Nearly 85 percent of babies born before 34 weeks stop breathing for 20 seconds or more, often because their undeveloped nervous systems fail to signal their lungs. That can be fatal. The babies are typically hooked up to monitors, but sometimes the systems fail to sound the alarm—and Juan Sebastián Osorio discovered why.

Osorio, then a biomedical-­engineering student with the Antioquia School of Engineering and CES University in Medellín, Colombia, realized that the sensors used on the infants were poorly adapted to their small size. Electrodes are placed on either side of the infant’s chest to watch for stoppages in motion. But the tiny chests move so little that the monitor can mistake heartbeats for breathing motions long after respiration has stopped.

Osorio and colleagues came up with a prototype detector attuned to the rhythms of infant physiology. The monitor combines heart rate recordings, electrical signals from the diaphragm muscle, and blood oxygen measurements for a potentially more precise and reliable way to measure a baby’s breathing. Eventually the device could predict the risk of apnea by analyzing the measurements along with information about the baby’s weight and gestational age. Osorio says that could help hospitals discharge low-risk babies earlier, saving costs and sparing the babies from extended ICU stays.

Osorio is testing his system and seeking to license it commercially. He’s integrating it with a mobile-phone app he developed that helps parents recognize signs of risk for sudden infant death syndrome. Next he plans to couple his detector with a video camera to make it easier for parents to monitor babies at high risk for apnea. If a problem comes up, the system will connect to pediatricians remotely.

Tests to detect rheumatoid arthritis, lupus, and other autoimmune diseases can cost hundreds of dollars and take days, and they aren’t always accurate. To address those shortcomings, Ryan Bailey, a chemist at the University of Illinois, developed a silicon testing chip that fuses optical sensor technology with semiconductor fabrication methods.

Bailey’s chip is faster and more sensitive than many other optical tests, which typically look for color changes or fluorescence in response to telltale proteins. And it also outperforms many tests that detect changes in the electric charge of proteins and DNA.

The device can detect almost vanishingly small concentrations of proteins in 10 minutes or less—which means test results can be put to clinical use during an office visit. For most assays, samples can be placed on the chip without any of the preparation required in current systems, making the test easy to run with little training. And at about one dollar per test, it costs a fraction as much as most others.

Each silicon chip has an array of 30-micrometer-wide rings. Each ring can be coated with a molecular trap for a different protein, gene, or biomarker. If light of a certain wavelength shines onto the empty rings, it will resonate and appear brighter to an optical scanner positioned over the chip. When a sample is washed over the chip, any sought-after molecules in the sample will be trapped on the rings—and the change causes the light to resonate at a different wavelength. The wavelength also varies with the amount of trapped material.

In 2007 Bailey helped launch a company called Genalyte; it recently introduced its first diagnostic assay for connective-tissue autoimmune diseases, with a focus on lupus.

The company is also working on applications of the technology in diagnostics for cancer and for cardiovascular and neurodegenerative disease. It is currently producing chips with 128 rings, but Bailey expects the number to go up. His group is also working to simultaneously detect two different kinds of biological molecules on a single chip, such as a protein and an RNA molecule.

Robotic limbs are usually packed with multiple powerful motors, making them heavy and bulky. Engineer Ken Endo hit on an idea for lightening and streamlining the limbs: replacing some of the motors with a series of springs. His goal isn’t to build better robots; rather, he wants to make prosthetic limbs and orthopedic devices that can, as he puts it, “eradicate disability.” He hopes to make artificial limbs that function nearly as well as real ones, affording amputees near-effortless motion with no discomfort.

Endo had been focused on building more advanced robots until about seven years ago, when he found himself moved by the determination of a friend who had lost his legs to bone cancer. “He said he wanted to walk by himself,” Endo says. “That’s when I changed my research focus from robots to biomechanics.”

