The Nose Knows: Dogs Are Being Trained to Detect the Coronavirus
Asher is eccentric and inquisitive. He loves an audience, likes keeping busy, and howls to be let through doors. He is a six-year-old working Cocker Spaniel, who, with five other furry colleagues, has now been trained to sniff body odor samples from humans to detect COVID-19 infections.
As the Delta variant and other new versions of the SARS-CoV-2 virus emerge, public health agencies are once again recommending masking while employers contemplate mandatory vaccination. While PCR tests remain the "gold standard" of COVID-19 tests, they can take hours to flag infections. To accelerate the process, scientists are turning to a new testing tool: sniffer dogs.
At the London School of Hygiene and Tropical Medicine (LSHTM), researchers deployed Asher and five other trained dogs to test sock samples from 200 asymptomatic, infected individuals and 200 healthy individuals. In May, they published the findings of the yearlong study in a preprint, concluding that dogs could identify COVID-19 infections with a high degree of accuracy – they could correctly identify a COVID-positive sample up to 94% of the time and a negative sample up to 92% of the time. The paper has yet to be peer-reviewed.
"Dogs can screen lots of people very quickly – 300 people per dog per hour. This means they could be used in places like airports or public venues like stadiums and maybe even workplaces," says James Logan, who heads the Department of Disease Control at LSHTM, adding that canines can also detect variants of SARS-CoV-2. "We included samples from two variants and the dogs could still detect them."
Detection dogs have been one of the most reliable biosensors for identifying the odor of human disease. According to Gemma Butlin, a spokesperson of Medical Detection Dogs, the UK-based charity that trained canines for the LSHTM study, the olfactory capabilities of dogs have been deployed to detect malaria, Parkinson's disease, different types of cancers, as well as pseudomonas, a type of bacteria known to cause infections in blood, lungs, eyes, and other parts of the human body.
COVID-19 has a distinctive smell — a result of chemicals known as volatile organic compounds released by infected body cells, which give off an odor "fingerprint."
"It's estimated that the percentage of a dog's brain devoted to analyzing odors is 40 times larger than that of a human," says Butlin. "Humans have around 5 million scent receptors dedicated to smell. Dogs have 350 million and can detect odors at parts per trillion. To put this into context, a dog can detect a teaspoon of sugar in a million gallons of water: two Olympic-sized pools full."
According to LSHTM scientists, COVID-19 has a distinctive smell — a result of chemicals known as volatile organic compounds released by infected body cells, which give off an odor "fingerprint." Other studies, too, have revealed that the SARS-CoV-2 virus has a distinct olfactory signature, detectable in the urine, saliva, and sweat of infected individuals. Humans can't smell the disease in these fluids, but dogs can.
"Our research shows that the smell associated with COVID-19 is at least partly due to small and volatile chemicals that are produced by the virus growing in the body or the immune response to the virus or both," said Steve Lindsay, a public health entomologist at Durham University, whose team collaborated with LSHTM for the study. He added, "There is also a further possibility that dogs can actually smell the virus, which is incredible given how small viruses are."
In April this year, researchers from the University of Pennsylvania and collaborators published a similar study in the scientific journal PLOS One, revealing that detection dogs could successfully discriminate between urine samples of infected and uninfected individuals. The accuracy rate of canines in this study was 96%. Similarly, last December, French scientists found that dogs were 76-100% effective at identifying individuals with COVID-19 when presented with sweat samples.
Grandjean Dominique, a professor at France's National Veterinary School of Alfort, who led the French study, said that the researchers used two types of dogs — search and rescue dogs, as they can sniff sweat, and explosive detection dogs, because they're often used at airports to find bomb ingredients. Dogs may very well be as good as PCR tests, said Dominique, but the goal, he added, is not to replace these tests with canines.
In France, the government gave the green light to train hundreds of disease detection dogs and deploy them in airports. "They will act as mass pre-test, and only people who are positive will undergo a PCR test to check their level of infection and the kind of variant," says Dominique. He thinks the dogs will be able to decrease the amount of PCR testing and potentially save money.
Since the accuracy rate for bio-detection dogs is fairly high, scientists think they could prove to be a quick diagnosis and mass screening tool, especially at ports, airports, train stations, stadiums, and public gatherings. Countries like Finland, Thailand, UAE, Italy, Chile, India, Australia, Pakistan, Saudi Arabia, Switzerland, and Mexico are already training and deploying canines for COVID-19 detection. The dogs are trained to sniff the area around a person, and if they find the odor of COVID-19 they will sit or stand back from an individual as a signal that they've identified an infection.
While bio-detection dogs seem promising for cheap, large-volume screening, many of the studies that have been performed to date have been small and in controlled environments. The big question is whether this approach work on people in crowded airports, not just samples of shirts and socks in a lab.
"The next step is 'real world' testing where they [canines] are placed in airports to screen people and see how they perform," says Anna Durbin, professor of international health at the John Hopkins Bloomberg School of Public Health. "Testing in real airports with lots of passengers and competing scents will need to be done."
According to Butlin of Medical Detection Dogs, scalability could be a challenge. However, scientists don't intend to have a dog in every waiting room, detecting COVID-19 or other diseases, she said.
"Dogs are the most reliable bio sensors on the planet and they have proven time and time again that they can detect diseases as accurately, if not more so, than current technological diagnostics," said Butlin. "We are learning from them all the time and what their noses know will one day enable the creation an 'E-nose' that does the same job – imagine a day when your mobile phone can tell you that you are unwell."
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.