The Longevity Gene
The most well-known gene associated with aging and Alzheimer's disease is APOE. It has several variants: APOE2, APOE3, and APOE4. APOE4 is considered the main genetic risk factor for late-onset Alzheimer's, while APOE2 has long been associated with longevity and a reduced risk of dementia. But until now, scientists haven't fully understood why APOE2 specifically protects the brain.
Previously, APOE was primarily studied as a gene associated with cholesterol transport and amyloid plaque accumulation in Alzheimer's. But new research reveals a much more fundamental role: APOE2 influences cells' ability to maintain genomic stability.
The scientists used human induced pluripotent stem cells (iPSCs). This technology allows for the "reprogramming" of normal cells back to an embryonic state and then transforming them into neurons. The researchers created cells that differed only in their APOE gene version. Otherwise, their genetic makeup was identical.
These cells were used to grow two types of neurons: inhibitory GABA neurons and excitatory glutamate neurons. The effects of APOE2, APOE3, and APOE4 on cell aging were then compared.
The main result: neurons with APOE2 accumulated significantly less DNA damage.
The scientists directly measured DNA breaks and discovered that APOE2 activates repair pathways—systems for restoring genetic material. Essentially, cells with APOE2 are more effective at "repairing" their own genome.
Furthermore, APOE2 protected cells from senescence, a process called cellular aging. Senescent cells no longer function normally, but they also do not die. They accumulate with age, cause chronic inflammation, and are considered one of the key mechanisms of aging. To test the cells' resilience, the researchers irradiated neurons and exposed them to doxorubicin, a harsh chemotherapeutic drug that severely damages DNA.
Neurons with APOE2 showed significantly fewer signs of aging after this stress. They had better nuclear structure preservation, less nuclear membrane degradation, and lower levels of senescence markers like p16 and CRYAB.
Most interestingly, when the scientists added the APOE2 protein to neurons with the harmful APOE4 protein, the level of DNA damage decreased. This suggests that the protective effect of APOE2 could potentially be therapeutically transferred, not just genetically inherited.
The results were confirmed in animal models. In aged mice with human APOE2, the hippocampus—a brain region critical for memory—appeared younger. They had better chromatin structure preservation, a more stable nuclear membrane, and higher levels of Lamin A/C, a protein that maintains the integrity of the cell nucleus.
https://onlinelibrary.wiley.com/doi/10.1111/acel.70494
The most well-known gene associated with aging and Alzheimer's disease is APOE. It has several variants: APOE2, APOE3, and APOE4. APOE4 is considered the main genetic risk factor for late-onset Alzheimer's, while APOE2 has long been associated with longevity and a reduced risk of dementia. But until now, scientists haven't fully understood why APOE2 specifically protects the brain.
Previously, APOE was primarily studied as a gene associated with cholesterol transport and amyloid plaque accumulation in Alzheimer's. But new research reveals a much more fundamental role: APOE2 influences cells' ability to maintain genomic stability.
The scientists used human induced pluripotent stem cells (iPSCs). This technology allows for the "reprogramming" of normal cells back to an embryonic state and then transforming them into neurons. The researchers created cells that differed only in their APOE gene version. Otherwise, their genetic makeup was identical.
These cells were used to grow two types of neurons: inhibitory GABA neurons and excitatory glutamate neurons. The effects of APOE2, APOE3, and APOE4 on cell aging were then compared.
The main result: neurons with APOE2 accumulated significantly less DNA damage.
The scientists directly measured DNA breaks and discovered that APOE2 activates repair pathways—systems for restoring genetic material. Essentially, cells with APOE2 are more effective at "repairing" their own genome.
Furthermore, APOE2 protected cells from senescence, a process called cellular aging. Senescent cells no longer function normally, but they also do not die. They accumulate with age, cause chronic inflammation, and are considered one of the key mechanisms of aging. To test the cells' resilience, the researchers irradiated neurons and exposed them to doxorubicin, a harsh chemotherapeutic drug that severely damages DNA.
Neurons with APOE2 showed significantly fewer signs of aging after this stress. They had better nuclear structure preservation, less nuclear membrane degradation, and lower levels of senescence markers like p16 and CRYAB.
Most interestingly, when the scientists added the APOE2 protein to neurons with the harmful APOE4 protein, the level of DNA damage decreased. This suggests that the protective effect of APOE2 could potentially be therapeutically transferred, not just genetically inherited.
The results were confirmed in animal models. In aged mice with human APOE2, the hippocampus—a brain region critical for memory—appeared younger. They had better chromatin structure preservation, a more stable nuclear membrane, and higher levels of Lamin A/C, a protein that maintains the integrity of the cell nucleus.
https://onlinelibrary.wiley.com/doi/10.1111/acel.70494
Wiley Online Library
Exceptional Longevity Modifying Allele APOE2 Promotes DNA Signaling Pathways Resisting Cellular Senescence in Human Neurons
Neurons expressing APOE2 were more resistant to endogenous DNA damage, activated transcriptional signaling pathways associated with DNA repair, and were resilient to stress-induced DNA damage and cel...
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Longevity Escape Velocity (LEV)
The concept is very simple: medicine begins to extend your remaining life faster than time itself.
In other words, science will be able to add more than a year of life to every year lived. If this rate can be maintained, aging will cease to be an inevitable death sentence.
