Regenerative medicine has come a long way, baby
The field of regenerative medicine had a shaky start. In 2002, when news spread about the first cloned animal, Dolly the sheep, a raucous debate ensued. Scary headlines and organized opposition groups put pressure on government leaders, who responded by tightening restrictions on this type of research.
Fast forward to today, and regenerative medicine, which focuses on making unhealthy tissues and organs healthy again, is rewriting the code to healing many disorders, though it’s still young enough to be considered nascent. What started as one of the most controversial areas in medicine is now promising to transform it.
Progress in the lab has addressed previous concerns. Back in the early 2000s, some of the most fervent controversy centered around somatic cell nuclear transfer (SCNT), the process used by scientists to produce Dolly. There was fear that this technique could be used in humans, with possibly adverse effects, considering the many medical problems of the animals who had been cloned.
But today, scientists have discovered better approaches with fewer risks. Pioneers in the field are embracing new possibilities for cellular reprogramming, 3D organ printing, AI collaboration, and even growing organs in space. It could bring a new era of personalized medicine for longer, healthier lives - while potentially sparking new controversies.
Engineering tissues from amniotic fluids
Work in regenerative medicine seeks to reverse damage to organs and tissues by culling, modifying and replacing cells in the human body. Scientists in this field reach deep into the mechanisms of diseases and the breakdowns of cells, the little workhorses that perform all life-giving processes. If cells can’t do their jobs, they take whole organs and systems down with them. Regenerative medicine seeks to harness the power of healthy cells derived from stem cells to do the work that can literally restore patients to a state of health—by giving them healthy, functioning tissues and organs.
Modern-day regenerative medicine takes its origin from the 1998 isolation of human embryonic stem cells, first achieved by John Gearhart at Johns Hopkins University. Gearhart isolated the pluripotent cells that can differentiate into virtually every kind of cell in the human body. There was a raging controversy about the use of these cells in research because at that time they came exclusively from early-stage embryos or fetal tissue.
Back then, the highly controversial SCNT cells were the only way to produce genetically matched stem cells to treat patients. Since then, the picture has changed radically because other sources of highly versatile stem cells have been developed. Today, scientists can derive stem cells from amniotic fluid or reprogram patients’ skin cells back to an immature state, so they can differentiate into whatever types of cells the patient needs.
In the context of medical history, the field of regenerative medicine is progressing at a dizzying speed. But for those living with aggressive or chronic illnesses, it can seem that the wheels of medical progress grind slowly.
The ethical debate has been dialed back and, in the last few decades, the field has produced important innovations, spurring the development of whole new FDA processes and categories, says Anthony Atala, a bioengineer and director of the Wake Forest Institute for Regenerative Medicine. Atala and a large team of researchers have pioneered many of the first applications of 3D printed tissues and organs using cells developed from patients or those obtained from amniotic fluid or placentas.
His lab, considered to be the largest devoted to translational regenerative medicine, is currently working with 40 different engineered human tissues. Sixteen of them have been transplanted into patients. That includes skin, bladders, urethras, muscles, kidneys and vaginal organs, to name just a few.
These achievements are made possible by converging disciplines and technologies, such as cell therapies, bioengineering, gene editing, nanotechnology and 3D printing, to create living tissues and organs for human transplants. Atala is currently overseeing clinical trials to test the safety of tissues and organs engineered in the Wake Forest lab, a significant step toward FDA approval.
In the context of medical history, the field of regenerative medicine is progressing at a dizzying speed. But for those living with aggressive or chronic illnesses, it can seem that the wheels of medical progress grind slowly.
“It’s never fast enough,” Atala says. “We want to get new treatments into the clinic faster, but the reality is that you have to dot all your i’s and cross all your t’s—and rightly so, for the sake of patient safety. People want predictions, but you can never predict how much work it will take to go from conceptualization to utilization.”
As a surgeon, he also treats patients and is able to follow transplant recipients. “At the end of the day, the goal is to get these technologies into patients, and working with the patients is a very rewarding experience,” he says. Will the 3D printed organs ever outrun the shortage of donated organs? “That’s the hope,” Atala says, “but this technology won’t eliminate the need for them in our lifetime.”
