having contracted COVID-19.
The drug candidate is being tested in a randomized, placebo-controlled safety trial with 32 patients hospitalized for COVID-19 at major medical centers in the U.S.
Face masks help limit the spread of COVID-19 and are currently recommended by governments worldwide.
Now, engineers at Indiana University demonstrate for the first time that a fabric generating a weak electric field can inactivate coronaviruses. The electroceutical fabric, described in a ChemRxiv preprint that has not yet been peer-reviewed, could be used to make face masks and other personal protective equipment (PPE), the authors say.
The fabric was tested against a pig respiratory coronavirus and a human coronavirus that causes the common cold. It has not yet been tested against SARS-CoV-2, the virus that causes COVID-19.
“The work is of interest for the scientific community; it will open new [areas to] search to provide smart solutions to overcome the COVID-19 pandemic,” says Mahmoud Al Ahmad, an electrical engineer at the University of United Arab Emirates, who was not involved in the research. While the concept will require more development before being applied to PPE, he says, “it is an excellent start in this direction.”
Beyond masks, the findings raise the possibility of using weak electrical fields to curb the spread of viruses in many ways, such as purifying air in common spaces or disinfecting operating room surfaces, says study author Chandan Sen, director of the Indiana Center for Regenerative Medicine and Engineering at Indiana University School of Medicine. “Coronavirus is not the first or last virus that is going to disrupt our lives,” he says. “We’re thinking about bigger and broader approaches to utilize weak electric fields against virus infectivity.”
Sen’s lab has been co-developing the electroceutical fabric technology, under the proprietary name V.Dox Technology, with Arizona-based company Vomaris for the past six years. Sen retains a financial stake in the company.
The technology consists of a matrix pattern of silver and zinc dots printed onto a material, such as polyester or cotton. The dots form a battery generating a weak electric field: When exposed to a conductive medium, like gel or sweat, electrons transfer from the zinc to the silver in a REDOX reaction, generating a potential difference of 0.5 volts. The technology is FDA-cleared and commercialized for wound care, where it has been shown to treat bacterial biofilm infections.
To be used in masks, moisture will need to be applied in some fashion. According to Sen, approaches could include embedding a hydrogel so it activates the dots or inserting liquid-filled piping on periphery of the mask. Moisture from exhaled air will continue to keep the fabric moist.
When the COVID-19 pandemic began, Sen and his team began to wonder if the technology might affect viruses as well as bacteria. Past work in the literature suggested coronaviruses rely on electrostatic forces for attachment and genome assembly, and Sen hoped an electric field would disrupt those forces and therefore kill the virus.
In collaboration with IU geneticist Kenneth Cornetta, who performed some of the initial virus experiments in his laboratory, the team exposed a pig respiratory coronavirus to the electroceutical fabric for 1 or 5 minutes. After one minute, they found evidence that the virus particles had begun to destabilize and aggregate, becoming larger than before exposure. That suggests the weak electric field was causing “damaging structural alterations to the virions,” the authors write.
Next, the team tested the virus particles exposed to the fabric against cells in a dish. “The infectivity was gone,” says Sen.
The results indicate “promise for this strategy,” says Murugappan Muthukumar, a professor of polymer science and engineering at the University of Massachusetts, Amherst, who was not involved in the study. “The authors’ hypothesis that the electrostatic forces within the virus particles and between the virus particles and the fabric are important is correct and is a very good idea.”
Still, Muthukumar notes, it is difficult to extrapolate how the electric field affects the viral genome, and more work needs to be done to investigate the effects observed in the paper.
Since publishing the preprint, the team also tested the fabric against human coronavirus 229E, a cause of upper respiratory tract infections, and gotten similar results, adds Sen.
The team has submitted the data to the FDA in the hopes of receiving Emergency Use Authorization to use the fabric in face masks. The technology could even be incorporated into the manufacturing of N95 masks or as an insert, says Sen.
Vomaris currently sells their wound-dressing kits for between $38 and $69 online. Sen says the technology is inexpensive to manufacture and could be used in PPE at a modest cost.
