The Scientist Behind the Pap Smear Saved Countless Women from Cervical Cancer
For decades, women around the world have made the annual pilgrimage to their doctor for the dreaded but potentially life-saving Papanicolaou test, a gynecological exam to screen for cervical cancer named for Georgios Papanicolaou, the Greek immigrant who developed it.
The Pap smear, as it is commonly known, is credited for reducing cervical cancer mortality by 70% since the 1960s; the American Cancer Society (ACS) still ranks the Pap as the most successful screening test for preventing serious malignancies. Nonetheless, the agency, as well as other medical panels, including the US Preventive Services Task Force and the American College of Obstetrics and Gynecology are making a strong push to replace the Pap with the more sensitive high-risk HPV screening test for the human papillomavirus virus, which causes nearly all cases of cervical cancer.
So, how was the Pap developed and how did it become the gold standard of cervical cancer detection for more than 60 years?
Born on May 13, 1883, on the island of Euboea, Greece, Georgios Papanicolaou attended the University of Athens where he majored in music and the humanities before earning his medical degree in 1904 and PhD from the University of Munich six years later. In Europe, Papanicolaou was an assistant military surgeon during the Balkan War, a psychologist for an expedition of the Oceanographic Institute of Monaco and a caregiver for leprosy patients.
When he and his wife, Andromache Mavroyenous (Mary), arrived at Ellis Island on October 19, 1913, the young couple had scarcely more than the $250 minimum required to immigrate, spoke no English and had no job prospects. They worked a series of menial jobs--department store sales clerk, rug salesman, newspaper clerk, restaurant violinist--before Papanicolaou landed a position as an anatomy assistant at Cornell University and Mary was hired as his lab assistant, an arrangement that would last for the next 50 years.
Papanikolaou would later say the discovery "was one of the greatest thrills I ever experienced during my scientific career."
In his early research, Papanikolaou used guinea pigs to prove that gender is determined by the X and Y chromosomes. Using a pediatric nasal speculum, he collected and microscopically examined vaginal secretions of guinea pigs, which revealed distinct cell changes connected to the menstrual cycle. He moved on to study reproductive patterns in humans, beginning with his faithful wife, Mary, who not only endured his almost-daily cervical exams for decades, but also recruited friends as early research participants.
Writing in the medical journal Growth in 1920, the scientist outlined his theory that a microscopic smear of vaginal fluid could detect the presence of cancer cells in the uterus. Papanikolaou would later say the discovery "was one of the greatest thrills I ever experienced during my scientific career."
At this time, cervical cancer was the number one cancer killer of American women but physicians were skeptical of these new findings. They continued to rely on biopsy and curettage to diagnose and treat the disease until Papanicolaou's discovery was published in American Journal of Obstetrics and Gynecology. An inexpensive, easy-to-perform test that could detect cervical cancer, precancerous dysplasia and other cytological diseases was a sea change. Between 1975 and 2001, the cervical cancer rate was cut in half.
Papanicolaou became Emeritus Professor at Cornell University Medical College and received numerous awards, including the Albert Lasker Award for Clinical Medical Research and the Medal of Honor from the American Cancer Society. His image was featured on the Greek currency and the US Post Office issued a commemorative stamp in his honor. But international acclaim didn't lead to a more relaxed schedule. The researcher continued to work seven days a week and refused to take vacations.
After nearly 50 years, Papanicolaou left Cornell to head and develop the Cancer Institute of Miami. He died of a heart attack on February 19, 1962, just three months after his arrival. Mary continued to work in the renamed Papanicolaou Cancer Research Institute until her death 20 years later.
The annual pap smear was originally tied to renewing a birth control prescription. Canada began recommending Pap exams every three years in 1978. The United States followed suit in 2012, noting that it takes many years for cervical cancer to develop. In September 2020, the American Cancer Society recommended delaying the first gynecological pelvic exam until age 25 and replacing the Pap test completely with the more accurate human papillomavirus (HPV) test every five years as the technology becomes more widely available.
Not everyone agrees that it's time to do away with this proven screening method, though. The incidence rate of cervical cancer among Hispanic women is 28% higher than for white women, and Black women are more likely to die of cervical cancer than any other racial or ethnicities.
Whether the Pap is administered every year, every three years or not at all, Papanicolaou will always be known as the medical hero who saved countless women who would otherwise have succumbed to cervical cancer.
