“This is a huge problem in the treatment of chronic disease,” said Kevin McHugh, corresponding author of a study about the technology published online in Advanced Materials. “It’s estimated that 50% of people don’t take their medications correctly. With this, you’d give them one shot, and they’d be all set for the next couple of months.”

When patients fail to take prescription medicine or take it incorrectly, the costs can be staggering. The annual toll in the United States alone has been estimated at more than 100,000 deaths, up to 25% of hospitalizations and more than $100 billion in healthcare costs.

Encapsulating medicine in microparticles that dissolve and release drugs over time isn’t a new idea. But McHugh and graduate student Tyler Graf used 21st-century methods to develop next-level encapsulation technology that is far more versatile than its forerunners.

Dubbed PULSED (short for Particles Uniformly Liquified and Sealed to Encapsulate Drugs), the technology employs high-resolution 3D printing and soft lithography to produce arrays of more than 300 nontoxic, biodegradable cylinders that are small enough to be injected with standard hypodermic needles.

The cylinders are made of a polymer called PLGA that’s widely used in clinical medical treatment. McHugh and Graf demonstrated four methods of loading the microcylinders with drugs, and showed they could tweak the PLGA recipe to vary how quickly the particles dissolved and released the drugs — from as little as 10 days to almost five weeks. They also developed a fast and easy method for sealing the cylinders, a critical step to demonstrate the technology is both scalable and capable of addressing a major hurdle in time-release drug delivery.

“The thing we’re trying to overcome is ‘first-order release,'” McHugh said, referring to the uneven dosing that’s characteristic with current methods of drug encapsulation. “The common pattern is for a lot of the drug to be released early, on day one. And then on day 10, you might get 10 times less than you got on day one.

“If there’s a huge therapeutic window, then releasing 10 times less on day 10 might still be OK, but that’s rarely the case,” McHugh said. “Most of the time it’s really problematic, either because the day-one dose brings you close to toxicity or because getting 10 times less — or even four or five times less — at later time points isn’t enough to be effective.”

In many cases, it would be ideal for patients to have the same amount of a drug in their systems throughout treatment. McHugh said PULSED can be tailored for that kind of release profile, and it also could be used in other ways.

“Our motivation for this particular project actually came from the vaccine space,” he said. “In vaccination, you often need multiple doses spread out over the course of months. That’s really difficult to do in low- and middle-income countries because of health care accessibility issues. The idea was, ‘What if we made particles that exhibit pulsatile release?’ And we hypothesized that this core-shell structure — where you’d have the vaccine in a pocket inside a biodegradable polymer shell — could both produce that kind of all-or-nothing release event and provide a reliable way to set the delayed timing of the release.”

Though PULSED hasn’t yet been tested for months-long release delays, McHugh said previous studies from other labs have shown PLGA capsules can be formulated to release drugs as much as six months after injection.

In their study, Graf and McHugh showed they could make and load particles with diameters ranging from 400 microns to 100 microns. McHugh said this size enables particles to stay where they are injected until they dissolve, which could be useful for delivering large or continuous doses of one or more drugs at a specific location, like a cancerous tumor.

“For toxic cancer chemotherapies, you’d love to have the poison concentrated in the tumor and not in the rest of the body,” he said. “People have done that experimentally, injecting soluble drugs into tumors. But then the question is how long is it going to take for that to diffuse out.

“Our microparticles will stay where you put them,” McHugh said. “The idea is to make chemotherapy more effective and reduce its side effects by delivering a prolonged, concentrated dose of the drugs exactly where they’re needed.”

The crucial discovery of the contactless sealing method happened partly by chance. McHugh said previous studies had explored the use of PLGA microparticles for time-released drug encapsulation, but sealing large numbers of particles had proven so difficult that the cost of production was considered impractical for many applications.