Independent of Vomaris, Sen’s laboratory is developing a tunable electroceutical called patterned electroceutical dressing, in which the field strength can be altered depending on need. The dressing has shown to be safe for patients with wounds, says Sen, and is currently in clinical testing.
In Maryland, restaurant patrons stand inside bumper-style tables to keep six feet apart. In New York, sunbathers maintain distance by lounging in white chalk circles painted on a grassy field.
As the United States slowly begins opening public spaces, organizations are getting creative about how to encourage social distancing. But two new studies on the airborne spread of saliva droplets, which can harbor virus particles from respiratory diseases like COVID-19, suggest those six feet alone are not always enough.
In a rare act of cooperation, Google and Apple this month released specifications for software developers to build digital contact tracing apps for Apple and Google mobile operating systems, which jointly encompass the majority of smartphones around the world.
Digital contact tracing, which can automatically notify an individual if they’ve crossed paths with someone who tested positive for COVID-19, has been proposed as a way to augment manual contact tracing, which requires the painstaking work of thousands of trained workers per state to identify, track, and assist individuals exposed to the virus.
As digital contact tracing technologies advance, two questions rise to the surface: Will state health officials and individuals opt to use the technology? And, if so, how well will it work?
It started with a tweet. Alpha Lee, co-founder and chief scientific officer of machine-learning company PostEra, read on Twitter that Diamond Light Source, the UK’s national synchrotron facility, had identified a set of chemical fragments that attach to an important coronavirus protein.
Lee wondered if his company, formed just six months earlier, could help connect the dots from fragments to viable drugs to fight COVID-19. PostEra uses AI algorithms to map routes for drug synthesis to speed the drug discovery process. But to do so, they would need some design ideas. So Lee asked the Internet.
On 17 March, in collaboration with Diamond, the PostEra team launched the COVID Moonshot to crowdsource drug designs from medicinal chemists. Then PostEra applied their technology, pro-bono, to determine if and how those designs could be made.
In reaction to the rapid spread of COVID-19, over 100 countries worldwide have instituted lockdowns, restricting movement for billions of people. Those restrictions are having major effects, such as a global food crisis, on economically vulnerable populations.
Now, a group of 73 volunteer engineers, students, and policy experts are working to identify and quantify unintended consequences of the COVID-19 lockdown on vulnerable populations.
For two months, the group, organized by Palo Alto-based Omdena, will scour publicly available data sources and apply data visualization and AI tools to investigate how government policies are impacting four effects of the pandemic lockdowns: reduced access to healthcare, wage loss, employment loss, and domestic abuse.
“All over the world, from the media to state leaders, we are hearing a lot of noise and a lot of propaganda, amidst a lot of facts,” says Baidurja Ray, a computational scientist and engineer based in Texas who is participating in the effort. “I hope this project provides a purely factual and data-driven look into the effect of government policies on their citizens.”
Over the past week, companies around the world announced a flurry of AI-based systems to detect COVID-19 on chest CT or X-ray scans. Already, these deep learning tools are being used in hospitals to screen mild cases, triage new infections, and monitor advancing disease.
AI-powered analysis of chest scans has the potential to alleviate the growing burden on radiologists, who must review and prioritize a rising number of patient chest scans each day, experts say. And in the future, the technology might help predict which patients are most likely to need a ventilator or medication, and which can be sent home.
Last Wednesday, Todd Goldstein was working on other projects. Then physicians in the New York-based hospital system where he works, hard hit by a surge in COVID-19 cases, told him they were worried about running out of supplies.
Specifically, they needed more nasal test swabs. A nasopharyngeal swab for COVID-19 is no ordinary Q-tip. These specialty swabs cannot be made of cotton, nor have wood handles. They must be long and skinny to fit up behind the nose into the upper part of the throat.
Goldstein, director of 3D Design and Innovation at Northwell Health, a network of 23 hospitals and 800 outpatient facilities, thought, “Well, we can make that.” He quickly organized a collaboration with Summer Decker and Jonathan Ford of the University of South Florida, and 3D-printing manufacturer Formlabs. In one week, the group designed, made, tested, and are now distributing 3D-printed COVID-19 test swabs.
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