Mesa Biotech gains emergency FDA approval for rapid, point-of-care COVID-19 test

The U.S. Food and Drug Administration (FDA) is making use of its Emergency Use Authorization (EUA) powers to expand the pool of available COVID-19 testing resources in the U.S., and now you can add another rapid test that delivers results in just 30 minutes to the list. Mesa’s test is also small enough to be able to be used right at the frontline of care, including in clinics and hospitals, with multiple tests able to be run in parallel.

Mesa’s rapid test follows one from Cepheid that was approved on Monday. Both are PCR-based molecular tests, which identify the presence of virus DNA in a sample of a patient’s mucus. Both these tests prevent an important expansion of the technologies available to those looking to combat the spread of the new coronavirus, since they can provide lab-quality results, but can do so much faster, and without requiring transportation of the samples from the point of collection to off-site testing facilities.

On-site testing not only has advantages in terms of convenience and speedy return of results, but also in limiting the potential exposure of medical personnel to the virus itself. Testing on-site means you don’t need to worry about possible exposure to the virus for more people in the chain, including logistics and delivery people, as well as lab technicians and dedicated diagnostics people.

These tests will require that facilities are equipped with Mesa’s Accula testing system, but its equipment is already in use for testing flu, as well as other less serious equipment, and it was originally designed specifically to address use on the frontlines of efforts to combat global pandemics, including SARS before this.

Oura partners with UCSF to determine if its smart ring can help detect COVID-19 early

Startups continue to find new ways to contribute to ongoing efforts to fight the global spread of COVID-19 during the current global coronavirus pandemic, and personal health hardware-maker Oura is no exception. The smart ring startup is working with the University of California, San Francisco (UCSF) on a new study to see if its device can help detect early physiological signs that might indicate the onset of COVID-19.

This study will include two parts: Around 2,000 frontline healthcare professionals will get Oura rings to wear during the study. The rings track a user’s body temperature continuously, as well as their sleep patterns, heart rate and activity levels. Fever is a common and early symptom that could indicate COVID-19, and a continuously updated body temperature reading could detect fever very early. That’s not enough to confirm a case of COVID-19, of course, but the purpose of the study is to determine whether the range of readings Oura’s ring tracks might, taken together and with other signals, be useful in some kind of early detection effort.

There’s good reason why researches believe that Oura could be used in early detection: An Oura user in Finland claims the ring alerted him to the fact that he was ill before he was displaying any overt symptoms of the virus, prompting him to get tested (relatively easy in that country). Test results confirmed that while asymptomatic, he had indeed contracted COVID-19. As a result, UCSF researcher Dr. Ashley Mason hypothesizes that the Oura ring could anticipate COVID-19 onset by as many as two to three days before the onset of more obvious symptoms, like coughing.

Being able to detect the presence of the virus in an individual early is key to global containment efforts, but even more important when it comes to frontline healthcare workers. The earlier a frontline responder is diagnosed, the less chance that they expose their colleagues or others they’re working around in close quarters.

In addition to the Oura rings being provided to study participants, the plan is to expand it to include Oura’s general user population, meaning its more than 150,000 global users can opt in to participate and add to the overall pool of available information with their ring’s readings and daily symptom surveys. For existing Oura users, it’s a relatively low-lift way to contribute to the global effort to combat the pandemic — without even leaving the house.

Kinsa’s fever map could show just how crucial it is to stay home to stop COVID-19 spread

Smart thermometer maker Kinsa has been working on building accurate, predictive models of how seasonal illnesses like the flu travel in and among communities — and its fever map is finding new utility as the novel coronavirus pandemic grows globally. While Kinsa’s US Health Weather Map has no way of tracking the spread of COVID-19 specifically, as it looks only at fevers tied to geographic data, it could provide easy-to-grasp early indicators of the positive effects of social distancing and isolation measures at the community level.