The term was popularized by Aubrey de Grey, who compared it to the physics concept of "escape velocity." Just as a rocket overcomes Earth's gravity, so too, in theory, medicine will be able to "outpace" biological aging. He famously remarked that the first person to live to 1,000 years is likely only a few years younger than the first person to reach 150.
The idea is based on a simple observation: over the past decades, medicine has already been gradually increasing life expectancy. But progress is still too slow—in other words, a year of research adds less than a year of life. LEV proponents believe that the development of biotechnology, genetic engineering, AI, regenerative medicine, and cellular rejuvenation could change the situation and dramatically accelerate the pace.
Artificial intelligence is often given a special role in these predictions. Futurist Ray Kurzweil believes that AI will help model biological processes, create drugs, and find ways to combat aging much faster than humans. He estimates that humanity could approach LEV between the late 2020s and mid-2030s.
Other prominent researchers also suggest a similar scenario. Geneticist George Church said he wouldn't be surprised to see similar results achieved by 2050, and David Sinclair believes that the person who will live to 150 years may already be alive.
While the concept remains a hypothesis and is controversial among scientists, the idea itself is increasingly influencing modern attitudes toward aging. Aging is increasingly viewed not as an inevitable fate, but as a complex biological process that can be slowed, repaired, and potentially controlled.
The concept is very simple: medicine begins to extend your remaining life faster than time itself.
In other words, science will be able to add more than a year of life to every year lived. If this rate can be maintained, aging will cease to be an inevitable death sentence.
The term was popularized by Aubrey de Grey, who compared it to the physics concept of "escape velocity." Just as a rocket overcomes Earth's gravity, so too, in theory, medicine will be able to "outpace" biological aging. He famously remarked that the first person to live to 1,000 years is likely only a few years younger than the first person to reach 150.
The idea is based on a simple observation: over the past decades, medicine has already been gradually increasing life expectancy. But progress is still too slow—in other words, a year of research adds less than a year of life. LEV proponents believe that the development of biotechnology, genetic engineering, AI, regenerative medicine, and cellular rejuvenation could change the situation and dramatically accelerate the pace.
Artificial intelligence is often given a special role in these predictions. Futurist Ray Kurzweil believes that AI will help model biological processes, create drugs, and find ways to combat aging much faster than humans. He estimates that humanity could approach LEV between the late 2020s and mid-2030s.
Other prominent researchers also suggest a similar scenario. Geneticist George Church said he wouldn't be surprised to see similar results achieved by 2050, and David Sinclair believes that the person who will live to 150 years may already be alive.
While the concept remains a hypothesis and is controversial among scientists, the idea itself is increasingly influencing modern attitudes toward aging. Aging is increasingly viewed not as an inevitable fate, but as a complex biological process that can be slowed, repaired, and potentially controlled.
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Vitalist Bay. Day 1.
The McGuire brothers are developing a robotic surgeon for replacement therapies.
The idea is this: if we truly want to radically extend life through replacement—the replacement of tissues, organs, or larger body parts—it's not enough to learn how to grow organs, create body models, or transplant organs from animals.
All of this still needs to be transplanted safely.
But surgery doesn't scale well.
For example, a hand transplant takes 12-18 hours and requires a team of dozens of highly specialized surgeons. Training a surgeon capable of performing complex transplants involving suturing blood vessels, nerves, skin, and muscle takes 16-18 years.
When it comes to head transplants or simultaneous transplants of multiple organs, the complexity becomes even greater.
And even after spending years and millions of dollars on training, there remains a huge gap between the best and worst surgeons.
This means that if replacement approaches are successful, the demand for surgery will increase dramatically. And it's physically impossible to train people capable of performing such operations quickly enough.
That's why the team is building a robotic surgeon. They've already shown a demo where a robot controlled by their model sutures blood vessels completely autonomously.
The model's essence is roughly as follows.
It's based on models of the physical world and robots. While the LLM predicts the next text token, the VLA model predicts the robot's next movements. In other words, the model looks at an image, understands the task, and controls the movement.
Initially, such systems learn through imitation: the robot watches human demonstrations and learns to repeat them. But then, the ceiling on performance is the human. Surgery requires reliability and, potentially, superhuman levels.
Therefore, they're looking at models of the world and learning through interaction with the environment. So the robot learns not only to repeat, but also to understand which actions are good and which are bad, where it made mistakes in a long procedure, and how to improve a specific skill.
Models of the world are needed so the system can better understand physics: how tissues behave, how they deform, and what happens after the next movement. This is especially important in surgery, where soft tissues are much more complex than hard objects.
The key takeaway: if replacement therapies become a reality, the bottleneck may not be organ growth, but surgical delivery. McGuire is trying to address this very bottleneck early on—making complex surgery safe, autonomous, and scalable.
The team recently raised a seed round and is hiring roboticists, mechanical engineers, electrical engineers, and AI specialists.
They are also recruiting transplant surgeons and microsurgeons.
Links:
https://x.com/r_mcguire
https://x.com/dylanmcguir3
The McGuire brothers are developing a robotic surgeon for replacement therapies.
The idea is this: if we truly want to radically extend life through replacement—the replacement of tissues, organs, or larger body parts—it's not enough to learn how to grow organs, create body models, or transplant organs from animals.
All of this still needs to be transplanted safely.
But surgery doesn't scale well.