As a PhD student working in the MIT Media Lab’s Biomechatronics Group, led by Hugh Herr, Endo created the first computer program that closely simulates human walking, a surprisingly complex motion. Now back in his native Japan as a researcher with Sony, he’s enlisting that model to build legs with spring-based ankle and knee joints that he says work much like the real things. “The ankle joint also requires a motor,” he notes, “because the human ankle generates a huge amount of mechanical power.” But most of the work will be done by the springs, he says, making the legs far more efficient and leaving the wearer less tired and sore. Endo is now perfecting his joints on a walking robot. He hopes to have the bugs smoothed out in mere months, at which point he’ll start working to make the device suitable for amputees.

Another big challenge Endo has taken on is making prostheses affordable. More than half of all amputees live in poor countries, where many are victims of land mines. The price tag of $35,000 or more for a high-quality prosthetic leg in the United States is far out of reach for the vast majority of these amputees.

To address that, Endo has been working to design prostheses specifically for people in developing countries and to find ways to distribute them there. He has already achieved one breakthrough: a leg costing about $30 whose knee joint can bend when the leg is lifted off the ground but locks into place when the leg is weighted, leading to a less effortful, more natural-looking gait.

There’s never been a great way to safely and accurately test what’s going on in the womb. The mother’s bloodstream contains some fetal cells, but not many of them, so a maternal blood sample rarely yields enough for a useful analysis. Now Christina Fan has come up with an approach to measuring the chromosomes and genes in the fetus without having to isolate the fetal cells, enabling her to develop tests for Down syndrome and a range of inherited and other conditions.

While still a graduate student in bioengineering at Stanford, Fan developed a DNA sequencing technique as well as an algorithm for estimating how many of certain chromosomes—such as chromosome 21, the one implicated in Down syndrome—should be present in a sample of the mother’s blood if the fetus is contributing the expected number. Any excess in the sample means the fetus has more than the normal number of the chromosome, indicating that the child is likely to have Down syndrome. There are other blood tests for this condition, which affects cognitive and physical development, but these tests are much less accurate. There are also more accurate tests performed on fluid drawn from the amniotic sac, but collecting this fluid carries a small chance of triggering a miscarriage.

Fan realized that to expand her work to other inherited conditions, she had to go beyond simply counting chromosomes and look at the genes associated with those conditions. She was able to adapt her chromosome technique for these other conditions by calculating, from an analysis of both the mother’s and father’s cells, how much of a certain type of gene ought to turn up in a sample of the mother’s blood. If the sample contains higher levels than expected, the excess is coming from the fetus. “We used this method to build the entire inherited fetal genome from maternal blood,” she explains.

Some of the conditions that are detectable this way can be prevented from causing problems if they’re treated promptly at birth. The metabolic disorder phenylketonuria, for example, can be managed through diet if that begins when the patient is a newborn.

People studying public-health issues must cope with surprisingly shoddy data. Plenty of numbers are available, but epidemiologists and policy makers often don’t trust them, because they are frequently incomplete, inconsistent, and inaccurate. “When I came to global health, I was shocked by how little we knew,” says mathematician Abraham Flaxman, an assistant professor of global health at the Institute for Health Metrics and Evaluation at the University of Washington.

In response, Flaxman has developed improved models and algorithms that can automatically fill in the gaps in flawed health data. His breakthrough approach, which is now widely used, came from a realization that improving the quality of a large data set requires not just analyzing it on its own but also cross-analyzing it against other relevant data sets that have at least some variables in common.

Flaxman started off as a postdoctoral fellow with Microsoft Research’s Theory Group, where he studied complex networks, but he soon yearned to apply his mathematical and modeling skills to big health problems. When he made the jump to academia, he immediately discovered that public health was beset by serious data problems, and he began trying to address those problems.

Flaxman is using his methodology to track the spread and treatment of a wide range of diseases. His latest model, called DisModIII, starts with all the available data on the incidence and mortality of a specific disease. It then integrates and cross-analyzes the data to produce consistent estimates of the way the disease progresses through a population as a function of age, time, gender, and geography.

