Antibody Testing Alone is Not the Key to Re-Opening Society
[Editor's Note: We asked experts from different specialties to weigh in on a timely Big Question: "How should immunity testing play a role in re-opening society?" Below, a virologist offers her perspective.]
With the advent of serology testing and increased emphasis on "re-opening" America, public health officials have begun considering whether or not people who have recovered from COVID-19 can safely re-enter the workplace.
"Immunity certificates cannot certify what is not known."
Conventional wisdom holds that people who have developed antibodies in response to infection with SARS-CoV-2, the coronavirus that causes COVID-19, are likely to be immune to reinfection.
For most acute viral infections, this is generally true. However, SARS-CoV-2 is a new pathogen, and there are currently many unanswered questions about immunity. Can recovered patients be reinfected or transmit the virus? Does symptom severity determine how protective responses will be after recovery? How long will protection last? Understanding these basic features is essential to phased re-opening of the government and economy for people who have recovered from COVID-19.
One mechanism that has been considered is issuing "immunity certificates" to individuals with antibodies against SARS-CoV-2. These certificates would verify that individuals have already recovered from COVID-19, and thus have antibodies in their blood that will protect them against reinfection, enabling them to safely return to work and participate in society. Although this sounds reasonable in theory, there are many practical reasons why this is not a wise policy decision to ease off restrictive stay-home orders and distancing practices.
Too Many Scientific Unknowns
Serology tests measure antibodies in the serum—the liquid component of blood, which is where the antibodies are located. In this case, serology tests measure antibodies that specifically bind to SARS-CoV-2 virus particles. Usually when a person is infected with a virus, they develop antibodies that can "recognize" that virus, so the presence of SARS-CoV-2 antibodies indicates that a person has been previously exposed to the virus. Broad serology testing is critical to knowing how many people have been infected with SARS-CoV-2, since testing capacity for the virus itself has been so low.
Tests for the virus measure amounts of SARS-CoV-2 RNA—the virus's genetic material—directly, and thus will not detect the virus once a person has recovered. Thus, the majority of people who were not severely ill and did not require hospitalization, or did not have direct contact with a confirmed case, will not test positive for the virus weeks after they have recovered and can only determine if they had COVID-19 by testing for antibodies.
In most cases, for most pathogens, antibodies are also neutralizing, meaning they bind to the virus and render it incapable of infecting cells, and this protects against future infections. Immunity certificates are based on the assumption that people with antibodies specific for SARS-CoV-2 will be protected against reinfection. The problem is that we've only known that SARS-CoV-2 existed for a little over four months. Although studies so far indicate that most (but not all) patients with confirmed COVID-19 cases develop antibodies, we don't know the extent to which antibodies are protective against reinfection, or how long that protection will last. Immunity certificates cannot certify what is not known.
The limited data so far is encouraging with regard to protective immunity. Most of the patient sera tested for antibodies show reasonable titers of IgG, the type of antibodies most likely to be neutralizing. Furthermore, studies have shown that these IgG antibodies are capable of neutralizing surrogate viruses as well as infectious SARS-CoV-2 in laboratory tests. In addition, rhesus monkeys that were experimentally infected with SARS-CoV-2 and allowed to recover were protected from reinfection after a subsequent experimental challenge. These data tentatively suggest that most people are likely to develop neutralizing IgG, and protective immunity, after being infected by SARS-CoV-2.
However, not all COVID-19 patients do produce high levels of antibodies specific for SARS-CoV-2. A small number of patients in one study had no detectable neutralizing IgG. There have also been reports of patients in South Korea testing PCR positive after a prior negative test, indicating reinfection or reactivation. These cases may be explained by the sensitivity of the PCR test, and no data have been produced to indicate that these cases are genuine reinfection or recurrence of viral infection.
Complicating matters further, not all serology tests measure antibody titers. Some rapid serology tests are designed to be binary—the test can either detect antibodies or not, but does not give information about the amount of antibodies circulating. Based on our current knowledge, we cannot be certain that merely having any level of detectable antibodies alone guarantees protection from reinfection, or from a subclinical reinfection that might not cause a second case of COVID-19, but could still result in transmission to others. These unknowns remain problematic even with tests that accurately detect the presence of antibodies—which is not a given today, as many of the newly available tests are reportedly unreliable.
A Logistical and Ethical Quagmire
While most people are eager to cast off the isolation of physical distancing and resume their normal lives, mere desire to return to normality is not an indicator of whether those antibodies actually work, and no certificate can confer immune protection. Furthermore, immunity certificates could lead to some complicated logistical and ethical issues. If antibodies do not guarantee protective immunity, certifying that they do could give antibody-positive people a false sense of security, causing them to relax infection control practices such as distancing and hand hygiene.
"We should not, however, place our faith in assumptions and make return to normality contingent on an arbitrary and uninformative piece of paper."
Certificates could be forged, putting susceptible people at higher exposure risk. It's not clear who would issue them, what they would entitle the bearer to do or not do, or how certification would be verified or enforced. There are many ways in which such certificates could be used as a pretext to discriminate against people based on health status, in addition to disability, race, and socioeconomic status. Tracking people based on immune status raises further concerns about privacy and civil rights.
Rather than issuing documents confirming immune status, we should instead "re-open" society cautiously, with widespread virus and serology testing to accurately identify and isolate infected cases rapidly, with immediate contact tracing to safely quarantine and monitor those at exposure risk. Broad serosurveillance must be coupled with functional assays for neutralization activity to begin assessing how protective antibodies might actually be against SARS-CoV-2 infection. To understand how long immunity lasts, we should study antibodies, as well as the functional capabilities of other components of the larger immune system, such as T cells, in recovered COVID-19 patients over time.
We should not, however, place our faith in assumptions and make return to normality contingent on an arbitrary and uninformative piece of paper. Re-opening society, the government, and the economy depends not only on accurately determining how many people have antibodies to SARS-CoV-2, but on a deeper understanding of how those antibodies work to provide protection.
Researchers probe extreme gene therapy for severe alcoholism
Story by Freethink
A single shot — a gene therapy injected into the brain — dramatically reduced alcohol consumption in monkeys that previously drank heavily. If the therapy is safe and effective in people, it might one day be a permanent treatment for alcoholism for people with no other options.
The challenge: Alcohol use disorder (AUD) means a person has trouble controlling their alcohol consumption, even when it is negatively affecting their life, job, or health.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.