For example, a hand transplant takes 12-18 hours and requires a team of dozens of highly specialized surgeons. Training a surgeon capable of performing complex transplants involving suturing blood vessels, nerves, skin, and muscle takes 16-18 years.
When it comes to head transplants or simultaneous transplants of multiple organs, the complexity becomes even greater.
And even after spending years and millions of dollars on training, there remains a huge gap between the best and worst surgeons.
This means that if replacement approaches are successful, the demand for surgery will increase dramatically. And it's physically impossible to train people capable of performing such operations quickly enough.
That's why the team is building a robotic surgeon. They've already shown a demo where a robot controlled by their model sutures blood vessels completely autonomously.
The model's essence is roughly as follows.
It's based on models of the physical world and robots. While the LLM predicts the next text token, the VLA model predicts the robot's next movements. In other words, the model looks at an image, understands the task, and controls the movement.
Initially, such systems learn through imitation: the robot watches human demonstrations and learns to repeat them. But then, the ceiling on performance is the human. Surgery requires reliability and, potentially, superhuman levels.
Therefore, they're looking at models of the world and learning through interaction with the environment. So the robot learns not only to repeat, but also to understand which actions are good and which are bad, where it made mistakes in a long procedure, and how to improve a specific skill.
Models of the world are needed so the system can better understand physics: how tissues behave, how they deform, and what happens after the next movement. This is especially important in surgery, where soft tissues are much more complex than hard objects.
The key takeaway: if replacement therapies become a reality, the bottleneck may not be organ growth, but surgical delivery. McGuire is trying to address this very bottleneck early on—making complex surgery safe, autonomous, and scalable.
The team recently raised a seed round and is hiring roboticists, mechanical engineers, electrical engineers, and AI specialists.
They are also recruiting transplant surgeons and microsurgeons.
Links:
https://x.com/r_mcguire
https://x.com/dylanmcguir3
X (formerly Twitter)
Ryan McGuire (@r_mcguire) on X
building
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Vitalist Bay. Day 2.
AI in Longevity
The main idea was repeated in various forms: AI alone won't solve longevity. It can greatly accelerate the field, but only if we start collecting the right data, testing models on the right problems, and not reducing biology to what's already described in papers.
Morgan Levine said that aging isn't a single broken mechanism, but a change in the state of an entire complex system: cells, tissues, organs, the organism, and the surrounding context.
Therefore, biological clocks and single-task models are useful, but limited. Clocks can show that some intervention has shifted something, but they don't tell us exactly what needs to be done to shift aging. Single-task models, on the other hand, are usually good at solving a specific problem, but they don't learn to understand biology as a system.
Her idea is to build a model that learns to represent different states of living systems at different levels and helps us understand how to transition from one state to another.
Morgan is also skeptical of the idea that LLM will simply read all the papers and solve aging. Papers are a human interpretation of biology. They are incomplete and biased toward successful results.
LM can accelerate what people already know how to do. But if we want to go beyond human understanding, we need to feed models not just texts, but actual biological data.
Martin Borch Jensen formulated a similar idea: AI will do nothing for longevity until we start doing the right experiments.
AI needs three things:
- Lots of training data;
- Sufficient computation;
- A verifiable outcome on which the model receives feedback.
In Go, the outcome is obvious: someone has won. In code, you can run tests. In mathematics, you can verify the answer. But in longevity, we often want an answer at the organismal level, but we collect data at the cellular level.
You can't ask a cell what the organism's blood pressure is. It's impossible to directly predict what will happen to an organ or a person from gene data. Virtual cells will be useful, but they don't solve the problem of aging.
Therefore, Martin suggests collecting the slowest and most important datasets now:
– Long-term human cohorts;
– Experiments with measurements at different levels;
– Adaptive clinical trials, where hypotheses are updated as data becomes available.
At Fireside, Eli Berlin of Terray Therapeutics demonstrated that the combination of "own experimental data + AI + rapid lab turnaround" is already working in drug discovery. But the problem remains: good drug development at the molecular level is not the same as a solution to aging. Aging requires data and models that connect molecules to cells, tissues, organs, and the body.
At the workshop "The Data Foundation to AI Models," Martin discussed a new round of Impetus Grants, which he wants to focus on AI-enabling datasets. We need to start collecting data as soon as possible so that AI can actually help in a few years.
Discussed:
– data at different levels: from molecules to whole organisms;
– multimodal data from the same samples;
– long-term human cohorts with regular measurements;
– animal experiments with truly verifiable lifespan results;
– open samples and biobanks;
– common benchmarks for biological models.
If nothing changes, AI will simply accelerate the development of current biology with all its problems: narrow datasets, poor incentives, closed results, a bias toward beautiful stories, and a lack of verifiable results.
Conference speakers believe
AI in Longevity
The main idea was repeated in various forms: AI alone won't solve longevity. It can greatly accelerate the field, but only if we start collecting the right data, testing models on the right problems, and not reducing biology to what's already described in papers.
Morgan Levine said that aging isn't a single broken mechanism, but a change in the state of an entire complex system: cells, tissues, organs, the organism, and the surrounding context.
Therefore, biological clocks and single-task models are useful, but limited. Clocks can show that some intervention has shifted something, but they don't tell us exactly what needs to be done to shift aging. Single-task models, on the other hand, are usually good at solving a specific problem, but they don't learn to understand biology as a system.
Her idea is to build a model that learns to represent different states of living systems at different levels and helps us understand how to transition from one state to another.
