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Longevity gene therapy and AI – What is on the horizon?
Gene therapy holds promise for extending human lifespan and enhancing healthspan by targeting genes associated with aging processes. Longevity gene therapy, particularly interventions focusing on genes like TERT (telomerase reverse transcriptase), Klotho, and Myostatin, is at the forefront of experimental research. Companies such as Bioviva, Libella, and Minicircle are pioneering these interventions, albeit with varying degrees of transparency and scientific rigor.
TERT, Klotho, and Myostatin in Longevity
TERT: The TERT gene encodes for an enzyme essential in telomere maintenance, which is linked to cellular aging. Overexpression of TERT in model organisms has shown potential in lengthening telomeres, potentially delaying aging.
Klotho: This gene plays a crucial role in regulating aging and lifespan. Klotho protein has been associated with multiple protective effects against age-related diseases.
Myostatin: Known for its role in regulating muscle growth, inhibiting Myostatin can result in increased muscle mass and strength, which could counteract some age-related physical decline.
The Experimental Nature of Longevity Gene Therapy
The application of gene therapy for longevity remains largely experimental. Most available data come from preclinical studies, primarily in animal models. Human data are scarce, raising questions about efficacy, safety, and potential long-term effects. The ethical implications of these experimental treatments, especially in the absence of robust data, are significant, touching on issues of access, consent, and potential unforeseen consequences.
Companies Offering Longevity Gene Therapy
Bioviva: Notably involved in this field, Bioviva has been vocal about its endeavors in gene therapy for aging. While they have published some data from mouse studies, human data remain limited.
Libella and Minicircle: These companies also offer longevity gene therapies but face similar challenges in providing comprehensive human data to back their claims.
Industry Perspective vs. Public Discourse
The discourse around longevity gene therapy is predominantly shaped by those within the industry, such as Liz Parrish of Bioviva and Bryan Johnson. While their insights are valuable, they may also be biased towards promoting their interventions. The lack of widespread discussion on platforms like Reddit and Twitter, especially from independent sources or those outside the industry, points to a need for greater transparency and peer-reviewed research.
Ethical and Regulatory Considerations
The ethical and regulatory landscape for gene therapy is complex, particularly for treatments aimed at non-disease conditions like aging. The experimental status of longevity gene therapies raises significant ethical questions, particularly around informed consent and the potential long-term impacts. Regulatory bodies are tasked with balancing the potential benefits of such innovative treatments against the risks and ethical concerns, requiring a robust framework for clinical trials and approval processes.
Longevity Gene Therapy and AI
Integrating Artificial Intelligence (AI) into longevity gene therapy represents a groundbreaking intersection of biotechnology and computational sciences. AI and machine learning algorithms are increasingly employed to decipher complex biological data, predict the impacts of genetic modifications, and optimize therapy designs. In the context of longevity gene therapy, AI can analyze vast datasets from genomics, proteomics, and metabolomics to identify new therapeutic targets, understand the intricate mechanisms of aging, and predict individual responses to gene therapies. This computational power enables researchers to simulate the effects of gene editing or modulation before actual clinical application, enhancing the precision and safety of therapies. Furthermore, AI-driven platforms facilitate the personalized tailoring of gene therapy interventions, taking into account the unique genetic makeup of each individual, which is crucial for effective and minimally invasive treatment strategies. The synergy between AI and longevity gene therapy accelerates the pace of discovery and development in this field, promising more rapid translation of research findings into clinical applications that could extend human healthspan and lifespan.
For longevity gene therapy to advance from experimental to accepted medical practice, several key developments are needed:
Robust Human Clinical Trials: Rigorous, peer-reviewed clinical trials involving human participants are essential to establish the safety and efficacy of gene therapies for longevity.
Transparency and Peer Review: Open sharing of data and peer-reviewed publication of results can help build credibility and foster a more informed public discourse.
Ethical and Regulatory Frameworks: Developing clear ethical guidelines and regulatory pathways for these therapies will be crucial in ensuring they are deployed responsibly.