At the time that Kinsa’s health weather map was covered in the New York Times in February, the company had around a million thermometers in market in the U.S., but it had experienced a significant increase in order volume of as many as 10,000 units per day in the week prior to its publication. That means that the company’s analytics are based on a very large data set relative to the total U.S. population. Kinsa founder and CEO Inder Singh told me this allowed them to achieve an unprecedented level of accuracy and granularity in flu forecasting down to the community level, working in partnership with Oregon State University Assistant Professor Ben Dalziel.

“We showed that the core hypothesis for why I started the company is real — and the core hypothesis was you need real-time, medically accurate, geolocated data that’s taken from people who’ve just fallen ill to detect outbreaks and predict the spread of illness,” Singh said. “What we did with our data is we punched it into Ben’s existing, first-principle models on infectious disease spread. And we were able to show that on September 15, we could predict the entire rest of cold and flu season with hyper-accuracy in terms of the peaks and the valleys — all the way out to the rest of flu season, i.e. 20 weeks out on a hyperlocal basis.”

Prior to this, there have been efforts to track and predict flu transmission, but the “state-of-the-art” to date has been predictions at the national or multi-state level — even trends in individual states, let alone within communities, was out of reach. And in terms of lead time, the best achievable was essentially three weeks out, rather than multiple months, as is possible with Kinsa and Dalziel’s model.

Even without the extraordinary circumstances presented by the global COVID-19 pandemic, what Singh, Dalziel and Kinsa have been able to accomplish is a major step forward in tech-enabled seasonal illness tracking and mitigation. But Kinsa also turned on a feature of their health weather map called “atypical illness levels” a month ago, and that could prove an important leading indicator in shedding more light on the transmission of COVID-19 across the U.S. — and the impact of key mitigation strategies like social distancing.

“We’re taking our real-time illness signal, and we’re subtracting out the expectation,” Singh says, explaining how the new view works. “So what you’re left with is atypical illness. In other words, a cluster of fevers that you would not expect from normal cold and flu time. So, presumably, that is COVID-19; I cannot definitively say it’s COVID-19, but what I can say is that it’s an unusual outbreak. It could be an anomalous flu, a strain that’s totally unexpected. It could be something else, but at least a portion of that is almost certainly going to be COVID-19.”

The ‘atypical illness’ view of Kinsa’s US Health Weather Map. Red indicates much higher than expected levels of illness, as indicated by fever.

The graph represents the actual number of reported fevers, versus the expected number for the region (represented in blue) based on Kinsa’s accurate seasonal flu prediction model.

In the example above, Singh says that the spike in fevers coincides with reports of Miami residents and tourists ignoring guidance around recommended distancing. The steep drop-off, however, follows after more extreme measures, including beach closures and other isolation tactics were adopted in the area. Singh says that they’re regularly seeing that areas where residents are ignoring social distancing best practices are seeing spikes, and that as soon as those are implemented, via lock-downs and other measures, within five days of those aggressive actions, you begin to see downward dips in the curve.

Kinsa’s data has the advantage of being real-time and continually updated by its users. That provides it with a time advantage over other indicators, like the results of increased testing programs for COVID-19, in terms of providing some indication of the more immediate effects of social distancing and isolation strategies. One of the criticisms that has appeared relative to these tactics is that the numbers continue to grow for confirmed cases — but experts expect those cases to grow as we expand the availability of testing and identify new cases of community transmission, even though social distancing is having a positive impact.

As Singh pointed out, Kinsa’s data is strictly about fever-range temperatures, not confirmed COVID-19 cases. But fever is a key and early symptom of COVID-19 in those who are symptomatic, and Kinsa’s existing work on predicting the prevalence of fevers related to cold and flu strongly indicate that what we’re looking at is in fact, at least to a significant degree, COVID-19 spread.

While some have balked at other discussions around using location data to track the spread of the outbreak, Singh says that they’re only interested in two things: geographic coordinates and temperature. They don’t want any personal identification details that they can tie to either of those signals, so it truly an anonymous aggregation project.