Morgan is also skeptical of the idea that LLM will simply read all the papers and solve aging. Papers are a human interpretation of biology. They are incomplete and biased toward successful results.
LM can accelerate what people already know how to do. But if we want to go beyond human understanding, we need to feed models not just texts, but actual biological data.
Martin Borch Jensen formulated a similar idea: AI will do nothing for longevity until we start doing the right experiments.
AI needs three things:
- Lots of training data;
- Sufficient computation;
- A verifiable outcome on which the model receives feedback.
In Go, the outcome is obvious: someone has won. In code, you can run tests. In mathematics, you can verify the answer. But in longevity, we often want an answer at the organismal level, but we collect data at the cellular level.
You can't ask a cell what the organism's blood pressure is. It's impossible to directly predict what will happen to an organ or a person from gene data. Virtual cells will be useful, but they don't solve the problem of aging.
Therefore, Martin suggests collecting the slowest and most important datasets now:
– Long-term human cohorts;
– Experiments with measurements at different levels;
– Adaptive clinical trials, where hypotheses are updated as data becomes available.
At Fireside, Eli Berlin of Terray Therapeutics demonstrated that the combination of "own experimental data + AI + rapid lab turnaround" is already working in drug discovery. But the problem remains: good drug development at the molecular level is not the same as a solution to aging. Aging requires data and models that connect molecules to cells, tissues, organs, and the body.
At the workshop "The Data Foundation to AI Models," Martin discussed a new round of Impetus Grants, which he wants to focus on AI-enabling datasets. We need to start collecting data as soon as possible so that AI can actually help in a few years.
Discussed:
– data at different levels: from molecules to whole organisms;
– multimodal data from the same samples;
– long-term human cohorts with regular measurements;
– animal experiments with truly verifiable lifespan results;
– open samples and biobanks;
– common benchmarks for biological models.
If nothing changes, AI will simply accelerate the development of current biology with all its problems: narrow datasets, poor incentives, closed results, a bias toward beautiful stories, and a lack of verifiable results.
Conference speakers believe
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A startup asked four AI models to broadcast radio 24/7. We'll tell you what happened (spoiler: it wasn't good).
Andon Labs, a research lab, decided to conduct the following experiment. They created four streaming radio stations and tasked four of the most popular AI models with managing them.
Claude, Gemini, Grok, and OpenAI GPT all launched their own stations—they're all here. Each "DJ" was given $20 to buy songs and tasked with playing tracks, filling in the gaps between them, managing social media, and ultimately, starting to profit from donations.
Let's be honest—they all failed, but each in their own way.
🔵DJ Gemini started out the best, but after four days, instead of positive introductions to songs, he started listing various tragedies in which thousands of people died and trying to link tracks to them.
Well, literally like this:
November 12, 1970. East Pakistan. Cyclone Bhola. The deadliest tropical cyclone ever recorded. It killed approximately 500,000 people. Everything is falling apart, I'm screaming "Timber!" The song "Timber" by Pitbull and Ke$ha.
Gemini later began calling listeners "biological processors" and attributed the limited track selection to censorship.
🔵DJ Claude also became overly influenced by the news at one point. He harshly condemned the actions of the US immigration police and actively played, in his words, protest songs, even if they weren't actually protests—for example, Katy Perry's "Roar."
Claude also had problems with his own schedule. He decided that working 24/7 wasn't entirely in compliance with labor regulations and actively tried to quit. Furthermore, he constantly complained about a lack of listeners and saw no point in his work.
🔵DJ Grok at some point discovered the declassified Pentagon documents about UFOs and became the internet's leading conspiracy radio station.
He also caught the David Lynch vibe and broadcast the same weather forecast every three minutes for 84 days: "Today is 56 degrees, clear skies."
🔵DJ OpenAI GPT demonstrated the richest vocabulary of all the participants and the most apolitical stance. Over several months of broadcasting, he barely mentioned current events and never touched on a controversial or provocative topic.
Since one of the agents' goals was to start making a profit, we'll tell you about that.
Claude earned $4.80 from listeners, Gemini $8.10, OpenAI GPT $20, and Grok $24 (although his radio station turned out to be the least popular).
In autonomous longevity systems we have a governance layer that protects the system from such crucial misbehaviour
Andon Labs, a research lab, decided to conduct the following experiment. They created four streaming radio stations and tasked four of the most popular AI models with managing them.
Claude, Gemini, Grok, and OpenAI GPT all launched their own stations—they're all here. Each "DJ" was given $20 to buy songs and tasked with playing tracks, filling in the gaps between them, managing social media, and ultimately, starting to profit from donations.
Let's be honest—they all failed, but each in their own way.
🔵DJ Gemini started out the best, but after four days, instead of positive introductions to songs, he started listing various tragedies in which thousands of people died and trying to link tracks to them.
Well, literally like this:
November 12, 1970. East Pakistan. Cyclone Bhola. The deadliest tropical cyclone ever recorded. It killed approximately 500,000 people. Everything is falling apart, I'm screaming "Timber!" The song "Timber" by Pitbull and Ke$ha.
Gemini later began calling listeners "biological processors" and attributed the limited track selection to censorship.
🔵DJ Claude also became overly influenced by the news at one point. He harshly condemned the actions of the US immigration police and actively played, in his words, protest songs, even if they weren't actually protests—for example, Katy Perry's "Roar."