The Friday Five covers five stories in research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on new scientific theories and progress to give you a therapeutic dose of inspiration headed into the weekend.
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Here are the stories covered this week:
- The eyes are the windows to the soul - and biological aging?
- What bean genes mean for health and the planet
- This breathing practice could lower levels of tau proteins
- AI beats humans at assessing heart health
- Should you get a nature prescription?
Two-and-a-half year-old Huckleberry, a blue merle Australian shepherd, pulls hard at her leash; her yelps can be heard by skiers and boarders high above on the chairlift that carries them over the ski patrol hut to the top of the mountain. Huckleberry is an avalanche rescue dog — or avy dog, for short. She lives and works with her owner and handler, a ski patroller at Breckenridge Ski Resort in Colorado. As she watches the trainer play a game of hide-and-seek with six-month-old Lume, a golden retriever and avy dog-in-training, Huckleberry continues to strain on her leash; she loves the game. Hide-and-seek is one of the key training methods for teaching avy dogs the rescue skills they need to find someone caught in an avalanche — skier, snowmobiler, hiker, climber.
Lume’s owner waves a T-shirt in front of the puppy. While another patroller holds him back, Lume’s owner runs away and hides. About a minute later — after a lot of barking — Lume is released and commanded to “search.” He springs free, running around the hut to find his owner who reacts with a great amount of excitement and fanfare. Lume’s scent training will continue for the rest of the ski season (Breckenridge plans operating through May or as long as weather permits) and through the off-season. “We make this game progressively harder by not allowing the dog watch the victim run away,” explains Dave Leffler, Breckenridge's ski patroller and head of the avy dog program, who has owned, trained and raised many of them. Eventually, the trainers “dig an open hole in the snow to duck out of sight and gradually turn the hole into a cave where the dog has to dig to get the victim,” explains Leffler.
By the time he is three, Lume, like Huckleberry, will be a fully trained avy pup and will join seven other avy dogs on Breckenridge ski patrol team. Some of the team members, both human and canine, are also certified to work with Colorado Rapid Avalanche Deployment, a coordinated response team that works with the Summit County Sheriff’s office for avalanche emergencies outside of the ski slopes’ boundaries.
There have been 19 avalanche deaths in the U.S. this season, according to avalanche.org, which tracks slides; eight in Colorado. During the entirety of last season there were 17. Avalanche season runs from November through June, but avalanches can occur year-round.
High tech and high stakes
Complementing avy dogs’ ability to smell people buried in a slide, avalanche detection, rescue and recovery is becoming increasingly high tech. There are transceivers, signal locators, ground scanners and drones, which are considered “games changers” by many in avalanche rescue and recovery
For a person buried in an avalanche, the chance of survival plummets after 20 minutes, so every moment counts.
A drone can provide thermal imaging of objects caught in a slide; what looks like a rock from far away might be a human with a heat signature. Transceivers, also known as beacons, send a signal from an avalanche victim to a companion. Signal locators, like RECCO reflectors which are often sewn directly into gear, can echo back a radar signal sent by a detector; most ski resorts have RECCO detector units.
Research suggests that Ground Penetrating Radar (GPR), an electromagnetic tool used by geophysicists to pull images from inside the ground, could be used to locate an avalanche victim. A new study from the Department of Energy’s Sandia National Laboratories suggests that a computer program developed to pinpoint the source of a chemical or biological terrorist attack could also be used to find someone submerged in an avalanche. The search algorithm allows for small robots (described as cockroach-sized) to “swarm” a search area. Researchers say that this distributed optimization algorithm can help find avalanche victims four times faster than current search mechanisms. For a person buried in an avalanche, the chance of survival plummets after 20 minutes, so every moment counts.
An avy dog in training is picking up scent
Sarah McLear
While rescue gear has been evolving, predicting when a slab will fall remains an emerging science — kind of where weather forecasting science was in the 1980s. Avalanche forecasting still relies on documenting avalanches by going out and looking,” says Ethan Greene, director of the Colorado Avalanche Information Center (CAIC). “So if there's a big snowstorm, and as you might remember, most avalanches happened during snowstorms, we could have 10,000 avalanches that release and we document 50,” says Greene. “Avalanche forecasting is essentially pattern recognition,” he adds--and understanding the layering structure of snow.