The future of longevity gene therapy is fraught with challenges but also holds immense promise. As the field evolves, a multidisciplinary approach involving scientists, ethicists, regulators, and the public will be crucial in realizing its potential in a responsible and beneficial manner.
Longevity gene therapy and AI: Annex
What are the top 10 most promising potential longevity therapies being researched?
I think the idea of treating aging as a disease that’s treatable and preventable in some ways is a really necessary focus. The OP works with some of the world’s top researchers using HBOT as part of that process to increase oxygen in the blood and open new pathways in the brain to address cognitive decline and increase HealthSpan (vs. just lifespan). Pretty cool stuff!
HBOT in longevity research stands for “hyperbaric oxygen therapy.” It has been the subject of research for its potential effects on healthy aging. Several studies have shown that HBOT can target aging hallmarks, including telomere shortening and senescent cell accumulation, at the cellular level. For example, a prospective trial found that HBOT can significantly modulate the pathophysiology of skin aging in a healthy aging population, indicating effects such as angiogenesis and senescent cell clearance. Additionally, research has demonstrated that HBOT may induce significant senolytic effects, including increasing telomere length and decreasing senescent cell accumulation in aging adults. The potential of HBOT in healthy aging and its implications for longevity are still being explored, and further research is needed to fully understand its effects and potential applications.
2- Are they also looking into HBOT as a treatment for erectile dysfunction?
Definitely! Dr. Shai Efrati has been doing research around that and had a study published in the Journal of Sexual Medicine. Dr. Efrati and his team found that 80% of men “reported improved erections” after HBOT therapy: https://www.nature.com/articles/s41443-018-0023-9
Cellular rejuvenation aka partial reprogramming (as someone else already said) but not just by Yamanaka (OSKM) factors or cocktail variants but also by other novel Yamanaka-factor alternatives.
I see a lot of people saying reprogramming, and I think the idea is promising but as someone who worked on reprogramming cells in vitro I can tell you that any proof of concepts in vivo large animal models is far aways.
7- I think plasmapheresis is a technology most likely to be proven beneficial in the near term and also a technology that can be scaled and offered for reasonable prices.
8- Bioelectricity, if we succeed in interpreting the code of electrical signals By which cells communicate , we can control any tissue growth and development including organs regeneration
9- Gene therapy and reprogramming will blow the lid off the maximum lifespan. Turning longevity genes on/expressing proteins that repair cellular damage and reversing epigenetic changes that occur with aging.
10- I don’t think anything currently being researched (that we know of) has the potential to take us to immortality. That’ll likely end up requiring some pretty sophisticated nanotechnology. However, the important part isn’t getting to immortality, but getting to LEV. In that respect, I’d say senolytics and stem cell treatments are both looking pretty promising. (And can likely achieve more in combination than on their own.)
11- Spiroligomers to remove glucosepane from the ECM.
12- Yuvan Research. Look up the recent paper they have with Steve Horvath on porcine plasma fractions.
13- This OP thinks most of the therapies being researched will end up having insignificant effects. The only thing that looks promising to me is new tissue grown from injected stem cells or outright organ replacement. Nothing else will address DNA damage, which results in gene loss, disregulation of gene expression, and loss of suppression of transposable elements.
Altos Labs is a biotechnology research company focused on unraveling the deep biology of cell rejuvenation to reverse disease and develop life extension therapies that can halt or reverse the human aging process. The company’s goal is to increase the “healthspan” of humans, with longevity extension being an “accidental consequence” of their work. Altos Labs is dedicated to restoring cell health and resilience through cell rejuvenation to reverse disease, injury, and disabilities that can occur throughout life. The company is working on specialized cell therapies based on induced pluripotent stem cells to achieve these objectives. Altos Labs is known for its atypical focus on basic research without immediate prospects of a commercially viable product, and it has attracted significant investment, including a $3 billion funding round in January 2022. The company’s research is based on the fundamental biology of cell rejuvenation, aiming to understand and harness the ability of cells to resist stressors that give rise to disease, particularly in the context of aging.