“There is no possible way to reverse engineer a geographic signal to an individual — it’s not possible to do it,” he told me. “This is the right equation to both protect people’s privacy and expose the data that society and communities need.”

For the purposes of tracking atypical illness, Kinsa isn’t currently able to get quite as granular as it is with its standard observed illness map, because it requires a higher degree of sophistication. But the company is eager to expand its data set with additional thermometers in the market. The Kinsa hardware is already out of stock everywhere, as are most health-related devices, but Singh says they’re pressing ahead with suppliers on sourcing more despite increased component costs across the board. Singh is also eager to work with other smart thermometer makers, either by inputting their data into his model, or by making the Kinsa app compatible with any Bluetooth thermometer that uses the standard connection interface for wireless thermometer hardware.

Currently, Kinsa is working on evolving the atypical illness view to include things like a visual indicator of how fast illness levels are dropping, and how fast they should be dropping in order to effectively break the chain of transmission, as a way to further help inform the public on the impact of their own choices and actions. Despite the widespread agreement by health agencies, researchers and medical professionals, advice to stay home and separated from others definitely presents a challenge for everyone — especially when the official numbers released daily are so dire. Kinsa’s tracker should provide a ray of hope, and a clear sign that each individual contribution matters.

FluSense system tracks sickness trends by autonomously monitoring public spaces

One of the obstacles to accurately estimating the prevalence of sickness in the general population is that most of our data comes from hospitals, not the 99.9 percent of the world that isn’t hospitals. FluSense is an autonomous, privacy-respecting system that counts the people and coughs in public spaces to keep health authorities informed.

Every year has a flu and cold season, of course, though this year’s is of course far more dire. But it’s like an ordinary flu season in that the main way anyone estimates how many people are sick is by analyzing stats from hospitals and clinics. Patients reporting “influenza-like illness” or certain symptoms get aggregated and tracked centrally. But what about the many folks who just stay home, or go to work sick?

We don’t know what we don’t know here, and that makes estimates of sickness trends — which inform things like vaccine production and hospital staffing — less reliable than they could be. Not only that, but it likely produces biases: Who is less likely to go to a hospital, and more likely to have to work sick? Folks with low incomes and no healthcare.

Researchers at the University of Massachusetts Amherst are attempting to alleviate this data problem with an automated system they call FluSense, which monitors public spaces, counting the people in them and listening for coughing. A few of these strategically placed in a city could give a great deal of valuable data and insight into flu-like illness in the general population.

Tauhidur Rahman and Forsad Al Hossain describe the system in a recent paper published in an ACM journal. FluSense basically consists of a thermal camera, a microphone, and a compact computing system loaded with a machine learning model trained to detect people and the sounds of coughing.

To be clear at the outset, this isn’t recording or recognizing individual faces; Like a camera doing face detection in order to set focus, this system only sees that a face and body exists and uses that to create a count of people in view. The number of coughs detected is compared to the number of people, and a few other metrics like sneezes and amount of speech, to produce a sort of sickness index — think of it as coughs per person per minute.

A sample setup, above, the FluSense prototype hardware, center, and sample output from the thermal camera with individuals being counted and outlined.

Sure, it’s a relatively simple measurement, but there’s nothing like this out there, even in places like clinic waiting rooms where sick people congregate; Admissions staff aren’t keeping a running tally of coughs for daily reporting. One can imagine not only characterizing the types of coughs, but visual markers like how closely packed people are, and location information like sickness indicators in one part of a city versus another.

“We believe that FluSense has the potential to expand the arsenal of health surveillance tools used to forecast seasonal flu and other viral respiratory outbreaks, such as the COVID-19 pandemic or SARS,” Rahman told TechCrunch. “By understanding the ebb and flow of the symptoms dynamics across different locations, we can have a better understanding of the severity of a novel infectious disease and that way we can enforce targeted public health intervention such as social distancing or vaccination.”

