Ten-minute scan enables detection and cure of the commonest cause of high blood pressure

 Doctors at Queen Mary University of London and Barts Hospital, and Cambridge University Hospital, have led research using a new type of CT scan to light up tiny nodules in a hormone gland and cure high blood pressure by their removal. The nodules are discovered in one-in-twenty people with high blood pressure.

Published today in Nature Medicine, the research solves a 60-year problem of how to detect the hormone producing nodules without a difficult catheter study that is available in only a handful of hospitals, and often fails. The research also found that, when combined with a urine test, the scan detects a group of patients who come off all their blood pressure medicines after treatment.

128 people participated in the study of a new scan after doctors found that their Hypertension (high blood pressure) was caused by a steroid hormone, aldosterone. The scan found that in two thirds of patients with elevated aldosterone secretion, this is coming from a benign nodule in just one of the adrenal glands, which can then be safely removed. The scan uses a very short-acting dose of metomidate, a radioactive dye that sticks only to the aldosterone-producing nodule. The scan was as accurate as the old catheter test, but quick, painless and technically successful in every patient. Until now, the catheter test was unable to predict which patients would be completely cured of hypertension by surgical removal of the gland. By contrast, the combination of a 'hot nodule' on the scan and urine steroid test detected 18 of the 24 patients who achieved a normal blood pressure off all their drugs.

The research, conducted on patients at Barts Hospital, Cambridge University Hospital, and Guy's and St Thomas's, and Universities of Glasgow and Birmingham, was funded by the National Institute for Health and Care Research (NIHR) and Medical Research Council (MRC) partnership, Barts Charity, and the British Heart Foundation.

Professor Morris Brown, co-senior author of the study and Professor of Endocrine Hypertension at Queen Mary University of London, said: "These aldosterone-producing nodules are very small and easily overlooked on a regular CT scan. When they glow for a few minutes after our injection, they are revealed as the obvious cause of Hypertension, which can often then be cured. Until now, 99% are never diagnosed because of the difficulty and unavailability of tests. Hopefully this is about to change."

Professor William Drake, co-senior author of the study and Professor of Clinical Endocrinology at Queen Mary University of London, said:"This study was the result of years of hard work and collaboration between centres across the UK. Much of the 'on the ground' energy and drive came from the talented research fellows who, in addition to doing this innovative work, gave selflessly of their time and energy during the national pandemic emergency. The future of research in this area is in very safe hands."

In most people with Hypertension (high blood pressure), the cause is unknown, and the condition requires life-long treatment by drugs. Previous research by the group at Queen Mary University discovered that in 5-10% of people with Hypertension the cause is a gene mutation in the adrenal glands, which results in excessive amounts of the steroid hormone, aldosterone, being produced. Aldosterone causes salt to be retained in the body, driving up the blood pressure. Patients with excessive aldosterone levels in the blood are resistant to treatment with the commonly used drugs for Hypertension, and at increased risk of heart attacks and strokes

Computers that power self-driving cars could be a huge driver of global carbon emissions

 In the future, the energy needed to run the powerful computers on board a global fleet of autonomous vehicles could generate as many greenhouse gas emissions as all the data centers in the world today.

That is one key finding of a new study from MIT researchers that explored the potential energy consumption and related carbon emissions if autonomous vehicles are widely adopted.

The data centers that house the physical computing infrastructure used for running applications are widely known for their large carbon footprint: They currently account for about 0.3 percent of global greenhouse gas emissions, or about as much carbon as the country of Argentina produces annually, according to the International Energy Agency. Realizing that less attention has been paid to the potential footprint of autonomous vehicles, the MIT researchers built a statistical model to study the problem. They determined that 1 billion autonomous vehicles, each driving for one hour per day with a computer consuming 840 watts, would consume enough energy to generate about the same amount of emissions as data centers currently do.

The researchers also found that in over 90 percent of modeled scenarios, to keep autonomous vehicle emissions from zooming past current data center emissions, each vehicle must use less than 1.2 kilowatts of power for computing, which would require more efficient hardware. In one scenario -- where 95 percent of the global fleet of vehicles is autonomous in 2050, computational workloads double every three years, and the world continues to decarbonize at the current rate -- they found that hardware efficiency would need to double faster than every 1.1 years to keep emissions under those levels.

"If we just keep the business-as-usual trends in decarbonization and the current rate of hardware efficiency improvements, it doesn't seem like it is going to be enough to constrain the emissions from computing onboard autonomous vehicles. This has the potential to become an enormous problem. But if we get ahead of it, we could design more efficient autonomous vehicles that have a smaller carbon footprint from the start," says first author Soumya Sudhakar, a graduate student in aeronautics and astronautics.

Sudhakar wrote the paper with her co-advisors Vivienne Sze, associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Research Laboratory of Electronics (RLE); and Sertac Karaman, associate professor of aeronautics and astronautics and director of the Laboratory for Information and Decision Systems (LIDS). The research appears in the January-February issue of IEEE Micro.

Modeling emissions

The researchers built a framework to explore the operational emissions from computers on board a global fleet of electric vehicles that are fully autonomous, meaning they don't require a back-up human driver.

The model is a function of the number of vehicles in the global fleet, the power of each computer on each vehicle, the hours driven by each vehicle, and the carbon intensity of the electricity powering each computer.

"On its own, that looks like a deceptively simple equation. But each of those variables contains a lot of uncertainty because we are considering an emerging application that is not here yet," Sudhakar says.

For instance, some research suggests that the amount of time driven in autonomous vehicles might increase because people can multitask while driving and the young and the elderly could drive more. But other research suggests that time spent driving might decrease because algorithms could find optimal routes that get people to their destinations faster.

In addition to considering these uncertainties, the researchers also needed to model advanced computing hardware and software that doesn't exist yet.