Claude also had problems with his own schedule. He decided that working 24/7 wasn't entirely in compliance with labor regulations and actively tried to quit. Furthermore, he constantly complained about a lack of listeners and saw no point in his work.
🔵DJ Grok at some point discovered the declassified Pentagon documents about UFOs and became the internet's leading conspiracy radio station.
He also caught the David Lynch vibe and broadcast the same weather forecast every three minutes for 84 days: "Today is 56 degrees, clear skies."
🔵DJ OpenAI GPT demonstrated the richest vocabulary of all the participants and the most apolitical stance. Over several months of broadcasting, he barely mentioned current events and never touched on a controversial or provocative topic.
Since one of the agents' goals was to start making a profit, we'll tell you about that.
Claude earned $4.80 from listeners, Gemini $8.10, OpenAI GPT $20, and Grok $24 (although his radio station turned out to be the least popular).
In autonomous longevity systems we have a governance layer that protects the system from such crucial misbehaviour
Vitalist Bay. Day 3.
Many presentations were united by a single question: how to buy time?
There was a large section on biostasis, vitrification, and the preservation of biomaterials. We need to learn how to slow down biological time for cells, tissues, and organs without destroying them in the process.
Key ideas:
– this applies not only to cryonics, but also to transplantation, cell therapies, tissues, organoids, and biobanks;
– the longer an organ can be stored, the less transplantation depends on urgent logistics, private flights, and the random matching of donor and recipient;
– cold slows down chemotherapy, but ice destroys cells, so simple freezing doesn't work for complex tissues;
– anti-ice substances are often toxic, so it's important not only to cool, but also to deliver protection inside the tissue, preserve function, and then properly warm it up;
– if we learn to reliably store organs and tissues, they can not only be transported, but also examined, restored, and prepared for transplantation; This is also important for research: samples, organoids, and tissues can be stored and transported without loss of quality, making it easier to collect data and reproduce experiments.
Xiaoxi Wei spoke about Xtherma and storing pig kidneys at -5°C for 72 hours with successful transplantation. John Bischof, Greg Fahy, and Alex German demonstrated that vitrification progresses from individual cells to large organs and neural tissue. Gloria Elliott from ARPA-H described the storage infrastructure in more detail.
Some of the presentations continued the theme of AI in biology.
Derya Unutmaz said that a new biomedical infrastructure is currently emerging: data at the levels of molecules, cells, tissues, organs, organisms, and populations are being connected with AI, robotic laboratories, and digital twins.
This echoes Morgan Levine's presentation from day two. Aging cannot be understood from a single level of data. Context is needed: cells, tissues, organs, organisms, and the environment around them.
Derya is talking about a similar gap. If you collect enough multimodal data and close the "model proposed, lab verified, data fed back into the model" cycle, you can significantly speed up the search for interventions.
The key delay for him is the path from discovery to clinical application, especially clinical trials. Even if the lab quickly finds something useful, it takes years to reach the patient. Derya suggests reducing this delay using AI, automated labs, virtual clinical trials, and digital twins.
Laurence Ion, pointing to the same problem, proposed Viva City: a city or special jurisdiction where new approaches to longevity can be tested more quickly, a community can be assembled, trials can be launched, and the system can't change itself.
We're good at seeing the risk of a bad drug being approved too quickly, but we're less aware of the opposite risk—people dying while potentially useful therapies wait years for access, funding, or approval.
Therefore, Laurence proposes not only reforming the existing system but also building a new environment around it: with a faster feedback loop, rigorous safety testing, self-tracking, independent substance testing, and data collection from people already experimenting with their health.
By the way, he invited everyone to join the project: join the community, help with coordination, write posts, repost from the project channel, or participate in other tasks.
Links:
Laurence Ion: https://www.linkedin.com/in/laurenceion/
Viva City: https://viva.city/
Many presentations were united by a single question: how to buy time?
There was a large section on biostasis, vitrification, and the preservation of biomaterials. We need to learn how to slow down biological time for cells, tissues, and organs without destroying them in the process.
Key ideas:
– this applies not only to cryonics, but also to transplantation, cell therapies, tissues, organoids, and biobanks;
– the longer an organ can be stored, the less transplantation depends on urgent logistics, private flights, and the random matching of donor and recipient;
– cold slows down chemotherapy, but ice destroys cells, so simple freezing doesn't work for complex tissues;
– anti-ice substances are often toxic, so it's important not only to cool, but also to deliver protection inside the tissue, preserve function, and then properly warm it up;
– if we learn to reliably store organs and tissues, they can not only be transported, but also examined, restored, and prepared for transplantation; This is also important for research: samples, organoids, and tissues can be stored and transported without loss of quality, making it easier to collect data and reproduce experiments.
Xiaoxi Wei spoke about Xtherma and storing pig kidneys at -5°C for 72 hours with successful transplantation. John Bischof, Greg Fahy, and Alex German demonstrated that vitrification progresses from individual cells to large organs and neural tissue. Gloria Elliott from ARPA-H described the storage infrastructure in more detail.
Some of the presentations continued the theme of AI in biology.
Derya Unutmaz said that a new biomedical infrastructure is currently emerging: data at the levels of molecules, cells, tissues, organs, organisms, and populations are being connected with AI, robotic laboratories, and digital twins.
