Kapitel 17: ROBOTIC IMPACT ON OUR HEALTH
Biorobotic Heart That Beats Like a Real One
Ellen Roche, senior author and MIT biomedical engineer, says the simulator is a great tool for researchers studying different heart valve conditions and interventions. She believes it could serve as a surgical training platform for physicians and medical students, allow engineers to study new designs, and even help patients better understand their own diseases and potential treatments.
Before new treatments are applied to humans, they undergo rigorous testing in heart simulators and animal models. However, current heart simulators do not fully reflect the complexity of the heart and have a shelf life of 2-4 hours. Animal studies are expensive and time-consuming, and the findings are not always transferable to humans. The biorobotic heart, which has a shelf life of months, could overcome these disadvantages by being a less expensive method.
The researchers focused their studies on mitral valve disease, a heart valve disease in which the valve between the left heart chambers does not close properly and blood flows backwards through the valve. This condition, which affects approximately 24.2 million people worldwide, can cause shortness of breath, swelling in the limbs, and heart failure. Given the complexity of the valve structure, the surgeries to correct the disorder are quite complex. Therefore, the need for effective technology and precise surgical techniques is of great importance. The team replaced the heart muscle in the left chamber with a soft robotic pump system made of air-powered silicone. When inflated, the system pumps artificial blood through a fake circulatory system by bending and squeezing the heart like real heart muscle, simulating the beating of a biological heart. When the researchers damaged the mitral valve in the biorobotic heart, the valve showed the characteristics of a leaky heart valve. Cardiac surgeons then repaired the damage using three different techniques: fixing the flailing valve leaflet tissue, replacing the valve with a prosthetic valve, and implanting a device to help the valve leaflet close. All three techniques were successful, restoring pressure, flow, and heart function to normal.
The system allowed the research team to collect real-time data during the surgery. The artificial blood used was clear, allowing direct visualization of the procedure. It was also observed that this system is compatible with current imaging technologies used in clinics. In the future, it is aimed to optimize the current biorobotic heart system by shortening the production time and extending the shelf life.
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A Sensor That Makes Parkinson's Patients Walk More
Parkinson's disease, which is caused by the loss of nerve cells in a part of the brain called the substantia nigra, reduces the amount of a chemical called dopamine that helps regulate movement, and often causes tremors, slow walking, and balance problems that can lead to falls. To overcome these symptoms, researchers at Physio Biometrics in Montréal, Canada, developed a sensor called Heel2Toe, which is attached to the inside of shoes. When the user walks forcefully (a movement from heel to toe by pressing on the heel of the foot), it sends a signal to a smartphone via Bluetooth, which makes a "beep" sound. To test the sensor, experts from Physio Biometrics in Montréal and McGill University followed 21 people with Parkinson's who had walking problems but could walk without a cane. All participants had five sessions with a physiotherapist and were given a workbook with tips for balanced walking. Fourteen of them were also given the HeelzToe sensor and told to clip it to their shoes while walking for at least 5 minutes twice a day. After three months, 13 of the 14 participants wearing the sensor had walked further on a six-minute walk than they had at the start of the study. None of the participants who received only physiotherapy sessions and a workbook showed a similar improvement. Forty percent of those using the sensor said they were pleased with the improvements they had made in their walking.
The team notes that the brain loves to be rewarded, and once the beep of the device after each powerful step is accustomed to, the person expects the beep, encouraging them to try harder. The researchers did not test whether the sensor changes dopamine levels in the brain, but they hope to stimulate a "dopamine-driven reward and feedback loop" that somehow compensates for the chemical depletion in the brains of people with Parkinson's.
The team says that with further research, the sensor could also be used by older people who do not have Parkinson's but have an unsteady gait that puts them at greater risk of falls and injury.
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Tiny robots made from human cells heal damaged tissue
Scientists have developed tiny robots made from human cells that can repair damaged nerve tissue. These tiny robots, called "Antrobots" and made using human tracheal cells, are thought to be used in personalized medicine in the future.
