“Coming Back from the Dead” Is No Longer Science Fiction
Last year, there were widespread reports of a 53-year-old Frenchman who had suffered a cardiac arrest and "died," but was then resuscitated back to life 18 hours after his heart had stopped.
The once black-and-white line between life and death is now blurrier than ever.
This was thought to have been possible in part because his body had progressively cooled down naturally after his heart had stopped, through exposure to the outside cold. The medical team who revived him were reported as being "stupefied" that they had been able to bring him back to life, in particular since he had not even suffered brain damage.
Interestingly, this man represents one of a growing number of extraordinary cases in which people who would otherwise be declared dead have now been revived. It is a testament to the incredible impact of resuscitation science -- a science that is providing opportunities to literally reverse death, and in doing so, shedding light on the age-old question of what happens when we die.
Death: Past and Present
Throughout history, the boundary between life and death was marked by the moment a person's heart stopped, breathing ceased, and brain function shut down. A person became motionless, lifeless, and was deemed irreversibly dead. This is because once the heart stops beating, blood flow stops and oxygen is cut off from all the body's organs, including the brain. Consequently, within seconds, breathing stops and brain activity comes to a halt. Since the cessation of the heart literally occurs in a "moment," the philosophical notion of a specific point in time of "irreversible" death still pervades society today. The law, for example, relies on "time of death," which corresponds to when the heart stops beating.
The advent of cardiopulmonary resuscitation (CPR) in the 1960s was revolutionary, demonstrating that the heart could potentially be restarted after it had stopped, and what had been a clear black-and-white line was shown to be potentially reversible in some people. What was once called death—the ultimate end point— was now widely called cardiac arrest, and became a starting point.
From then on, it was only if somebody had requested not to be resuscitated or when CPR was deemed to have failed that people would be declared dead by "cardiopulmonary criteria." Biologically, cardiac arrest and death by cardiopulmonary criteria are the same process, albeit marked at different points in time depending on when a declaration of death is made.
The apparent irreversibility of death as we know it may not necessarily reflect true irretrievable cellular damage inside the body.
Clearly, contrary to many people's perceptions, cardiac arrest is not a heart attack; it is the final step in death irrespective of cause, whether it be a stroke, a heart attack, a car accident, an overwhelming infection or cancer. This is how roughly 95 percent of the population are declared dead.
The only exception is the small proportion of people who may have suffered catastrophic brain injuries, but whose hearts can be artificially kept beating for a period of time on life-support machines. These people can be legally declared dead based on brain death criteria before their hearts have stopped. This is because the brain can die either from oxygen starvation after cardiac arrest or from massive trauma and internal bleeding. Either way, the brain dies hours or possibly longer after these injuries have taken place and not just minutes.
A Profound Realization
What has become increasingly clear is that the apparent irreversibility of death as we know it may not necessarily reflect true irretrievable cellular damage inside the body. This is consistent with a mounting understanding: it is only after a person actually dies that the cells in the body start to undergo their own process of death. Intriguingly, this process is something that can now be manipulated through medical intervention. Being cold is one of the factors that slows down the rate of cellular decay. The 53-year-old Frenchman's case and the other recent cases of resuscitation after prolonged periods of time illustrate this new understanding.
Last week's earth-shattering announcement by neuroscientist Dr. Nenad Sestan and his team out of Yale, published in the prestigious scientific journal Nature, provides further evidence that a time gap exists between actual death and cellular death in cadavers. In this seminal study, these researchers were able to restore partial function in pig brains four hours after their heads were severed from their bodies. These results follow from the pioneering work in 2001 of geneticist Fred Gage and colleagues from the Salk Institute, also published in Nature, which demonstrated the possibility of growing human brain cells in the laboratory by taking brain biopsies from cadavers in the mortuary up to 21 hours post-mortem.
The once black-and-white line between life and death is now blurrier than ever. Some people may argue this means these humans and pigs weren't truly "dead." However, that is like saying the people who were guillotined during the French Revolution were also not dead. Clearly, that is not the case. They were all dead. The problem is not death; it's our reliance on an outdated philosophical, rather than biological, notion of death.