While exploring alternative sealing methods, Graf noticed that trying to seal the microparticles by dipping them into different melted polymers was not giving the desired outcome. “Eventually, I questioned whether dipping the microparticles into a liquid polymer was even necessary,” said Graf, who proceeded to suspend the PLGA microparticles above a hot plate, enabling the top of the particles to melt and to self-seal while the bottom of the particles remained intact, “Those first particle batches barely sealed, but seeing the process was possible was very exciting.”Further optimization and experimentation resulted in consistent and robust sealing of the cylinders, which eventually proved to be one of the easier steps in making the time-released drug capsules. Each 22×14 array of cylinders was about the size of a postage stamp, and Graf made them atop glass microscope slides.

After loading an array with drugs, Graf said he would suspend it about a millimeter or so above the hot plate for a short time. “I’d just flip it over and rest it on two other glass slides, one on either end, and set a timer for however long it would take to seal. It just takes a few seconds.”

This work was supported by the Cancer Prevention and Research Institute of Texas (RR190056), the National Institutes of Health (EB031495, EB023833) and the National Science Foundation (1842494, 2236422).

The discovery, described Apr. 3 in Current Biology, adds to the scientific understanding of how vision — far from being a mere absorption of incoming signals from the retina — is controlled and directed by cognitive processes.

“These eye movements are so tiny that we’re not even conscious of them, and yet our brains somehow can use the knowledge of the visual task to control them,” says study lead author Dr. Yen-Chu Lin, who carried out the work as a Fred Plum Fellow in Systems Neurology and Neuroscience in the Feil Family Brain and Mind Research Institute at Weill Cornell Medicine.

Dr. Lin works in the laboratory of study senior author Dr. Jonathan Victor, the Fred Plum Professor of Neurology at Weill Cornell Medicine.

The study involved a close collaboration with the laboratory of Dr. Michele Rucci, professor of brain and cognitive sciences and neuroscience at the University of Rochester.

Neuroscientists have known for decades that information stored in memory can strongly shape the processing of sensory inputs, including the streams of visual data coming from the eyes. In other words, what we see is influenced by what we expect to see or the requirements of the task at hand.

Most studies of cognitive control over eye movement have covered more obvious movements, such as the “saccade” movements in which the eyes dart across large parts of the visual field. In the new study, Drs. Lin and Victor and their colleagues examined ocular drift, tiny jitters of the eye that occur even when gaze seems fixed. Ocular drifts are subtle motions that shift a visual target on the retina by distances on the order of a fraction of a millimeter or so — across just a few dozen photoreceptors (cones). They are thought to improve detection of small, stationary details in a visual scene by scanning across them, effectively converting spatial details into trains of visual signals in time.

Prior studies had suggested that ocular drift and other small-scale “fixational eye movements” are under cognitive control only in a broad sense — for example, slowing when scanning across more finely detailed scenes. In the new study, the researchers found evidence for a more precise type of control.

Using sensitive equipment in Dr. Rucci’s laboratory, the researchers recorded ocular drifts in six volunteers who were asked to identify which of a pair of letters (H vs. N, or E vs. F) was being shown to them on a background of random visual noise. Based on computational modeling, the scientists expected that optimal eye movements for discriminating between letters would cross the key elements distinguishing the letters at right angles. Thus, they hypothesized that a more precise cognitive control, if it existed, would tend to direct ocular drift in both vertical and oblique (lower left to upper right) directions for the H vs. N discrimination, compared to more strictly vertical movements for the E vs. F discrimination.

They found that the subjects’ eye movements did indeed tend to follow these patterns — even in the 20 percent of trials in which the subjects, though expecting to see a letter, were shown only noise. The latter result showed that the cognitive control of ocular drift could be driven solely by specific prior knowledge of the visual task, independently of any incoming visual information.

“These results underscore the interrelationship between the sensory and the motor parts of vision — one really can’t view them separately,” said Dr. Victor, who is also a professor of neuroscience in the Feil Family Brain and Mind Research Institute at Weill Cornell.