Michael Levin, a developmental biologist at Tufts University and the leader of the study, made the first robots four years ago. He and his colleagues combined embryonic heart and skin cells from the African clawed frog (Xenopus laevis) to create tiny robots that can crawl and even swim, with tiny cilia and move back and forth. However, these robots, called xenobots, have limited application in medicine because they are not derived from human cells and therefore the human immune system would reject such amphibian-based biorobots.
So, Levin's doctoral student, Gizem Gümüşkaya, began the new study with cells lining the trachea, obtained from anonymous donors of varying ages and genders.
The researchers focused on these types of cells because they are relatively easy to access because of their work on COVID-19 and lung disease, and more importantly, they believe they can be made mobile. Tracheal cells are covered in hair-like projections called cilia that sway back and forth. These projections help the cells push out small particles that usually enter the lungs' airways.
The researchers also planned to use the cells' structures as tiny paddles to power and move the organoid.
Gümüşkaya created a rat-made model of the tracheal cells, which is similar to the microenvironment in the body, where cell-cell interactions take place. After two weeks, the cells had multiplied and formed small spheres, but the cilia were inside the spheres and therefore could not be used for movement. The researchers then grew the cells in a less viscous solution for a week. This solution had certain properties that made the cilia point outward. These cilia acted like oars on a boat for the tiny spheres. The researchers found that some of the anthrobots, each containing several hundred cells, swam in straight lines, some in circles or arcs, and some moved erratically.
To test the therapeutic potential of the anthrobots, Levin and his colleagues placed a few of them in a small petri dish. There, the anthrobots came together to form a "superrobot." The researchers placed this super robot on a layer of damaged nerve tissue. The layer of neurons under the super robot healed completely within three days. Gizem Gümüşkaya says this is surprising because the antrobot cells perform this repair function without requiring any genetic modification. Levin, Gümüşkaya and their colleagues, who published their work in the journal Advanced Science on November 30, think that in the future, antrobots made from a person's own tissue, with or without genetic engineering, could be used to open blood vessels, break down mucus or deliver drugs. By combining different types of cells and exploring other stimuli, it will also be possible to develop robots made from biological materials, which have potential applications even in sustainable construction and space exploration.
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This Helmet Reads Minds!
Researchers from the GrapheneX-UTS Centre for Human-Centred Artificial Intelligence at the University of Technology Sydney (UTS) have developed a portable, non-invasive system that can decipher thoughts and convert them into text. This technology could help people who are unable to speak due to injuries or illnesses such as strokes communicate. It could also provide seamless communication between humans and machines, like a bionic arm or robot.
In the study conducted by Prof. Chin-Teng Lin, Yiqun Duan and Jinzhou Zhou, participants wore helmet-like headgear that recorded electrical brain activity from their scalps and read texts given to them. During this time, the participants' brain signals were recorded using the electroencephalography method (EEG). These recordings were then converted into text using an artificial intelligence model called DeWave. Lin says the system is far from perfect, with an accuracy rate of about 40%, but new peer-reviewed data has reached an accuracy rate exceeding 60%.
Last year, a team led by Jerry Tang at the University of Texas at Austin achieved similar accuracy in transcribing thoughts into text, but that study used magnetic resonance (MRI) scans to interpret brain activity. In contrast, MRI requires participants to lie still in a scanner. From this perspective, it is thought that using EEG is a more practical way. Team member Charles Zhou from UTS says that the DeWave AI model was trained by looking at a large number of examples where brain signals matched certain sentences. For example, when we think about saying "hello," our brain sends out certain signals. The DeWave model learns how these signals relate to the word "hello" by seeing many examples of similar signals for different words or sentences. Once DeWave had a good understanding of the brain signals, the team connected it to an open-source big language model, similar to the AI that powers ChatGPT. Zhou likens the open-source large language model to an intelligent writer who can construct sentences, and says they taught the writer to use signals from DeWave as a guide to construct sentences. Finally, the team trained both DeWave and the language model together to write more accurate sentences using EEG data.
The researchers predict that the system could be further developed to help people who have lost the ability to speak, such as those who have suffered a stroke, communicate, and could also have applications in robotics.
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