by George Taniwaki
NASA recently celebrated the fifteenth anniversary of the launching of the Chandra X-ray Observatory by releasing several new images. One of the images, shown below, is an amazing composite that reveals in exquisite detail the turbulence surrounding the remnants of the Tycho supernova. (Scroll down to Figure 2, then come back.)
The scientific name of the Tycho supernova remnant is SN 1572, where SN means supernova and 1572 refers to the year it was first observed. That’s right, over 400 years ago, in November 1572, many people noticed a new bright object in the sky near the constellation Cassiopeia. Reports at the time indicated that it was as bright as Venus (peak magnitude of –4) meaning it was visible during the day.
SN 1572 is called the Tycho supernova because a Danish scientist named Tycho Brahe published a paper detailing his observations. His paper is considered one of the most important in the history of astronomy, and science in the Renaissance.
Figure 1. Star map drawn by Tycho Brahe showing position of SN 1572 (labelled I) within the constellation Cassiopeia. Image from Wikipedia
What people at the time didn’t know was that SN 1572 was about 9,000 light years away, meaning it was unimaginably far away. The explosion that caused it happened long ago but the light had just reached the earth.
(Actually, SN 1572 is fairly close to us relative to the size of the Milky Way which is 100,000 light years across, and extremely close relative to the size of the observable universe which is 29 billion light years across. Space is just really unimaginably large.)
What they also didn’t know was that SN 1572 was probably a Type 1a supernova. This type of supernova is common, and has a very specific cause. It starts with a binary star system. Two stars orbit one another very closely. Over time, one of the stars consumes all of its hydrogen and dies out, leaving a carbon-oxygen core. Its gravity causes it to accrete the gas surrounding it until its mass reaches what is called the Chandrasekhar limit and it collapses. The increased pressure causes carbon fusion to start. This results in a runaway reaction, causing the star to explode.
About a supernova remnant
In the 400 years since SN1572 exploded, the debris from it has been flying away at 5,000 km/s (3100 mi/s). It is hard to see this debris. Imagine a large explosion on the earth that occurs at night.
The debris itself doesn’t generate very much light, but it does produce some. Space is not a vacuum. It is a very thin gas. When electrons from the moving debris of the supernova remnant strike a stationary particle, it gives off a photon (which depending on the energy of the collision, is seen as radio waves, microwaves, visible light, UV, or x-rays). This energy also heats up the remaining particles, releasing additional photons, making them detectable with a very sensitive telescope.
About false color images
The Chandra X-ray Observatory was launched in 1999 from the space shuttle Columbia. As the name implies, it can take digital images of objects in the x-ray range of light. Since humans cannot see in this range, images taken in the x-ray range are often color coded in the range from green to blue to purple.
Often, composite images of space objects are created using telescopes designed to capture photons from different wavelengths. For instance, visible light telescopes like the Hubble Space Telescope often have the colors in their images compressed to black and white. Images from infrared telescopes, like the Spitzer Space Telescope, and ground-based radio telescopes are often given a false color range between red to orange.
All right, finally the results. Below is the most detailed image ever of the Tycho supernova remnant. It is a composite created by layering multiple, long-exposure, high-resolution images from the Chandra X-ray Observatory. The press release says, “The outer shock has produced a rapidly moving shell of extremely high-energy electrons (blue), and the reverse shock has heated the expanding debris to millions of degrees (red and green).
“This composite image of the Tycho supernova remnant combines X-ray and infrared observations obtained with NASA’s Chandra X-ray Observatory and Spitzer Space Telescope, respectively, and the Calar Alto observatory, Spain.
“The explosion has left a blazing hot cloud of expanding debris (green and yellow) visible in X-rays. The location of ultra-energetic electrons in the blast’s outer shock wave can also be seen in X-rays (the circular blue line). Newly synthesized dust in the ejected material and heated pre-existing dust from the area around the supernova radiate at infrared wavelengths of 24 microns (red).”