Death can no longer be considered an absolute moment but rather a process that can be reversed even many hours after it has taken place.
But the distinction between irreversibility from a medical perspective and biological irreversibility may not matter much from a pragmatic perspective today. If medical interventions do not exist at any given time or place, then of course death cannot be reversed.
However, it is crucial to distinguish between biologically and medically: When "irreversible" loss of function arises due to inadequate treatment, then a person could be potentially brought back in the future when an alternative therapy becomes available, or even today if he or she dies in a location where novel treatments can slow down the rate of cell death. However, when true irreversible loss of function arises from a biological perspective, then no treatment will ever be able to reverse the process, whether today, tomorrow, or in a hundred years.
Probing the "Grey Zone"
Today, thanks to modern resuscitation science, death can no longer be considered an absolute moment but rather a process that can be reversed even many hours after it has taken place. How many hours? We don't really know.
One of the wider implications of our medical advances is that we can now study what happens to the human mind and consciousness after people enter the "grey zone," which marks the time after the heart stops, but before irreversible and irretrievable cell damage occurs, and people are then brought back to life. Millions have been successfully revived and many have reported experiencing a unique, universal, and transformative mental state.
Were they "dead"? Yes, according to all the criteria we have ever used. But they were able to be brought back before their "dead" bodies had reached the point of permanent, irreversible cellular damage. This reflects the period of death for all of us. So rather than a "near-death experience," I prefer a new terminology to describe these cases -- "an actual-death experience." These survivors' unique experiences are providing eyewitness testimonies of what we will all be likely to experience when we die.
Such an experience reportedly includes seeing a warm light, the presence of a compassionate perfect individual, deceased relatives, a review of their lives, a judgment of their actions and intentions as they pertain to their humanity, and in some cases a sensation of seeing doctors and nurses working to resuscitate them.
Are these experiences compatible with hallucinations or illusions? No -- in part, because these people have described real, verifiable events, which, by definition are not hallucinations, and in part, because their experiences are not compatible with confused and delirious memories that characterize oxygen deprivation.
The challenge for us scientifically is understanding how this is possible at a time when all our science tells us the brain shuts down.
For instance, it is hard to classify a structured meaningful review of one's life and one's humanity as hallucinatory or illusory. Instead, these experiences represent a new understanding of the overall human experience of death. As an intensive care unit physician for more than 10 years, I have seen numerous cases where these reports have been corroborated by my colleagues. In short, these survivors have been known to come back with reports of full consciousness, with lucid, well-structured thought processes and memory formation.
The challenge for us scientifically is understanding how this is possible at a time when all our science tells us the brain shuts down. The fact that these experiences occur is a paradox and suggests the undiscovered entity we call the "self," "consciousness," or "psyche" – the thing that makes us who we are - may not become annihilated at the point of so-called death.
At New York University, the State University of New York, and across 20 hospitals in the U.S. and Europe, we have brought together a new multi-disciplinary team of experts across many specialties, including neurology, cardiology, and intensive care. Together, we hope to improve cardiac arrest prevention and treatment, as well as to address the impact of new scientific discoveries on our understanding of what happens at death.
One of our first studies, Awareness during Resuscitation (AWARE), published in the medical journal Resuscitation in 2014, confirmed that some cardiac arrest patients report a perception of awareness without recall; others report detailed memories and experiences; and a few report full auditory and visual awareness and consciousness of their experience, from a time when brain function would be expected to have ceased.
While you probably have some opinion or belief about this based upon your own philosophical, religious, or cultural background, you may not realize that exploring what happens when we die is now a subject that science is beginning to investigate.
There is no question more intriguing to humankind. And for the first time in our history, we may finally uncover some real answers.
Two years, six million deaths and still counting, scientists are searching for answers to prevent another COVID-19-like tragedy from ever occurring again. And it’s a gargantuan task.
Our disturbed ecosystems are creating more favorable conditions for the spread of infectious disease. Global warming, deforestation, rising sea levels and flooding have contributed to a rise in mosquito-borne infections and longer tick seasons. Disease-carrying animals are in closer range to other species and humans as they migrate to escape the heat. Bats are thought to have carried the SARS-CoV-2 virus to Wuhan, either directly or through another host animal, but thousands of novel viruses are lurking within other wild creatures.