He noted that the direction of fine eye movements is thought to come from neurons in the brainstem, whereas the task knowledge presumably resides in the upper brain: the cortex — implying some kind of non-conscious connection between them.

“The subjects are aware of the tasks they have to do, yet they don’t know that their eyes are executing these tiny movements, even when you tell them,” Dr. Victor said.

Studies of this pathway, he added, could lead to better insights not only into the neuroscience of vision, but possibly also visual disorders — which traditionally have been seen as disorders of the retina or sensory processing within the brain.

“What our findings suggest is that visual disorders may sometimes have a motor component too, since optimal vision depends on the brain’s ability to execute these very tiny movements,” Dr. Victor said.

Many robotic hands pack all their powerful sensors into the fingertips, so an object must be in full contact with those fingertips to be identified, which can take multiple grasps. Other designs use lower-resolution sensors spread along the entire finger, but these don’t capture as much detail, so multiple regrasps are often required.

Instead, the MIT team built a robotic finger with a rigid skeleton encased in a soft outer layer that has multiple high-resolution sensors incorporated under its transparent “skin.” The sensors, which use a camera and LEDs to gather visual information about an object’s shape, provide continuous sensing along the finger’s entire length. Each finger captures rich data on many parts of an object simultaneously.

Using this design, the researchers built a three-fingered robotic hand that could identify objects after only one grasp, with about 85 percent accuracy. The rigid skeleton makes the fingers strong enough to pick up a heavy item, such as a drill, while the soft skin enables them to securely grasp a pliable item, like an empty plastic water bottle, without crushing it.

These soft-rigid fingers could be especially useful in an at-home-care robot designed to interact with an elderly individual. The robot could lift a heavy item off a shelf with the same hand it uses to help the individual take a bath.

“Having both soft and rigid elements is very important in any hand, but so is being able to perform great sensing over a really large area, especially if we want to consider doing very complicated manipulation tasks like what our own hands can do. Our goal with this work was to combine all the things that make our human hands so good into a robotic finger that can do tasks other robotic fingers can’t currently do,” says mechanical engineering graduate student Sandra Liu, co-lead author of a research paper on the robotic finger.

Liu wrote the paper with co-lead author and mechanical engineering undergraduate student Leonardo Zamora Yañez and her advisor, Edward Adelson, the John and Dorothy Wilson Professor of Vision Science in the Department of Brain and Cognitive Sciences and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). The research will be presented at the RoboSoft Conference.

A human-inspired finger

The robotic finger is comprised of a rigid, 3D-printed endoskeleton that is placed in a mold and encased in a transparent silicone “skin.” Making the finger in a mold removes the need for fasteners or adhesives to hold the silicone in place.

The researchers designed the mold with a curved shape so the robotic fingers are slightly curved when at rest, just like human fingers.

“Silicone will wrinkle when it bends, so we thought that if we have the finger molded in this curved position, when you curve it more to grasp an object, you won’t induce as many wrinkles. Wrinkles are good in some ways — they can help the finger slide along surfaces very smoothly and easily — but we didn’t want wrinkles that we couldn’t control,” Liu says.

The endoskeleton of each finger contains a pair of detailed touch sensors, known as GelSight sensors, embedded into the top and middle sections, underneath the transparent skin. The sensors are placed so the range of the cameras overlaps slightly, giving the finger continuous sensing along its entire length.

The GelSight sensor, based on technology pioneered in the Adelson group, is composed of a camera and three colored LEDs. When the finger grasps an object, the camera captures images as the colored LEDs illuminate the skin from the inside.

Using the illuminated contours that appear in the soft skin, an algorithm performs backward calculations to map the contours on the grasped object’s surface. The researchers trained a machine-learning model to identify objects using raw camera image data.

As they fine-tuned the finger fabrication process, the researchers ran into several obstacles.

First, silicone has a tendency to peel off surfaces over time. Liu and her collaborators found they could limit this peeling by adding small curves along the hinges between the joints in the endoskeleton.