Figure 2. Tycho supernova remnant composite image released in 2014. Image from NASA
Compare Figure 2 above to an image of the Tycho supernova remnant that NASA released in 2009 using data from observations made in 2003 shown below. Notice the lack of details. Also notice the large number of stars in the background, some even shining through the dust of the explosion. Apparently, the image above has been modified to eliminate most of these distractions.
These two images dated only a few years apart reveal what is likely remarkable advances in software for manipulating space images. I say that because the hardware in the telescopes themselves, such as optics, detectors, and transmitters probably have not changed much since launch. Thus, any improvements in resolution and contrast between the two images is a result of better capabilities of the software used to process images after the raw data is collected.
Figure 3. Tycho supernova remnant composite image release in 2009. Image from NASA
by George Taniwaki
Your smartphone is more than an addictive toy. With simple modifications, it can become a lifesaving medical device. The phone can already receive and send data to medical sensors and controllers wirelessly. By adding the right software, a smartphone can do a better job than a more expensive standalone hospital-grade machine.
In addition, smartphones are portable and patients can be trained to use them outside a clinical setting. The spread of smartphones has the potential to revolutionize the treatment of chronic conditions like diabetes. This can enhance the quality of life of the patient and significantly increase survival.
Monitoring blood sugar
Type 1 diabetes mellitus is an autoimmune disease in which the body attacks the pancreas and interrupts the production of insulin. Insulin is a hormone that causes the cells in the body to absorb glucose (a type of sugar) from the blood and metabolize it. Blood sugar must be controlled to a very tight range to stay healthy.
A lack of insulin after meals can lead to persistent and repeated episodes of high blood sugar, called hyperglycemia. This in turn can lead to complications such as damage to nerves, blood vessels, and organs, including the kidneys. Too much insulin can deplete glucose from the blood, a situation called hypoglycemia. This can cause dizziness, seizures, unconsciousness, cardiac arrhythmias, and even brain damage or death.
Back when I was growing up (the 1970s), patients with type 1 diabetes had to prick their finger several times a day to get a blood sample and determine if their glucose level was too low or too high. If it was too low, they had to eat a snack or meal. (But not one containing sugar.)
They would also test themselves about an hour after each meal. Often, their glucose level was too high, and they had to calculate the correct does of insulin to self-inject into their abdomen, arm, or leg to reduce it. If they were noncompliant (forgetful, busy, unable to afford the medication, fearful or distrustful of medical institutions or personnel, etc.), they would eventually undergo diabetic ketoacidosis, which often would require a hospital stay to treat.
Figure 1a. Example of blood glucose test strip. Photo from Mistry Medical
Figure 1b. Boy demonstrating how to inject insulin in his leg. Photo from Science Photo Library
If all these needle pricks and shots sound painful and tedious, they were and still are. There are better test devices available now and better insulin injectors, but they still rely on a patient being diligent and awake.
Yes, being awake is a problem. It is not realistic to ask a patient to wake up several times a night to monitoring her glucose level and inject herself with insulin. So most patients give themselves an injection just before going to bed and hope they don’t give themselves too much and that it will last all night.
Continuous glucose monitoring
Taking a blood sample seven or eight times a day is a hassle. But even then, it doesn’t provide information about how quickly or how much a patient’s glucose level changes after a meal, after exercise, or while sleeping.
More frequent measurements would be needed to estimate the rate at which a patient’s glucose level would likely rise or fall after a meal, exercise, or sleeping. Knowing the rate would allow the patient to inject insulin before the glucose level was outside the safe range or reduce the background dosage if it is too high.
In the 1980s, the first continuous glucose meters were developed to help estimate the correct background dosage of insulin and the correct additional amounts to inject after snacks and meals.
The early devices were bulky and hard to use. They consisted of a sensor that was inserted under the skin (usually in the abdomen) during a doctor visit and had wires that connected it to a monitoring device that the patient carried around her waist. The sensor reported the glucose level every five to ten seconds and the monitor had enough memory to store the average reading every five to ten minutes over the course of a week.
The devices were not very accurate and had to be calibrated using the blood prick method several times a day. The patient would also have to keep a paper diary of the times of meals, medication, snacks, exercise, and sleep. After a week, the patient would return to the doctor to have the sensor removed.