16– not so much a “therapy”but I think research into growing human organs may be very promising long term. Being able to get organ transplants made from your own cells means zero rejection issues and no limitations of supply for transplants. Near term drugs like rampamycin show good potential for slowing the aging process and are in human trials.
What is biological reprogramming technology?
Biological reprogramming technology involves the process of converting specialized cells into a pluripotent state, which can then be directed to become a different cell type. This technology has significant implications for regenerative medicine, disease modeling, and drug discovery. It is based on the concept that a cell’s identity is defined by the gene regulatory networks that are active in the cell, and these networks can be controlled by transcription factors. Reprogramming can be achieved through various methods, including the introduction of exogenous factors such as transcription factors. The process of reprogramming involves the erasure and remodeling of epigenetic marks, such as DNA methylation, to reset the cell’s epigenetic memory, allowing it to be directed to different cell fates. This technology has the potential to create new cells for regenerative medicine and to provide insights into the fundamental basis of cell identity and disease.
See also
Gene Therapy Basics for foundational understanding of gene therapy techniques and applications.
Anthropic Introduces Claude 3.5 Sonnet with Visual PDF Analysis for Images, Charts, and Graphs under 100 Pages.[1] Quantum Machines and Nvidia use machine learning to get closer to an error-corrected quantum computer.[2] Runway goes 3D with new AI video camera controls for Gen-3 Alpha Turbo.[3] Scientists Use AI to Turn 134-Year-Old Photo Into 3D Model of Lost Temple Relief.[4] Sources: [1] https://analyticsindiamag.com/ai-news-updates/anthropic-introduces-claude-3-5-sonnet-with-visual-pdf-analysis-for-images-charts-and-graphs-under-100-pages/ [2] https://techcrunch.com/2024/11/02/quantum-machines-and-nvidia-use-machine-learning-to-get-closer-to-an-error-corrected-quantum-computer/ [3] https://venturebeat.com/ai/runway-goes-3d-with-new-ai-video-camera-controls-for-gen-3-alpha-turbo/ [4] https://gizmodo.com/scientists-use-ai-to-turn-134-year-old-photo-into-3d-model-of-lost-temple-relief-2000519484 submitted by /u/Excellent-Target-847 [link] [comments]
Spotlight- Nvidia (NVDA) to Replace Intel in the Dow Jones Industrial Average after Intel fail in the race of AI boom Perplexity launches an elections tracker Claude can now view images within a PDF submitted by /u/codeharman [link] [comments]
Hi All, I am trying to automate some of the work processes carried out by engineering companies in the oil and gas sector and the energy industry. I have over 20 years of experience in the industry as an engineer, project manager and commercial manager, and I have also started learning about AI since early this year. I have identified two separate Micro SaaS that can be started with, requiring both traditional machine learning and LLMs. I am therefore looking for a technical co-founder for the company, which I plan to set up early next year. I am ideally looking for a co-founder based in the UK like myself. However, I am also open to any other part of the EU that is tech-startup friendly and with significant energy industry players, such as Norway, Denmark, Netherlands, France, etc. So, if you have a technical background and interested, please let me know, so that we can discuss further. Thank you. submitted by /u/StrategyNo6493 [link] [comments]
Growing up as an autistic kid, I always had melodies play in my head. Symphonies and songs and beats and stuff. But I could never figure out if I had heard it in a song or if I had made it up. But there was no way to look it up. Decades later Shazam arrives, which can listen to songs, but you can't hum to it. Later something else came along that sort of had a 'sing into the mic' feature, but it barely worked. Either because I don't sing very well, or the Siri of song is even worse. So here, I'm asking: Has Ai helped with that? Imagine chatGPT as a person that always knew what song that was. Even though you can barely remember the lyrics or the melody and can barely sing. Is that a thing yet? submitted by /u/Aion2099 [link] [comments]