When the finger bends, the bending of the silicone is distributed along the tiny curves, which reduces stress and prevents peeling. They also added creases to the joints so the silicone is not squashed as much when the finger bends.

While troubleshooting their design, the researchers realized wrinkles in the silicone prevent the skin from ripping.

“The usefulness of the wrinkles was an accidental discovery on our part. When we synthesized them on the surface, we found that they actually made the finger more durable than we expected,” she says.

Getting a good grasp

Once they had perfected the design, the researchers built a robotic hand using two fingers arranged in a Y pattern with a third finger as an opposing thumb. The hand captures six images when it grasps an object (two from each finger) and sends those images to a machine-learning algorithm which uses them as inputs to identify the object.

Because the hand has tactile sensing covering all of its fingers, it can gather rich tactile data from a single grasp.

“Although we have a lot of sensing in the fingers, maybe adding a palm with sensing would help it make tactile distinctions even better,” Liu says.

In the future, the researchers also want to improve the hardware to reduce the amount of wear and tear in the silicone over time and add more actuation to the thumb so it can perform a wider variety of tasks.

This work was supported, in part, by the Toyota Research Institute, the Office of Naval Research, and the SINTEF BIFROST project.

Zachariah Reagh, an assistant professor of psychological and brain sciences in Arts & Sciences at Washington University in St. Louis, and co-author Charan Ranganath of the University of California, Davis, used functional MRI scanners to monitor the brains of subjects watching short videos of scenes that could have come from real life. These included men and women working on laptops in a cafe or shopping in a grocery store.

“They were very ordinary scenes,” Reagh said. “No car chases or anything.”

The research subjects then immediately described the scenes with as much detail as they could muster. The mundane snippets led to intriguing findings, including that different parts of the brain worked together to understand and remember a situation.

Networks in the front part of the temporal lobe, a region of the brain long known to play an important role in memory, focused on the subject regardless of their surroundings. But the posterior medial network, which involves the parietal lobe toward the back of the brain, paid more attention to the environment. Those networks then sent information to the hippocampus, Reagh explained, which combined the signals to create a cohesive scene.

Researchers had previously used very simple objects and scenarios — such as a picture of an apple on a beach — to study the different building blocks of memories, Reagh said. But life isn’t so simple, he said. “I wondered if anyone had done these types of studies with dynamic real-word situations and, shockingly, the answer was no.”

The new study suggests that the brain makes mental sketches of people that can be transposed from one location to another, much like an animator can copy and paste a character into different scenes. “It may not seem intuitive that your brain can create a sketch of a family member that it moves from place to place, but it’s very efficient,” he said.

Some subjects could recall the scenes in the café and grocery store more completely and accurately than others. Reagh and Ranganath found that those with the clearest memories used the same neural patterns when recalling scenes that they used while watching the clips. “The more you can bring those patterns back online while describing an event, the better your overall memory,” he said.

At this time, Reagh said, it’s unclear why some people seem more adept than others at reproducing the thought patterns needed to access memory. But it’s clear that many things can get in the way. “A lot can go wrong when you try to retrieve a memory,” he said.

Even memories that seem crisp and vivid may not actually reflect reality. “I tell my students that your memory is not a video camera. It doesn’t give you a perfect representation of what happened. Your brain is telling you a story,” he said.

Reagh is one of the Washington University faculty members involved in the research cluster “The Storytelling Lab: Bridging Science, Technology, and Creativity,” part of the Incubator for Transdisciplinary Futures. Led by Jeff Zacks, chair of the Department of Psychological & Brain Sciences, with Ian Bogost and Colin Burnett, the Storytelling Lab explores the psychology and neurology of narratives.

In future, Reagh plans to study the brain activity and memory of people watching more complicated stories.

“The Storytelling Lab fits perfectly with the scientific questions that I find most exciting,” Reagh said. “I want to understand how the brain creates and remembers narratives.”