Understanding how climate change contributes to the spread of disease is critical in predicting and thwarting future calamities. But the problem is that predictive models aren’t yet where they need to be for forecasting with certainty beyond the next year, as we could for weather, for instance.
The association between climate and infectious disease is poorly understood, says Irina Tezaur, a computational scientist at Sandia National Laboratories. “Correlations have been observed but it’s not known if these correlations translate to causal relationships.”
To make accurate longer-term predictions, scientists need more empirical data, multiple datasets specific to locations and diseases, and the ability to calculate risks that depend on unpredictable nature and human behavior. Another obstacle is that climate scientists and epidemiologists are not collaborating effectively, so some researchers are calling for a multidisciplinary approach, a new field called Outbreak Science.
Climate scientists are far ahead of epidemiologists in gathering essential data.
Earth System Models—combining the interactions of atmosphere, ocean, land, ice and biosphere—have been in place for two decades to monitor the effects of global climate change. These models must be combined with epidemiological and human model research, areas that are easily skewed by unpredictable elements, from extreme weather events to public environmental policy shifts.
“There is never just one driver in tracking the impact of climate on infectious disease,” says Joacim Rocklöv, a professor at the Heidelberg Institute of Global Health & Heidelberg Interdisciplinary Centre for Scientific Computing in Germany. Rocklöv has studied how climate affects vector-borne diseases—those transmitted to humans by mosquitoes, ticks or fleas. “You need to disentangle the variables to find out how much difference climate makes to the outcome and how much is other factors.” Determinants from deforestation to population density to lack of healthcare access influence the spread of disease.
Even though climate change is not the primary driver of infectious disease today, it poses a major threat to public health in the future, says Rocklöv.
The promise of predictive modeling
“Models are simplifications of a system we’re trying to understand,” says Jeremy Hess, who directs the Center for Health and the Global Environment at University of Washington in Seattle. “They’re tools for learning that improve over time with new observations.”
Accurate predictions depend on high-quality, long-term observational data but models must start with assumptions. “It’s not possible to apply an evidence-based approach for the next 40 years,” says Rocklöv. “Using models to experiment and learn is the only way to figure out what climate means for infectious disease. We collect data and analyze what already happened. What we do today will not make a difference for several decades.”
To improve accuracy, scientists develop and draw on thousands of models to cover as many scenarios as possible. One model may capture the dynamics of disease transmission while another focuses on immunity data or ocean influences or seasonal components of a virus. Further, each model needs to be disease-specific and often location-specific to be useful.
“All models have biases so it’s important to use a suite of models,” Tezaur stresses.
The modeling scientist chooses the drivers of change and parameters based on the question explored. The drivers could be increased precipitation, poverty or mosquito prevalence, for instance. Later, the scientist may need to isolate the effect of one driver so that will require another model.
There have been some related successes, such as the latest models for mosquito-borne diseases like Dengue, Zika and malaria as well as those for flu and tick-borne diseases, says Hess.
Rocklöv was part of a research team that used test data from 2018 and 2019 to identify regions at risk for West Nile virus outbreaks. Using AI, scientists were able to forecast outbreaks of the virus for the entire transmission season in Europe. “In the end, we want data-driven models; that’s what AI can accomplish,” says Rocklöv. Other researchers are making an important headway in creating a framework to predict novel host–parasite interactions.
Modeling studies can run months, years or decades. “The scientist is working with layers of data. The challenge is how to transform and couple different models together on a planetary scale,” says Jeanne Fair, a scientist at Los Alamos National Laboratory, Biosecurity and Public Health, in New Mexico.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts a tall spatial and temporal resolutions.
And it’s a constantly changing picture. A modeling study in an April 2022 issue of Nature predicted that thousands of animals will migrate to cooler locales as temperatures rise. This means that various species will come into closer contact with people and other mammals for the first time. This is likely to increase the risk of emerging infectious disease transmitted from animals to humans, especially in Africa and Asia.