This echoes Morgan Levine's presentation from day two. Aging cannot be understood from a single level of data. Context is needed: cells, tissues, organs, organisms, and the environment around them.
Derya is talking about a similar gap. If you collect enough multimodal data and close the "model proposed, lab verified, data fed back into the model" cycle, you can significantly speed up the search for interventions.
The key delay for him is the path from discovery to clinical application, especially clinical trials. Even if the lab quickly finds something useful, it takes years to reach the patient. Derya suggests reducing this delay using AI, automated labs, virtual clinical trials, and digital twins.
Laurence Ion, pointing to the same problem, proposed Viva City: a city or special jurisdiction where new approaches to longevity can be tested more quickly, a community can be assembled, trials can be launched, and the system can't change itself.
We're good at seeing the risk of a bad drug being approved too quickly, but we're less aware of the opposite risk—people dying while potentially useful therapies wait years for access, funding, or approval.
Therefore, Laurence proposes not only reforming the existing system but also building a new environment around it: with a faster feedback loop, rigorous safety testing, self-tracking, independent substance testing, and data collection from people already experimenting with their health.
By the way, he invited everyone to join the project: join the community, help with coordination, write posts, repost from the project channel, or participate in other tasks.
Links:
Laurence Ion: https://www.linkedin.com/in/laurenceion/
Viva City: https://viva.city/
Viva City
Viva.City is a community building a new city to make death optional.
Yes, we're building a permanent city for people who prioritize longevity!
Yes, we're building a permanent city for people who prioritize longevity!
Vitalist Bay. Day 4.
Day 4 was again largely devoted to biostasis and its role in longevity infrastructure.
Several themes recurred across various presentations:
– Biostasis can be viewed as an extension of emergency medicine. If medicine today cannot help a person, the goal is to preserve them until a cure becomes available;
– Cryonics was considered as part of a broader set of approaches to aging and biostasis. Various models were discussed: classical cryopreservation, chemical fixation, brain preservation, and pet cryonics;
– The quality of cell, tissue, and organ preservation. Perfusion, ischemia, CT scanning, damage assessment, and comparisons of cryopreservation and chemical fixation were discussed. Briefly: cryopreservation emphasizes low-temperature preservation and potential biological recovery, while chemical fixation emphasizes the preservation of structure and information, possibly for future scanning or emulation;
– how to preserve not just tissue form, but also information and function. Especially when it comes to the brain. For the brain, this means preserving synapses, connections between neurons, molecular traces, and other structures that may be associated with memory and personality;
Nathan Cheng, in his technical roadmap, also identified biostasis as one of three major approaches to solving the problem of aging. Biostasis buys time and doesn't require understanding all the mechanisms of aging. Replacement attempts to circumvent aging by replacing old parts with young ones. General bioengineering is the most complex approach: understanding all cellular and molecular changes and learning to control them.
Retro Biosciences' goal is to add 10 healthy years to people. Their approach includes both replacing old cells with young ones and rejuvenating old cells within the body. Cryopreservation is also part of their solution.
They currently have several areas of focus.
The first is HSCs, or hematopoietic stem cells. These are the cells in the bone marrow that form blood and much of the immune system. With age, the immune system weakens, and Retro's idea is to give older people a younger version of their own hematopoietic system.
Importantly, the cells are taken from the patient themselves. They are reprogrammed to a pluripotent state, then converted back to HSCs and returned. This reduces the risk of rejection because the DNA remains the patient's own.
These cells must be produced in advance, frozen, carefully transported, thawed, and administered to the patient. If the cells are damaged during storage or thawing, the entire therapy falls apart.
The second area is autophagy. This is the cellular waste recycling system: old and broken proteins are disassembled and reused. This process deteriorates with aging and Alzheimer's disease. Retro is developing a small molecule, RTR242, which is designed to restore autophagy in aging cells, cross the blood-brain barrier, and reduce the accumulation of damaged proteins. A clinical trial is already underway in Australia.
The third area is microglia. These are the brain's immune cells. Their health is important for neurodegeneration, inflammation, and possibly for brain aging. The microglia program is expected to enter the clinic later this year.
Day 4 was again largely devoted to biostasis and its role in longevity infrastructure.
Several themes recurred across various presentations:
– Biostasis can be viewed as an extension of emergency medicine. If medicine today cannot help a person, the goal is to preserve them until a cure becomes available;
– Cryonics was considered as part of a broader set of approaches to aging and biostasis. Various models were discussed: classical cryopreservation, chemical fixation, brain preservation, and pet cryonics;
– The quality of cell, tissue, and organ preservation. Perfusion, ischemia, CT scanning, damage assessment, and comparisons of cryopreservation and chemical fixation were discussed. Briefly: cryopreservation emphasizes low-temperature preservation and potential biological recovery, while chemical fixation emphasizes the preservation of structure and information, possibly for future scanning or emulation;
– how to preserve not just tissue form, but also information and function. Especially when it comes to the brain. For the brain, this means preserving synapses, connections between neurons, molecular traces, and other structures that may be associated with memory and personality;
Nathan Cheng, in his technical roadmap, also identified biostasis as one of three major approaches to solving the problem of aging. Biostasis buys time and doesn't require understanding all the mechanisms of aging. Replacement attempts to circumvent aging by replacing old parts with young ones. General bioengineering is the most complex approach: understanding all cellular and molecular changes and learning to control them.