Obviously privacy is an important consideration with something like this, and Rahman explained that was partly why they decided to build their own hardware, since as some may have realized already, this is a system that’s possible (though not trivial) to integrate into existing camera systems.

“The researchers canvassed opinions from clinical care staff and the university ethical review committee to ensure the sensor platform was acceptable and well-aligned with patient protection considerations,” he said. “All persons discussed major hesitations about collection any high-resolution visual imagery in patient areas.”

Similarly, the speech classifier was built specifically to not retain any speech data beyond that someone spoke — can’t leak sensitive data if you never collect any.

The plan for now is to deploy FluSense “in several large public spaces,” one presumes on the UMass campus in order to diversify their data. “We are also looking for funding to run a large-scale multi-city trial,” Rahman said.

In time this could be integrated with other first- and second-hand metrics used in forecasting flu cases. It may not be in time to help much with controlling COVID-19, but it could very well help health authorities plan better for the next flu season, something that could potentially save lives.

Vaping additive blamed for outbreak produces ‘exceptionally toxic’ byproducts

The threat of vape lung seems to have receded to the distant past now that we are all facing the coronavirus, but new research has shed light on the nature of that much more limited epidemic. It turns out vitamin E acetate, the vape fluid additive until now merely associated with lung damage, converts into a toxic chemical cocktail when heated.

That vitamin E acetate is not healthy to inhale is not a surprise; the chemical, which essentially had been used to dilute THC oil in cheap aftermarket vape cartridges, was associated with lung damage in several studies cited by the CDC. But at the time it was more correlative than causative information — the oil was found in the lung tissues of those suffering from vape lung, but there was no proven mechanism for harm.

Now work from researchers at the Royal College of Surgeons, in Dublin, has shown that vitamin E acetate’s effects are not limited to sticking around in the lungs and gumming up operations there. In a paper published in the Proceedings of the National Academy of Sciences, Dan Wu and Donal O’Shea write:

A combined analytical, theoretical, and experimental study has shown that the vaping of vitamin E acetate has the potential to produce exceptionally toxic ketene gas…

Additionally, the pyrolysis [i.e. heating] of vitamin E acetate also produces carcinogen alkenes and benzene for which the negative long-term medical effects are well recognized.

In other words, heating up vitamin E acetate, on its own or in a mixture, provably produces a number of toxic and carcinogenic compounds. Other studies would have to establish the potential pathology of those compounds interacting with the lungs, bloodstream, etc., but it’s safe to say that this substance is an extremely dangerous one to be burning and inhaling.

The obvious question of why it was put into cartridges in the first place is answered by the fact that vaping is a much lower-cost alternative to cigarettes and many forms of cannabis. As vaping went downmarket, a race to the bottom was all but assured, and unscrupulous cartridge makers cut THC oils with substances that would produce no major changes in the immediate experience of taste, mouthfeel and so on. Vitamin E acetate was one of those substances.

There are few regulations on this sector of commerce and, frankly, even if there were, it would be trivial to avoid them. Besides, marijuana has a long history of existing outside of FDA approval. People are going to smoke whatever they want. But it seems clear now that there are plenty in the industry who have no problem putting others at risk of serious injury or death to sell more of their product.

It’s difficult to say which vape product providers were using vitamin E acetate, though it seems it was mostly THC cartridges on the low end, which make sense. There’s no need to dilute your product if people are already paying a healthy premium and you have a reputation as a high-end brand.

Exactly how testing and verification should be accomplished is a matter for the FDA, the vaping industry and individual shops, which may conduct their own checks to reassure customers. But the testing must be done — or else other unknown interactions or substances may still produce the sort of long-term health effects we are trying to avoid.

As Wu and O’Shea write:

The potential for unexpected chemistries to take place on individual components within a vape mixture is high. Educational programs to inform of the danger are now required, as public perception has grown that vaping is not harmful.

Until it’s been tested, it’s still a risk.