Other things can happen too. Global warming could precipitate viral mutations or new infectious diseases that don’t respond to antimicrobial treatments. Insecticide-resistant mosquitoes could evolve. Weather-related food insecurity could increase malnutrition and weaken people’s immune systems. And the impact of an epidemic will be worse if it co-occurs during a heatwave, flood, or drought, says Hess.
The devil is in the climate variables
Solid predictions about the future of climate and disease are not possible with so many uncertainties. Difficult-to-measure drivers must be added to the empirical model mix, such as land and water use, ecosystem changes or the public’s willingness to accept a vaccine or practice social distancing. Nor is there any precedent for calculating the effect of climate changes that are accelerating at a faster speed than ever before.
The most critical climate variables thought to influence disease spread are temperature, precipitation, humidity, sunshine and wind, according to Tezaur’s research. And then there are variables within variables. Influenza scientists, for example, found that warm winters were predictors of the most severe flu seasons in the following year.
The human factor may be the most challenging determinant. To what degree will people curtail greenhouse gas emissions, if at all? The swift development of effective COVID-19 vaccines was a game-changer, but will scientists be able to repeat it during the next pandemic? Plus, no model could predict the amount of internet-fueled COVID-19 misinformation, Fair noted. To tackle this issue, infectious disease teams are looking to include more sociologists and political scientists in their modeling.
Addressing the gaps
Currently, researchers are focusing on the near future, predicting for next year, says Fair. “When it comes to long-term, that’s where we have the most work to do.” While scientists cannot foresee how political influences and misinformation spread will affect models, they are positioned to make headway in collecting and assessing new data streams that have never been merged.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions, Fair and her co-authors stated in their recent study. For example real-time data on mosquito prevalence and diversity in various settings and times is limited or non-existent. Fair also would like to see standards set in mosquito data collection in every country. “Standardizing across the US would be a huge accomplishment,” she says.
Understanding how climate change contributes to the spread of disease is critical for thwarting future calamities.
Jeanne Fair
Hess points to a dearth of data in local and regional datasets about how extreme weather events play out in different geographic locations. His research indicates that Africa and the Middle East experienced substantial climate shifts, for example, but are unrepresented in the evidentiary database, which limits conclusions. “A model for dengue may be good in Singapore but not necessarily in Port-au-Prince,” Hess explains. And, he adds, scientists need a way of evaluating models for how effective they are.
The hope, Rocklöv says, is that in the future we will have data-driven models rather than theoretical ones. In turn, sharper statistical analyses can inform resource allocation and intervention strategies to prevent outbreaks.
Most of all, experts emphasize that epidemiologists and climate scientists must stop working in silos. If scientists can successfully merge epidemiological data with climatic, biological, environmental, ecological and demographic data, they will make better predictions about complex disease patterns. Modeling “cross talk” and among disciplines and, in some cases, refusal to release data between countries is hindering discovery and advances.
It’s time for bold transdisciplinary action, says Hess. He points to initiatives that need funding in disease surveillance and control; developing and testing interventions; community education and social mobilization; decision-support analytics to predict when and where infections will emerge; advanced methodologies to improve modeling; training scientists in data management and integrated surveillance.
Establishing a new field of Outbreak Science to coordinate collaboration would accelerate progress. Investment in decision-support modeling tools for public health teams, policy makers, and other long-term planning stakeholders is imperative, too. We need to invest in programs that encourage people from climate modeling and epidemiology to work together in a cohesive fashion, says Tezaur. Joining forces is the only way to solve the formidable challenges ahead.
This article originally appeared in One Health/One Planet, a single-issue magazine that explores how climate change and other environmental shifts are increasing vulnerabilities to infectious diseases by land and by sea. The magazine probes how scientists are making progress with leaders in other fields toward solutions that embrace diverse perspectives and the interconnectedness of all lifeforms and the planet.
Scientists use AI to predict how hospital stays will go
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 scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
Here are the promising studies covered in this week's Friday Five:
- The problem with bedtime munching
- Scientists use AI to predict how stays in hospitals will go
- How to armor the shields of our livers against cancer
- One big step to save the world: turn one kind of plastic into another
- The perfect recipe for tiny brains
And an honorable mention this week: Bigger is better when it comes to super neurons in super agers