Retro Biosciences' goal is to add 10 healthy years to people. Their approach includes both replacing old cells with young ones and rejuvenating old cells within the body. Cryopreservation is also part of their solution.
They currently have several areas of focus.
The first is HSCs, or hematopoietic stem cells. These are the cells in the bone marrow that form blood and much of the immune system. With age, the immune system weakens, and Retro's idea is to give older people a younger version of their own hematopoietic system.
Importantly, the cells are taken from the patient themselves. They are reprogrammed to a pluripotent state, then converted back to HSCs and returned. This reduces the risk of rejection because the DNA remains the patient's own.
These cells must be produced in advance, frozen, carefully transported, thawed, and administered to the patient. If the cells are damaged during storage or thawing, the entire therapy falls apart.
The second area is autophagy. This is the cellular waste recycling system: old and broken proteins are disassembled and reused. This process deteriorates with aging and Alzheimer's disease. Retro is developing a small molecule, RTR242, which is designed to restore autophagy in aging cells, cross the blood-brain barrier, and reduce the accumulation of damaged proteins. A clinical trial is already underway in Australia.
The third area is microglia. These are the brain's immune cells. Their health is important for neurodegeneration, inflammation, and possibly for brain aging. The microglia program is expected to enter the clinic later this year.
Neuroscientist and AI pioneer Marvin Minsky—the man who created one of the first neural network machines back in 1951 and co-founded the MIT Artificial Intelligence Lab with John McCarthy—arrived at a deeply uncomfortable idea about the human mind.
In his book, The Society of Mind, he argued that humans have no single self or central intelligence. What we perceive as a coherent personality is actually the result of thousands of small, specialized processes within the brain. They constantly compete, argue, suppress each other, and temporarily merge. Essentially, humans are not a single thinker, but the result of the internal conflict of multiple systems.
Minsky wrote that intelligence arises not from a single, ideal mechanism, but from a vast diversity of these internal "agents." There is no central observer behind the eyes calmly making rational decisions. Rather, the brain is like an endless parliamentary session.
This idea sheds new light on many things. Procrastination isn't just laziness. It's a conflict between a system that understands long-term consequences and a system that seeks comfort right now. Weak willpower isn't necessarily a character flaw, but a situation where one small part of the brain is overwhelmed by dozens of other impulses.
Minsky's idea that we understand the least the processes the brain performs best was particularly important. Recognizing faces, understanding speech, navigating spatially, or navigating through a crowd seem effortless only because they rely on incredibly complex automatic neural systems operating outside of conscious control.
This leads to a practical conclusion: the problem of self-control is solved not only by "willpower" but by changing the environment. Removing the phone from the room is often more effective than trying to ignore it. Simple restrictions and habits work better than motivation because they change the balance of the brain's internal systems.
As a result, Minsky arrived at a paradoxical idea: the human mind is not a unified consciousness, but a noisy city of many competing processes without a single control center. And people who understand this stop trying to “break themselves with discipline” and instead begin to restructure their own environment and behavior so that the right brain systems win automatically.
In his book, The Society of Mind, he argued that humans have no single self or central intelligence. What we perceive as a coherent personality is actually the result of thousands of small, specialized processes within the brain. They constantly compete, argue, suppress each other, and temporarily merge. Essentially, humans are not a single thinker, but the result of the internal conflict of multiple systems.
Minsky wrote that intelligence arises not from a single, ideal mechanism, but from a vast diversity of these internal "agents." There is no central observer behind the eyes calmly making rational decisions. Rather, the brain is like an endless parliamentary session.
This idea sheds new light on many things. Procrastination isn't just laziness. It's a conflict between a system that understands long-term consequences and a system that seeks comfort right now. Weak willpower isn't necessarily a character flaw, but a situation where one small part of the brain is overwhelmed by dozens of other impulses.
Minsky's idea that we understand the least the processes the brain performs best was particularly important. Recognizing faces, understanding speech, navigating spatially, or navigating through a crowd seem effortless only because they rely on incredibly complex automatic neural systems operating outside of conscious control.
This leads to a practical conclusion: the problem of self-control is solved not only by "willpower" but by changing the environment. Removing the phone from the room is often more effective than trying to ignore it. Simple restrictions and habits work better than motivation because they change the balance of the brain's internal systems.
As a result, Minsky arrived at a paradoxical idea: the human mind is not a unified consciousness, but a noisy city of many competing processes without a single control center. And people who understand this stop trying to “break themselves with discipline” and instead begin to restructure their own environment and behavior so that the right brain systems win automatically.
A portable device that detects cancer from a drop of blood has been invented in China.
Soon, just a single drop of blood will be able to detect cancer at an early stage, according to Dr. Wen Liaoyun of Westlake University in Hangzhou.
Sensitive and rapid detection of disease biomarkers is crucial for early diagnosis and treatment monitoring. However, many existing biomonitoring technologies face a fundamental challenge: highly sensitive optical sensors often require bulky instruments, precise spectral measurements, and fragile nanostructures, while miniaturized systems typically suffer from performance limitations.
Wen Liaoyun's team has created a portable cancer cell detection system that fits in the palm of your hand. The accuracy is approximately 10,000 times higher than traditional methods. The results of the study were published in the journal Nature Photonics.
This work presents a scalable and robust nanophotonic biosensing paradigm for miniaturized, high-throughput diagnostics in clinical, remote, and at-home settings, the article states.
