Longevity InTime: Autonomous AI Institute. Anti-Aging Digital Health Immortality Transhumanist AI Channel

• Following the sale of CrossBridge Bio to Eli Lilly for up to $300 million, Michael Torres offers VITARNA token holders the same path from a university project to a deal with Big Pharma. https://www.pharmaceutical-technology.com/news/lilly-to-acquire-crossbridge-bio/

• Nautilus took on a study of 15,836 ancient genomes from Western Eurasia and shifted the conversation from gene evolution to the evolution of cultural systems. https://www.nature.com/articles/s41586-026-10358-1 https://nautil.us/humans-evolving-one-way-or-another-1279967/

Following the death of Alcor employee and cryonics historian Mike Perry, the organization deployed its emergency response team, transported him to its center, and reported cryopreservation. On April 15, Perry, who had worked at Alcor since the late 1980s, was involved in a pedestrian accident and did not recover after resuscitation. In a post about his death dated April 15, Alcor stated that its DART team was on standby, transported him to its Scottsdale facility, and performed cryopreservation. An independent technical report on the quality of the procedure has not yet been released. Perry himself is important in this story. He spent nearly forty years monitoring Alcor patients, keeping journals, maintaining liquid nitrogen vessels, and writing a history of the field. In a 2014 update to his text on early cryonics failures, he formulated his mission as follows: "These disasters need to be documented, if only to make such mistakes less frequent." This phrase has practical implications. Perry wrote about early cryonics failures precisely as a chain of mundane glitches. The body remains warm for a long time, the team is late, the hospital delays access, transport is delayed, and then arguing about personality preservation becomes much more difficult. In March, The Guardian's analysis of a film about cryonics reduced the topic to the same point: the minutes during which the brain can still be protected from decay. Therefore, Alcor specifically describes DART, its full-time mobile team, as a standby, stabilization, and transport team. On this page, the organization writes that it assembled an internal DART team in 2023. Perry's case tests precisely this layer. According to Alcor, he was declared dead on April 15 at 9:59 AM. DART was already on standby, after which he was quickly transported to the center. Alcor co-founder Linda Chamberlain described the day this way: "Everyone at Alcor did everything possible to ensure Mike received the best possible cryopreservation." It's a powerful statement, but for now, it's only Alcor's own. There's no public report detailing the timing, temperature, quality of perfusion (i.e., the pumping of the cryoprotective solution through the vessels), or a list of interventions. Without it, it's impossible to verify how long it took before cooling began and how well the entire team—resuscitation, transport, and the lab—worked. In cryonics, this is no small matter. It's during this window that the brain loses structure due to ischemia, or lack of blood flow and oxygen. So the news here is more than just an obituary. On April 15, Alcor had to implement its own route on a man who had cared for its patients for nearly forty years. Days like these show cryonics at its most mundane, as a work of people, machines, and protocols that must deliver on time even on the worst possible day.

Superintelligence won't remove the main brake on longevity: biology needs data, experiments, and clinical feedback, writes Bo Wang. On March 19, Bo Wang, a professor at the University of Toronto and chief AI scientist at University Health Network, supported Jeffrey Miller's thesis that the success of language models doesn't automatically translate to aging. For text and code, models had internet-scale corpora and fast response verification. For the human body, data is sparse, scattered, and returns results too slowly. ASI, or hypothetical superintelligence, is often described as a universal accelerator for biomedicine. The logic is clear: if a model becomes smarter than the best researchers, it will find targets, molecules, and treatment regimens faster. Bo Wang points to the next link in this chain. For longevity, it's not enough to come up with a hypothesis; it also needs to be tested on the human body. Language models have grown on billions of existing examples. Biomedicine works with closed medical records, small cohorts, and various protocols and outcomes that cannot be summarized into a single, clean dataset. In longevity research, the challenge is even greater: the central question sounds simple—who lived longer and why—but the answer unfolds over years. Therefore, the field requires surrogate metrics, that is, indicators that allow one to assess the risk of aging before the patient's death. Even such metrics are slowly validated: in osteoporosis, the SABRE program sought recognition of bone mineral density as a surrogate indicator over 12 years, even though the data itself already existed. Feedback is a similar story. The chatbot receives an assessment of the response almost immediately. A drug candidate first undergoes testing in cells and animals, then the first stage of human trials, where safety is assessed, followed by patient recruitment and months of observation. Biology, logistics, and regulatory processes dictate the calendar time here. Asimov Press recently described this clinical ceiling for AI: even a very good drug still faces limitations in terms of patient recruitment, side effects, logistics, and regulatory requirements. "To cure cancer, we don't need a magic oracle. We need more experiments, more data, more clinical trials. These are bought with money, not conjured," wrote Bo Wang. Bo Wang added a financial argument: he estimates that the current hundreds of billions going to ASI would increase funding for longevity research by approximately 100 times if it were directed directly to this field. This is a rough estimate. The field needs a machine for discovering the truth about humans: long-term biobanks, uniform measurement protocols, surrogate aging indicators, and clinical pathways where a signal is visible before death or severe complications occur. Without this infrastructure, AI will become increasingly adept at proposing hypotheses and will continue to wait for slow biology to respond.

Eli Lilly is buying Insilico Medicine https://www.ftc.gov/legal-library/browse/early-termination-notices/20261000

Xenotransplantation case: transplantation of organ (liver) from one bio kind (pig) to human: https://doi.org/10.1016/j.jhep.2025.08.044

https://www.nature.com/articles/s41586-026-10235-x Now, immune cells can be "reprogrammed" directly within the body during cancer treatment, without being removed. Currently, CAR-T therapy works like this: T cells are removed from the patient, sent to a lab, genetically modified, and then returned. This takes weeks and costs approximately $400,000-$500,000. Additionally, the patient often needs to undergo intensive chemotherapy before the procedure. The new approach eliminates all of this. Instead of complex logistics, it uses a system of two nanoparticles administered in a single injection. The first particle delivers the famous CRISPR-Cas9, the "molecular scissors" that cut DNA at a precise location, to the T cells. The second particle carries the CAR gene itself—the instructions that transform a normal T cell into a cancer-killing cell. The key technical breakthrough is that DNA insertion occurs precisely at a specific point in the genome, rather than randomly, as in classical methods. This reduces the risk of side effects, including secondary tumors. The system is also configured to work only in T cells. This is achieved through a kind of "molecular switch" that is activated only in these cells. This is critical because the body has no way to filter out incorrectly modified cells, as is done in the laboratory. The results in mice with a "humanized" immune system appear very strong. After a single injection, cancer disappeared in almost all animals in just two weeks. Modified T cells accounted for up to 40 percent of immune cells in some organs and effectively destroyed tumors in both the bone marrow and spleen. The method worked not only against leukemia, but also against multiple myeloma and sarcoma—this is especially important because solid tumors typically respond poorly to CAR-T therapy. Interestingly, T cells created directly in the body were even more effective than those produced in the laboratory. It is believed that when grown outside the body, the cells lose some of their properties—their so-called "stemness" and their ability to proliferate. This does not occur inside the body.