The doctor would then have to interpret the results and calculate an estimated required background dose of insulin during the day and during the night and the correct amount of additional injections after snacks and meals. The patient would repeat the process every year or so to ensure the insulin dosages were keeping the glucose levels within the desired range.
Today, continuous glucose monitors can measure glucose levels using a disposable sensor patch on the skin that will stay in place for a week. It transmits data to the monitor wirelessly. Using a keypad, the monitor can also record eating, medication, exercise, and sleeping. The monitor can store months of personal data and calculate the amount of insulin needed in real-time. Alerts remind the patient when to inject insulin and how much. They are cheap enough and portable enough that the patient never stops wearing it.
Figure 2. Wireless continuous blood glucose monitor and display device. Image from Diabetes Healthy Solutions
Continuous insulin pump
Also in the 1980s, the first generation of subcutaneous insulin pumps were commercialized. These pumps could supply a low background dose of insulin rather than big spikes provided by manual injections. The first pumps were expensive, bulky, hard to use. By the early 2000s though, insulin pumps became widely available and were shown to reliably reduce the fluctuations in glucose levels seen in patients who relied on manual injections. By providing a low dose of insulin continuously during the day and at night with the ability of the patient to manually apply larger doses after meals, it lowered the average level of glucose while also reducing the incidence of hypoglycemia. Over longer periods it also reduced the incidence of complications commonly seen with diabetes.
Figure 3a and 3b. Early insulin pump (left) and modern version (right). Images from Medtronic
There is one drawback to the continuous insulin pump. It can provide too much insulin at night while the patient is asleep. While sleeping, the patient’s glucose level falls. Since she is not performing blood tests, she will not notice that the insulin pump is set too high. Further, since she is asleep she may not realize that she is in danger, a condition called nocturnal hypoglycemia.
Software to control the pump
Imagine combining the continuous glucose meter with the continuous insulin pump. Now you have a system the mimics the behavior of the human pancreas. Sensors constantly monitor the patient’s glucose level, and anticipate changes caused by activities like eating, sleeping, and exercise.
The key is to use a well-written algorithm to predict the amount of insulin needed to be injected by the pump to keep sugar levels within the acceptable range. Instead of a human, software controls the insulin pump. If the glucose level does not stay within the desired levels, the algorithm learns its mistake and corrects it.
The initial goal of the combined monitor and pump was to predict low glucose levels while a patient was sleeping and suspend the pumping of insulin to prevent nocturnal hypoglycemia. Ironically, the US FDA panel rejected the first application submitted for the device saying that the traditional uncontrolled continuous insulin pump was actually safer than a new device because of the new device’s lack of field experience.
After years of additional studies the combined device, manufactured by Medtronic, was approved for use in the US in 2013. Results of a study involving 25 patients in the UK was published in Lancet Jun 2014. Another trial, involving 95 patients in Australia was published in J. Amer. Med. Assoc. Sept 2013.
Figure 4. Combined glucose meter and insulin pump form a bionic pancreas. Image from Medtronic
Better software and smartphones
The Medtronic combined device is proprietary. But several groups are hacking it to make improvements. For instance, researchers led by Z. Mahmoudi and M. Jensen at Aalborg University in Denmark have published several papers (Diabetes Techn Ther Jun 2014, Diabetes Sci Techn Apr 2014, Diabetes Techn Ther Oct 2013) on control algorithms that may be superior to the one currently used in the Medtronic device.
Another interesting paper appeared in the New Engl J Med Jun 2014. It reports a study by Dr. Steven Russell of Massachusetts General Hospital and his colleagues. They wrote an app for a smartphone (Apple’s iPhone 4S) that could receive the wireless data from the Medtronic glucose meter and wirelessly control the Medtronic insulin pump.
Smartphones are ideal platforms for use in developing medical devices because they can communicate wirelessly with other devices, have sufficient computing power and memory for even the most complex control tasks, are designed to be easy to program and easy to use, and many people already own one.
