Anyone with a Computer Can Join the Fight Against COVID-19 Right Now
With millions of people left feeling helpless as COVID-19 sweeps across the U.S. and the rest of the planet, there is one way in which absolutely anyone can help fight the pandemic -- all you need is a computer and an Internet connection.
"The more donors that participate, the more science we're able to do."
The Folding@home project allows members of the public to contribute a portion of their computing power to a gigantic virtual network which has mushroomed over the past month to become the most powerful supercomputer on the planet.
As of April 6, more than one million people across the globe have donated some of their home computing resources to the project. Combined, this gives Folding@home processing powers that dwarf even NASA and IBM's most powerful devices. To join, all you have to do is go to this website and click 'Download Now' to load the Folding@home software on your computer. This runs in the background, and only adds your unused computing power to the project, so it will not drain resources from tasks you're trying to do.
"It's totally crazy," said Vincent Voelz, associate professor of chemistry at Temple University, Philadelphia, and one of the scientists leading the project. "A month ago, we had around 30,000 to 40,000 participants. And then last week, it rose up 400,000 and now we've hit a million. But the more donors that participate, the more science we're able to do."
Voelz and the other scientists behind Folding@home are using these vast resources to model the ever-changing shapes of the coronavirus's proteins, in the hopes of identifying vulnerabilities or 'pockets' in its structure that can be targeted with new drugs.
One of the reasons it's difficult to find treatments for viruses like COVID-19 and Ebola is because the proteins, the innate building blocks of the viral structure, have notoriously smooth surfaces, making it hard for drugs to bind to them.
But viral proteins don't stay still. They are constantly evolving and changing shape as the atoms within push and pull against each other. Having a supercomputer enables scientists to simulate all these different shapes, revealing potential weaknesses which were not immediately visible. And the more powerful the supercomputer, the faster these simulations can happen.
"Simulating these protein motions also enables us to answer basic questions such as what makes this new coronavirus strain different from previous strains," said Voelz. "Is there something about the dynamics of these proteins that makes it more virulent?"
Finding a genuinely novel drug for COVID-19 is particularly critical.
Once they have identified suitable pockets within the proteins of COVID-19, the Folding@home scientists can then take the many compounds being identified by chemists around the world as potential drugs, and try to predict which ones will stand the best chance of binding to those pockets and inhibiting the virus's ability to invade and take over human cells.
"We have so much bandwidth now with Folding@home that we really think we can make a dent with screening these, and prioritizing which compounds are then going to get experimentally tested," said Voeltz.
The team are particularly hopeful they can succeed, having already used the supercomputer to identify a new vulnerability in the Ebola virus, which could go on to yield a new treatment for the disease.
Finding a genuinely novel drug for COVID-19 is particularly critical. While researchers are also looking at repurposing existing medications, like the antimalarials Hydroxychloroquine and Chloroquine (which have just been approved by the FDA for emergency use in coronavirus patients), concerns remain about the safety of these treatments. Researchers at the Mayo Clinic recently warned that the use of these drugs could have the side effect of inducing heart problems and run the risk of sudden cardiac arrest.
But with the death toll increasing by the day, speed is of the essence. Voelz explains that the scientific community has been left playing catch-up, because a drug was never actually developed for the original SARS outbreak in the early 2000s. The enormous computational power of the Folding@home project has the potential to allow scientists to quickly answer some of the key questions needed to get a new treatment into the pipeline.
"We don't have a SARS drug for whatever reason," said Voelz. "So the missing ingredient really, is the basic science to reveal possible drug targets and then the pharma can take that information and do the engineering work and optimizing and clinically testing drugs. But we now have a lot of basic science going on in response to this pandemic."
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.