However, determining where the hazards lie can be tricky. While a dense layer of snow over a softer, weaker layer may be a recipe for an avalanche, there’s so much variability in snowpack that no one formula can predict the trigger. Further, observing and measuring snow at a single point may not be representative of all nearby slopes. Finally, there’s not enough historical data to help avalanche scientists create better prediction models.
That, however, may be changing.
Last year, an international group of researchers created computer simulations of snow cover using 16 years of meteorological data to forecast avalanche hazards, publishing their research in Cold Regions Science and Technology. They believe their models, which categorize different kinds of avalanches, can support forecasting and determine whether the avalanche is natural (caused by temperature changes, wind, additional snowfall) or artificial (triggered by a human or animal).
With smell receptors ranging from 800 million for an average dog, to 4 billion for scent hounds, canines remain key to finding people caught in slides.
With data from two sites in British Columbia and one in Switzerland, researchers built computer simulations of five different avalanche types. “In terms of real time avalanche forecasting, this has potential to fill in a lot of data gaps, where we don't have field observations of what the snow looks like,” says Simon Horton, a postdoctoral fellow with the Simon Fraser University Centre for Natural Hazards Research and a forecaster with Avalanche Canada, who participated in the study. While complex models that simulate snowpack layers have been around for a few decades, they weren’t easy to apply until recently. “It's been difficult to find out how to apply that to actual decision-making and improving safety,” says Horton. If you can derive avalanche problem types from simulated snowpack properties, he says, you’ll learn “a lot about how you want to manage that risk.”
The five categories include “new snow,” which is unstable and slides down the slope, “wet snow,” when rain or heat makes it liquidly, as well as “wind-drifted snow,” “persistent weak layers” and “old snow.” “That's when there's some type of deeply buried weak layer in the snow that releases without any real change in the weather,” Horton explains. “These ones tend to cause the most accidents.” One step by a person on that structurally weak layer of snow will cause a slide. Horton is hopeful that computer simulations of avalanche types can be used by scientists in different snow climates to help predict hazard levels.
Greene is doubtful. “If you have six slopes that are lined up next to each other, and you're going to try to predict which one avalanches and the exact dimensions and what time, that's going to be really hard to do. And I think it's going to be a long time before we're able to do that,” says Greene.
What both researchers do agree on, though, is that what avalanche prediction really needs is better imagery through satellite detection. “Just being able to count the number of avalanches that are out there will have a huge impact on what we do,” Greene says. “[Satellites] will change what we do, dramatically.” In a 2022 paper, scientists at the University of Aberdeen in England used satellites to study two deadly Himalayan avalanches. The imaging helped them determine that sediment from a 2016 ice avalanche plus subsequent snow avalanches contributed to the 2021 avalanche that caused a flash flood, killing over 200 people. The researchers say that understanding the avalanches characteristics through satellite imagery can inform them how one such event increases the magnitude of another in the same area.
Avy dogs trainers hide in dug-out holes in the snow, teaching the dogs to find buried victims
Sarah McLear
Lifesaving combo: human tech and Mother Nature’s gear
Even as avalanche forecasting evolves, dogs with their built-in rescue mechanisms will remain invaluable. With smell receptors ranging from 800 million for an average dog, to 4 billion for scent hounds, canines remain key to finding people caught in slides. (Humans in comparison, have a meager 12 million.) A new study published in the Journal of Neuroscience revealed that in dogs smell and vision are connected in the brain, which has not been found in other animals. “They can detect the smell of their owner's fingerprints on a glass slide six weeks after they touched it,” says Nicholas Dodman, professor emeritus at Cummings School of Veterinary Medicine at Tufts University. “And they can track from a boat where a box filled with meat was buried in the water, 100 feet below,” says Dodman, who is also co-founder and president of the Center for Canine Behavior Studies.
Another recent study from Queens College in Belfast, United Kingdom, further confirms that dogs can smell when humans are stressed. They can also detect the smell of a person’s breath and the smell of the skin cells of a deceased person.
The emerging avalanche-predicting human-made tech and the incredible nature-made tech of dogs’ olfactory talents is the lifesaving “equipment” that Leffler believes in. Even when human-made technology develops further, it will be most efficient when used together with the millions of dogs’ smell receptors, Leffler believes. “It is a combination of technology and the avalanche dog that will always be effective in finding an avalanche victim.”