MIT researchers have developed a new bionic knee that is integrated directly with the user’s muscle and bone tissue. It can help people with above-the-knee amputations walk faster, climb stairs, and avoid obstacles more easily than they could with a traditional prosthesis, which is attached to the residual limb by means of a socket and can be uncomfortable.

For several years, Hugh Herr, SM ’93, co-director of the K. Lisa Yang Center for Bionics, has been working with his colleagues on techniques that can extract neural information from muscles left behind after an amputation and use that information to help guide a prosthetic limb. The approach, known as agonist-antagonist myoneuronal interface (AMI), has been shown to help people with below-the-knee amputations walk faster and navigate around obstacles much more naturally. 

COURTESY OF THE RESEARCHERS

In the new study, the researchers developed a procedure to insert a titanium rod into the residual femur bone of people who had amputations above the knee. This implant allows for better mechanical control and load bearing than a traditional prosthesis. It also contains 16 wires that collect information from electrodes located on the AMI muscles inside the body, offering better neuroprosthetic control.

Two people who received the implant in a clinical study performed better on several types of tasks, including stair climbing, and reported that the limb felt more like a part of their own body, compared with people who had more traditional above-the-knee amputations and used conventional prostheses.

“A prosthesis that’s tissue-integrated—anchored to the bone and directly controlled by the nervous system—is not merely a lifeless, separate device,” says Herr, but rather “an integral part of self.” The system will need larger trials to receive FDA approval for commercial use, which he expects may take about five years. 

With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat bacteria: multi-drug-­resistant Neisseria gonorrhoeae and Staphylococcus aureus (MRSA).

The team used two approaches. First, they directed generative AI to design molecules based on a chemical fragment their model had predicted would show antimicrobial activity, and second, they let the algorithms generate molecules without constraints. They designed more than 36 million possible compounds this way and computationally screened them for antimicrobial properties. 

The top candidates they discovered are structurally distinct from any existing antibiotics, and they appear to work by novel mechanisms that disrupt bacterial cell membranes. This makes them less vulnerable to antibiotic resistance, a growing problem: It is estimated that drug-­resistant bacterial infections cause nearly 5 million deaths per year worldwide.

Now that they can generate and evaluate compounds that have never been seen before, the researchers hope they can use the same strategy to identify and design drugs that attack other species of bacteria.

“We’re excited about the new possibilities that this project opens up for antibiotics development,” says James Collins, a professor of biological engineering and the senior author of the study. “Our work shows the power of AI from a drug design standpoint and enables us to exploit much larger chemical spaces that were previously inaccessible.”