How a Deadly Fire Gave Birth to Modern Medicine
On the evening of November 28, 1942, more than 1,000 revelers from the Boston College-Holy Cross football game jammed into the Cocoanut Grove, Boston's oldest nightclub. When a spark from faulty wiring accidently ignited an artificial palm tree, the packed nightspot, which was only designed to accommodate about 500 people, was quickly engulfed in flames. In the ensuing panic, hundreds of people were trapped inside, with most exit doors locked. Bodies piled up by the only open entrance, jamming the exits, and 490 people ultimately died in the worst fire in the country in forty years.
"People couldn't get out," says Dr. Kenneth Marshall, a retired plastic surgeon in Boston and president of the Cocoanut Grove Memorial Committee. "It was a tragedy of mammoth proportions."
Within a half an hour of the start of the blaze, the Red Cross mobilized more than five hundred volunteers in what one newspaper called a "Rehearsal for Possible Blitz." The mayor of Boston imposed martial law. More than 300 victims—many of whom subsequently died--were taken to Boston City Hospital in one hour, averaging one victim every eleven seconds, while Massachusetts General Hospital admitted 114 victims in two hours. In the hospitals, 220 victims clung precariously to life, in agonizing pain from massive burns, their bodies ravaged by infection.
The scene of the fire.
Boston Public Library
Tragic Losses Prompted Revolutionary Leaps
But there is a silver lining: this horrific disaster prompted dramatic changes in safety regulations to prevent another catastrophe of this magnitude and led to the development of medical techniques that eventually saved millions of lives. It transformed burn care treatment and the use of plasma on burn victims, but most importantly, it introduced to the public a new wonder drug that revolutionized medicine, midwifed the birth of the modern pharmaceutical industry, and nearly doubled life expectancy, from 48 years at the turn of the 20th century to 78 years in the post-World War II years.
The devastating grief of the survivors also led to the first published study of post-traumatic stress disorder by pioneering psychiatrist Alexandra Adler, daughter of famed Viennese psychoanalyst Alfred Adler, who was a student of Freud. Dr. Adler studied the anxiety and depression that followed this catastrophe, according to the New York Times, and "later applied her findings to the treatment World War II veterans."
Dr. Ken Marshall is intimately familiar with the lingering psychological trauma of enduring such a disaster. His mother, an Irish immigrant and a nurse in the surgical wards at Boston City Hospital, was on duty that cold Thanksgiving weekend night, and didn't come home for four days. "For years afterward, she'd wake up screaming in the middle of the night," recalls Dr. Marshall, who was four years old at the time. "Seeing all those bodies lined up in neat rows across the City Hospital's parking lot, still in their evening clothes. It was always on her mind and memories of the horrors plagued her for the rest of her life."
The sheer magnitude of casualties prompted overwhelmed physicians to try experimental new procedures that were later successfully used to treat thousands of battlefield casualties. Instead of cutting off blisters and using dyes and tannic acid to treat burned tissues, which can harden the skin, they applied gauze coated with petroleum jelly. Doctors also refined the formula for using plasma--the fluid portion of blood and a medical technology that was just four years old--to replenish bodily liquids that evaporated because of the loss of the protective covering of skin.
"Every war has given us a new medical advance. And penicillin was the great scientific advance of World War II."
"The initial insult with burns is a loss of fluids and patients can die of shock," says Dr. Ken Marshall. "The scientific progress that was made by the two institutions revolutionized fluid management and topical management of burn care forever."
Still, they could not halt the staph infections that kill most burn victims—which prompted the first civilian use of a miracle elixir that was being secretly developed in government-sponsored labs and that ultimately ushered in a new age in therapeutics. Military officials quickly realized this disaster could provide an excellent natural laboratory to test the effectiveness of this drug and see if it could be used to treat the acute traumas of combat in this unfortunate civilian approximation of battlefield conditions. At the time, the very existence of this wondrous medicine—penicillin—was a closely guarded military secret.
From Forgotten Lab Experiment to Wonder Drug
In 1928, Alexander Fleming discovered the curative powers of penicillin, which promised to eradicate infectious pathogens that killed millions every year. But the road to mass producing enough of the highly unstable mold was littered with seemingly unsurmountable obstacles and it remained a forgotten laboratory curiosity for over a decade. But Fleming never gave up and penicillin's eventual rescue from obscurity was a landmark in scientific history.
In 1940, a group at Oxford University, funded in part by the Rockefeller Foundation, isolated enough penicillin to test it on twenty-five mice, which had been infected with lethal doses of streptococci. Its therapeutic effects were miraculous—the untreated mice died within hours, while the treated ones played merrily in their cages, undisturbed. Subsequent tests on a handful of patients, who were brought back from the brink of death, confirmed that penicillin was indeed a wonder drug. But Britain was then being ravaged by the German Luftwaffe during the Blitz, and there were simply no resources to devote to penicillin during the Nazi onslaught.
In June of 1941, two of the Oxford researchers, Howard Florey and Ernst Chain, embarked on a clandestine mission to enlist American aid. Samples of the temperamental mold were stored in their coats. By October, the Roosevelt Administration had recruited four companies—Merck, Squibb, Pfizer and Lederle—to team up in a massive, top-secret development program. Merck, which had more experience with fermentation procedures, swiftly pulled away from the pack and every milligram they produced was zealously hoarded.
After the nightclub fire, the government ordered Merck to dispatch to Boston whatever supplies of penicillin that they could spare and to refine any crude penicillin broth brewing in Merck's fermentation vats. After working in round-the-clock relays over the course of three days, on the evening of December 1st, 1942, a refrigerated truck containing thirty-two liters of injectable penicillin left Merck's Rahway, New Jersey plant. It was accompanied by a convoy of police escorts through four states before arriving in the pre-dawn hours at Massachusetts General Hospital. Dozens of people were rescued from near-certain death in the first public demonstration of the powers of the antibiotic, and the existence of penicillin could no longer be kept secret from inquisitive reporters and an exultant public. The next day, the Boston Globe called it "priceless" and Time magazine dubbed it a "wonder drug."
Within fourteen months, penicillin production escalated exponentially, churning out enough to save the lives of thousands of soldiers, including many from the Normandy invasion. And in October 1945, just weeks after the Japanese surrender ended World War II, Alexander Fleming, Howard Florey and Ernst Chain were awarded the Nobel Prize in medicine. But penicillin didn't just save lives—it helped build some of the most innovative medical and scientific companies in history, including Merck, Pfizer, Glaxo and Sandoz.
"Every war has given us a new medical advance," concludes Marshall. "And penicillin was the great scientific advance of World War II."
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.