About 800 researchers use DisModIII to track more than 140 diseases, including hepatitis B and cholera. Researchers and policy makers had long dealt with data that had to be taken on faith and data analysis tools that were unique to each disease. Applying Flaxman’s one tool to different data sets covering many different diseases provides credible, apples-to-apples comparisons of their relative impact. That helps policy makers direct health funds to the interventions likely to save the most lives.

Flaxman continues to come across new areas in which his modeling approaches can play a pivotal role. One of them is determining causes of death. Death certificates in developing countries are often incomplete or inaccurate—if they exist at all. The conventional approach is to collect information about symptoms and other matters from relatives and friends of the deceased person, and have a physician review the results to make an educated guess—a labor-intensive  technique called a “verbal autopsy.”

Flaxman created a computer program that examined available information about a wide range of deceased people whose causes of death were known in order to come up with accurate correlations between observations and causes. Now the software can determine causes of death far more cheaply than physicians can conduct verbal autopsies, getting it right more often as well.

Flaxman wants to do much more to improve health data; he’s driven by the knowledge that the policy and public-health decisions informed by his tools are matters of life and death on a large scale. “These are data that really matter,” he says.

Pulling medical tape off newborn babies in hospitals can be extremely painful and even potentially dangerous. To find something safer, Bryan Laulicht, a postdoctoral fellow at Harvard University and MIT, tested dozens of adhesive materials commonly used in medicine. He soon discovered that the adhesives fell into two groups: those that stuck securely and those that could be removed painlessly. None of them met both criteria.

But Laulicht knew that evolution had long since solved the problem. The feet of the gecko, for example, sport pads that adhere strongly to surfaces for climbing, but when rotated in a certain way, the pads release easily so the animal can run. Convinced that an artificial material ought to be able to do the same, ­Laulicht hunted for a way to fabricate it.

Using existing adhesives and a new quick-release backing layer, Laulicht developed a dry adhesive, suitable for bandages and medical tape, that was inspired by the gecko’s feet. Though he won’t give more details before the results are published, he says that he and colleagues are gearing up to test his creation on humans.

Newborns are the immediate intended beneficiaries of the adhesive technology, but Laulicht says elderly patients and others with sensitive or injured skin need it, too. Because the adhesive is based mostly on materials found in existing types of tape, he hopes his bandage will find its way to the clinic quickly.

There are surprisingly few ways to directly observe how cells and proteins work inside living creatures. Weian Zhao devised simple sensors that let scientists do exactly that.

Zhao starts by identifying a short, single-stranded piece of DNA called an aptamer that selectively binds with a protein or other biomolecule researchers are interested in. He attaches a fluorescent dye to the aptamer and then attaches the aptamer-dye combination to the surface of a type of stem cell, found in bone marrow and fat tissue, that homes in on inflamed tissue and tumors.

When the combination of dye, aptamer, and stem cell is injected into a living organism, the stem cell seeks out the targeted biomolecules. For example, if researchers want to look at unhealthy tissue, the aptamer latches onto the biomolecule suspected of being at the root of the problem, and the dye lights up or changes color.

By putting mice that have been injected with these sensors under a special microscope designed to hold a living animal and spot fluorescent dye, Zhao can see where in the organism the dye ends up. He can observe the action down to the level of individual cells, and he can even watch in real time how the biomolecular traffic is altered by the presence of drugs or by other changes in the organism. That’s never before been possible.

Zhao’s lab is working on a way to rapidly create vast libraries of aptamers that bind to almost any molecule. He foresees scientists using these libraries to build a collection of cellular sensors not only for use in basic research but also to improve the drug discovery process. At present, drug discovery suffers because what happens in cell cultures rarely duplicates what happens in living animals, sometimes misleading researchers and wasting time. Zhao’s sensor will allow scientists instead to immediately observe what the drug does inside animals, which can help speed a promising drug toward human trials.

Zhao is also currently working toward getting his stem-cell-based sensors to bind to various cancer markers found in whole blood, in the hope of developing a faster, less expensive, and potentially more accurate diagnostic tool that could even in many cases eliminate the need for a biopsy. He figures that if the work pans out, some of these tests could be on the market within as little as five years.