New methods are out of this world
Jeanne Loring, another pioneer in the field and director of the Center for Regenerative Medicine at Scripps Research Institute in San Diego, says that investment in regenerative medicine is not only paying off, but is leading to truly personalized medicine, one of the holy grails of modern science.
This is because a patient’s own skin cells can be reprogrammed to become replacements for various malfunctioning cells causing incurable diseases, such as diabetes, heart disease, macular degeneration and Parkinson’s. If the cells are obtained from a source other than the patient, they can be rejected by the immune system. This means that patients need lifelong immunosuppression, which isn’t ideal. “With Covid,” says Loring, “I became acutely aware of the dangers of immunosuppression.” Using the patient’s own cells eliminates that problem.
Microgravity conditions make it easier for the cells to form three-dimensional structures, which could more easily lead to the growing of whole organs. In fact, Loring's own cells have been sent to the ISS for study.
Loring has a special interest in neurons, or brain cells that can be developed by manipulating cells found in the skin. She is looking to eventually treat Parkinson’s disease using them. The manipulated cells produce dopamine, the critical hormone or neurotransmitter lacking in the brains of patients. A company she founded plans to start a Phase I clinical trial using cell therapies for Parkinson’s soon, she says.
This is the culmination of many years of basic research on her part, some of it on her own cells. In 2007, Loring had her own cells reprogrammed, so there’s a cell line that carries her DNA. “They’re just like embryonic stem cells, but personal,” she said.
Loring has another special interest—sending immature cells into space to be studied at the International Space Station. There, microgravity conditions make it easier for the cells to form three-dimensional structures, which could more easily lead to the growing of whole organs. In fact, her own cells have been sent to the ISS for study. “My colleagues and I have completed four missions at the space station,” she says. “The last cells came down last August. They were my own cells reprogrammed into pluripotent cells in 2009. No one else can say that,” she adds.
Future controversies and tipping points
Although the original SCNT debate has calmed down, more controversies may arise, Loring thinks.
One of them could concern growing synthetic embryos. The embryos are ultimately derived from embryonic stem cells, and it’s not clear to what stage these embryos can or will be grown in an artificial uterus—another recent invention. The science, so far done only in animals, is still new and has not been widely publicized but, eventually, “People will notice the production of synthetic embryos and growing them in an artificial uterus,” Loring says. It’s likely to incite many of the same reactions as the use of embryonic stem cells.
Bernard Siegel, the founder and director of the Regenerative Medicine Foundation and executive director of the newly formed Healthspan Action Coalition (HSAC), believes that stem cell science is rapidly approaching tipping point and changing all of medical science. (For disclosure, I do consulting work for HSAC). Siegel says that regenerative medicine has become a new pillar of medicine that has recently been fast-tracked by new technology.
Artificial intelligence is speeding up discoveries and the convergence of key disciplines, as demonstrated in Atala’s lab, which is creating complex new medical products that replace the body’s natural parts. Just as importantly, those parts are genetically matched and pose no risk of rejection.
These new technologies must be regulated, which can be a challenge, Siegel notes. “Cell therapies represent a challenge to the existing regulatory structure, including payment, reimbursement and infrastructure issues that 20 years ago, didn’t exist.” Now the FDA and other agencies are faced with this revolution, and they’re just beginning to adapt.
Siegel cited the 2021 FDA Modernization Act as a major step. The Act allows drug developers to use alternatives to animal testing in investigating the safety and efficacy of new compounds, loosening the agency’s requirement for extensive animal testing before a new drug can move into clinical trials. The Act is a recognition of the profound effect that cultured human cells are having on research. Being able to test drugs using actual human cells promises to be far safer and more accurate in predicting how they will act in the human body, and could accelerate drug development.
Siegel, a longtime veteran and founding father of several health advocacy organizations, believes this work helped bring cell therapies to people sooner rather than later. His new focus, through the HSAC, is to leverage regenerative medicine into extending not just the lifespan but the worldwide human healthspan, the period of life lived with health and vigor. “When you look at the HSAC as a tree,” asks Siegel, “what are the roots of that tree? Stem cell science and the huge ecosystem it has created.” The study of human aging is another root to the tree that has potential to lengthen healthspans.
The revolutionary science underlying the extension of the healthspan needs to be available to the whole world, Siegel says. “We need to take all these roots and come up with a way to improve the life of all mankind,” he says. “Everyone should be able to take advantage of this promising new world.”
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.