To accomplish that, they modeled the workload of a popular algorithm for autonomous vehicles, known as a multitask deep neural network because it can perform many tasks at once. They explored how much energy this deep neural network would consume if it were processing many high-resolution inputs from many cameras with high frame rates, simultaneously.

When they used the probabilistic model to explore different scenarios, Sudhakar was surprised by how quickly the algorithms' workload added up.

For example, if an autonomous vehicle has 10 deep neural networks processing images from 10 cameras, and that vehicle drives for one hour a day, it will make 21.6 million inferences each day. One billion vehicles would make 21.6 quadrillion inferences. To put that into perspective, all of Facebook's data centers worldwide make a few trillion inferences each day (1 quadrillion is 1,000 trillion).

"After seeing the results, this makes a lot of sense, but it is not something that is on a lot of people's radar. These vehicles could actually be using a ton of computer power. They have a 360-degree view of the world, so while we have two eyes, they may have 20 eyes, looking all over the place and trying to understand all the things that are happening at the same time," Karaman says.

Autonomous vehicles would be used for moving goods, as well as people, so there could be a massive amount of computing power distributed along global supply chains, he says. And their model only considers computing -- it doesn't take into account the energy consumed by vehicle sensors or the emissions generated during manufacturing.

Keeping emissions in check

To keep emissions from spiraling out of control, the researchers found that each autonomous vehicle needs to consume less than 1.2 kilowatts of energy for computing. For that to be possible, computing hardware must become more efficient at a significantly faster pace, doubling in efficiency about every 1.1 years.

One way to boost that efficiency could be to use more specialized hardware, which is designed to run specific driving algorithms. Because researchers know the navigation and perception tasks required for autonomous driving, it could be easier to design specialized hardware for those tasks, Sudhakar says. But vehicles tend to have 10- or 20-year lifespans, so one challenge in developing specialized hardware would be to "future-proof" it so it can run new algorithms.

In the future, researchers could also make the algorithms more efficient, so they would need less computing power. However, this is also challenging because trading off some accuracy for more efficiency could hamper vehicle safety.

Now that they have demonstrated this framework, the researchers want to continue exploring hardware efficiency and algorithm improvements. In addition, they say their model can be enhanced by characterizing embodied carbon from autonomous vehicles -- the carbon emissions generated when a car is manufactured -- and emissions from a vehicle's sensors.

While there are still many scenarios to explore, the researchers hope that this work sheds light on a potential problem people may not have considered.

"We are hoping that people will think of emissions and carbon efficiency as important metrics to consider in their designs. The energy consumption of an autonomous vehicle is really critical, not just for extending the battery life, but also for sustainability," says Sze.

Rx for prolonged sitting: A five-minute stroll every half hour

     Mounting evidence suggests that prolonged sitting -- a staple of modern-day life -- is hazardous to your health, even if you exercise regularly. Based on these findings, doctors advise all adults to sit less and move more.

But how often do we need to get up from our chairs? And for how long?

Few studies have compared multiple options to come up with the answer most office workers want: What is the least amount of activity needed to counteract the health impact of a workday filled with sitting?

Now a study by Columbia University exercise physiologists has an answer: just five minutes of walking every half hour during periods of prolonged sitting can offset some of the most harmful effects.

The study, led by Keith Diaz, PhD, associate professor of behavioral medicine at Columbia University Vagelos College of Physicians and Surgeons, was published online in Medicine & Science in Sports & Exercise, the journal of the American College of Sports Medicine.

Unlike other studies that test one or two activity options, Diaz's study tested five different exercise "snacks": one minute of walking after every 30 minutes of sitting, one minute after 60 minutes; five minutes every 30; five minutes every 60; and no walking.

"If we hadn't compared multiple options and varied the frequency and duration of the exercise, we would have only been able to provide people with our best guesses of the optimal routine," Diaz says.

Each of the 11 adults who participated in the study came to Diaz's laboratory, where participants sat in an ergonomic chair for eight hours, rising only for their prescribed exercise snack of treadmill walking or a bathroom break. Researchers kept an eye on each participant to ensure they did not over- or under-exercise and periodically measured the participants' blood pressure and blood sugar (key indicators of cardiovascular health). Participants were allowed to work on a laptop, read, and use their phones during the sessions and were provided standardized meals.

The optimal amount of movement, the researchers found, was five minutes of walking every 30 minutes. This was the only amount that significantly lowered both blood sugar and blood pressure. In addition, this walking regimen had a dramatic effect on how the participants responded to large meals, reducing blood sugar spikes by 58% compared with sitting all day.

Taking a walking break every 30 minutes for one minute also provided modest benefits for blood sugar levels throughout the day, while walking every 60 minutes (either for one minute or five minutes) provided no benefit.

All amounts of walking significantly reduced blood pressure by 4 to 5 mmHg compared with sitting all day. "This is a sizeable decrease, comparable to the reduction you would expect from exercising daily for six months," says Diaz.

The researchers also periodically measured participants' levels of mood, fatigue, and cognitive performance during the testing. All walking regimens, except walking one minute every hour, led to significant decreases in fatigue and significant improvements in mood. None of the walking regimens influenced cognition.

"The effects on mood and fatigue are important," Diaz says. "People tend to repeat behaviors that make them feel good and that are enjoyable."

The Columbia researchers are currently testing 25 different doses of walking on health outcomes and testing a wider variety of people: Participants in the current study were in their 40s, 50s, and 60s, and most did not have diabetes or high blood pressure.

"What we know now is that for optimal health, you need to move regularly at work, in addition to a daily exercise routine," says Diaz. "While that may sound impractical, our findings show that even small amounts of walking spread through the work day can significantly lower your risk of heart disease and other chronic illnesses."