Dr. Russell and his colleagues used a machine learning algorithm they had previously developed (J Clin Endocrinol Metab May 2014) to couple the two.
Even though this is a research project, not a commercial product, the results are pretty impressive. The study lasted 5 days, with the first day used to calibrate the algorithm and days 2-5 as the test.
As can be seen in Figure 5, after a day of “training” patients using the bionic pancreas (solid black line) had lower average glucose levels than patients on the standard protocol (solid red line). Further, the variance of their glucose level (black shaded area) was smaller than for patients on the standard protocol (red shaded area). Notice how much better the control is using the bionic pancreas, especially at night.
Figure 5. Variation in mean glucose level among adults during 5-day study. Image from New Engl J Med
Another measure of quality is the amount of time the patients’ glucose levels were within the desired level of 70 to 120 mg/dl (the green shaded region in Figure 6). Patients with the bionic pancreas (solid black line) spent about 55% of the time within the desired level. They also had fewer incidents of hypoglycemia (pink shaded region) or hyperglycemia (white region on right) than patients using the standard protocol (red line).
Note that even with the bionic pancreas, 15% of the time patients had a glucose level above 180, so there is still plenty of room to improve control.
Figure 6. Cumulative glucose level in adults during day 1 where the bionic pancreas adapted to the patient (dashed line) and days 2-5 (solid black). Image from New Engl J Med
by George Taniwaki
Patients are often frustrated and confused when navigating the healthcare system. Part of the problem is that if you are sick or hurt, it reduces your cognitive abilities. But it also because hospitals are busy places with little funding for improving the user experience. Often the layout of the rooms, the signage, the forms and instructions, and the language used by the staff are not tailored to the needs of patients who are unfamiliar with the system.
Design to reduce patient violence
A significant problem in hospital emergency medical departments (called A&E in Britain, ER in America) is abusive and violent patients. According to the National Audit Office, violence and aggression towards hospital staff costs the NHS at least £69 million a year in staff absence, loss of productivity and additional security.
Some other statistics from the Design Council report: More than 150 incidents of violence and aggression are reported each day within the NHS system. In 2010, the incidence rate of violence and aggression was about 1 per 1000 patients. In 2009, 21% of staff report bullying, harassment, and abuse by patients, 11% report physical attacks by patients.
Working with the National Health Service, a design firm called PearsonLloyd developed some low-cost methods to reduce the incidence of violence and aggression, increase patient satisfaction, improve staff morale, and reduce security costs. They call their program, A Better A&E. The program was pilot tested at St. George’s Hospital in London and Southampton General. For an introduction, see the video below.
Figure 1. Still from video “A Better A&E. Image from Vimeo
Signage and brochure
The program consisted of three parts. First, improved signage was installed that included an estimated wait times along with a brochure that explained why a patient who arrived after you could be seen a doctor before you.
Figures 2a and 2b. Large screen monitor alternately shows how busy the A&E is and then how long the wait time is for different categories of patients. Images from Design Council report
Figure 3a and 3b. A page from brochure explaining why wait times differ among patients and what to expect at each station. Signage posted at each patient area keyed to the brochure. Images from Dezeen.com
Root cause analysis
The second part of the redesign was the introduction of program to capture information from doctors, nurses, and other staff about factors that led to violent and abusive behavior. The program included root cause analysis and a prominently posted Incident Tally Chart to record the “variables within the system that might hinder the ability of staff to deliver high quality care.”
Figure 4. Incident tally posted where staff can record any events during their shift. Images from Design Council Report
Toolkit and patterns
The final part of the program was to design a toolkit that would take the lessons from the A&E departments of the two pilot hospitals and generalize them so that they could be adopted by any hospital within the NHS system. The toolkit is presented as an easy to use website, http://www.abetteraande.com
Surveys of patients and staff taken after the redesign indicated that both groups saw benefits.
- 88% of patients felt the guidance solution was clear
- 75% of patients felt the signage reduced their frustration during waiting times
- Staff reported a 50% drop in threatening body language and aggressive behavior
- NHS calculated that each £1 spent on design solutions resulted in £3 in benefits