To demonstrate the practical utility of the technology, the scientists used it to detect very small particles released by body cells. These particles, called extracellular vesicles (sEVs), contain information about the cell's state, including potential diseases such as cancer.
The new QMRS system was able to detect these particles, associated with lung cancer, in extremely small quantities—in just 15 minutes. The researchers then tested the technology on 171 patient blood samples. The system analyzed specific biomarkers associated with lung cancer and distinguished between patients with lung cancer and healthy controls with high accuracy. Diagnostic accuracy reached nearly 95%. The method also demonstrated good performance in monitoring patients after surgery, helping to assess the effectiveness of treatment.
Soon, just a single drop of blood will be able to detect cancer at an early stage, according to Dr. Wen Liaoyun of Westlake University in Hangzhou.
Sensitive and rapid detection of disease biomarkers is crucial for early diagnosis and treatment monitoring. However, many existing biomonitoring technologies face a fundamental challenge: highly sensitive optical sensors often require bulky instruments, precise spectral measurements, and fragile nanostructures, while miniaturized systems typically suffer from performance limitations.
Wen Liaoyun's team has created a portable cancer cell detection system that fits in the palm of your hand. The accuracy is approximately 10,000 times higher than traditional methods. The results of the study were published in the journal Nature Photonics.
This work presents a scalable and robust nanophotonic biosensing paradigm for miniaturized, high-throughput diagnostics in clinical, remote, and at-home settings, the article states.
To demonstrate the practical utility of the technology, the scientists used it to detect very small particles released by body cells. These particles, called extracellular vesicles (sEVs), contain information about the cell's state, including potential diseases such as cancer.
The new QMRS system was able to detect these particles, associated with lung cancer, in extremely small quantities—in just 15 minutes. The researchers then tested the technology on 171 patient blood samples. The system analyzed specific biomarkers associated with lung cancer and distinguished between patients with lung cancer and healthy controls with high accuracy. Diagnostic accuracy reached nearly 95%. The method also demonstrated good performance in monitoring patients after surgery, helping to assess the effectiveness of treatment.
Yesterday, May 24, the first Enhanced Games took place in Las Vegas—a competition where, for the first time in history, athletes were openly permitted to use doping under medical supervision.
Organizers called it a "new model of sport," while critics called it a legalized doping Olympics. The main result: Greek athlete Christian Gkolomeyev swam the 50m freestyle in 20.81, breaking the official world record, but sports federations do not recognize this record due to doping and prohibited equipment.
The Enhanced Games published aggregated statistics from their clinical program.
Here's what, according to Enhanced themselves, the participants used:
91%—testosterone or testosterone esters
The basis of a hormonal "enhancement." These forms are typically used to increase strength, power, muscle mass, and accelerate recovery. Esters are variants of testosterone with varying durations of action. 79% — growth hormone, hGH
Used for recovery, body composition, support of tendons and tissues, and tolerance to high training volumes.
62% — stimulants, such as Adderall
This promotes energy, concentration, reaction time, and competitive drive. The downside is the strain on the nervous and cardiovascular systems.
50% — metabolic modulators, such as anastrozole
This is often not a direct "booster," but rather an auxiliary part of the protocol: managing hormonal levels and the side effects of anabolic steroids.
41% — EPO
Classic blood doping: it stimulates red blood cell production, allowing the blood to carry more oxygen, which can improve endurance. One of the main risks is blood thickening and cardiovascular complications. 29% — anabolic steroids, such as Deca-Durabolin
Used for strength, muscle mass, and recovery after heavy training. Risks include hormonal imbalances, heart problems, mental health issues, fertility issues, and other body systems.
5% — hormonal support, such as hCG
More of a "maintenance" element of the protocol than a standalone performance booster: used to support the endocrine system while hormonal interventions are in place.
What would you add?
Organizers called it a "new model of sport," while critics called it a legalized doping Olympics. The main result: Greek athlete Christian Gkolomeyev swam the 50m freestyle in 20.81, breaking the official world record, but sports federations do not recognize this record due to doping and prohibited equipment.
The Enhanced Games published aggregated statistics from their clinical program.
Here's what, according to Enhanced themselves, the participants used:
91%—testosterone or testosterone esters
The basis of a hormonal "enhancement." These forms are typically used to increase strength, power, muscle mass, and accelerate recovery. Esters are variants of testosterone with varying durations of action. 79% — growth hormone, hGH
Used for recovery, body composition, support of tendons and tissues, and tolerance to high training volumes.
62% — stimulants, such as Adderall
This promotes energy, concentration, reaction time, and competitive drive. The downside is the strain on the nervous and cardiovascular systems.
50% — metabolic modulators, such as anastrozole
This is often not a direct "booster," but rather an auxiliary part of the protocol: managing hormonal levels and the side effects of anabolic steroids.
41% — EPO
Classic blood doping: it stimulates red blood cell production, allowing the blood to carry more oxygen, which can improve endurance. One of the main risks is blood thickening and cardiovascular complications. 29% — anabolic steroids, such as Deca-Durabolin
Used for strength, muscle mass, and recovery after heavy training. Risks include hormonal imbalances, heart problems, mental health issues, fertility issues, and other body systems.
5% — hormonal support, such as hCG
More of a "maintenance" element of the protocol than a standalone performance booster: used to support the endocrine system while hormonal interventions are in place.
What would you add?