Super Micro’s $50 billion stock collapse underscores risk of AI hype.[1] Perplexity launches an elections tracker.[2] AI chatbots aren’t reliable for voting information, government officials warn.[3] Walt Disney forms business unit to coordinate use of AI, augmented reality.[4] Sources: [1] https://www.cnbc.com/2024/10/31/super-micros-50-billion-stock-collapse-underscores-risk-of-ai-hype.html [2] https://techcrunch.com/2024/11/01/perplexity-launches-an-elections-tracker/ [3] https://www.cnbc.com/2024/11/01/ai-chatbots-arent-reliable-for-voting-questions-government-officials.html [4] https://www.reuters.com/technology/artificial-intelligence/walt-disney-forms-business-unit-coordinate-use-ai-augmented-reality-2024-11-01/ submitted by /u/Excellent-Target-847 [link] [comments]
Avaliable at Sirius Model IIe Ok, so first of all I got a whole lot of AIs self prompting behind a login on my website and then I turned that into a reasoning model with Claude and other AI's. Claude turned out to be a fantastic reasoner but too expensive to run in that format so I thought I would do a public demo of a crippled reasoning model using only GPT-4o mini and three steps. I had a fear that this would create too much traffic but actually no, so I have taken off many of the restrictions and put it up to a max six steps of reasoning and user customisable sub-prompts. It looks something like this: The Sirius IIe model How it works: It sends the user prompt with a 'master' system message to an incidence of GPT-4o mini. It adds in a second part of the system message from one of the slots starting with slot one and the instance then provides the response. At the end of the response it can call another 'slot' of reasoning (typically slot 2) whereby It again prompts the API server with the master system message and the sub system message in 'slot 2' and it reads the previous context in the message also.and then provides the response and so on. Until it gets to six reasoning steps or provides the solution. At least I think that's how it works. You can make it work differently. submitted by /u/rutan668 [link] [comments]
Older AI models showed some capacity for generalization, but pre-O1 models weren't directly incentivized to reason. This fundamentally differs from humans: our limbic system can choose its reward function and reward us for making correct reasoning steps. The key distinction was that older models only received RLHF rewards based on outcomes, not the reasoning process itself. The current gap between humans and O1 models centers on flexibility: AI can't choose its reward function. This limitation impacts higher-level capabilities like creativity and autonomous goal-setting (like maximizing profit). We're essentially turning these models into reasoning engines. However, there are notable similarities between humans and AI: Both use "System 1" thinking: We generate sequences of pattern-matched data. In humans, we call this imagination; in models, we call it output. Imagination is essentially predicted output that isn't physically present. This is exactly what models do and what we do (relating to the thousand brains theory of columns). Both can potentially train on generated data. Models can use their outputs for further training (though this might require an evaluator function). Humans might do something similar during sleep. Both can improve System 1 thinking through evaluation. With an evaluator function, models can increase their generation performance to match their evaluation capabilities. This makes sense because it's typically easier to validate an answer than to generate a good one initially. Humans can do this too. The key aspect here is that while models are becoming more sophisticated reasoning engines, they still lack the flexible, self-directed reward systems that humans possess through their limbic systems. submitted by /u/PianistWinter8293 [link] [comments]
The relationship between human and artificial reasoning reveals an interesting tension in reward function design. While the human brain features a remarkably flexible reward system through its limbic system, current AI architectures rely on more rigid reward structures - and this might not be entirely negative. Consider O1's approach to reasoning: it receives rewards for both correct reasoning steps and achieving the right outcome. This rigid reward structure intentionally shapes the model toward step-by-step logical reasoning. It's like having a strict but effective teacher who insists on showing your work, not just getting the right answer. A truly adaptive reward system, similar to human cognition, would operate differently. It could: Dynamically focus attention on verifying individual reasoning steps Shift between prioritizing logical rigor and other objectives (elegance, novelty, clarity) Adjust its success criteria based on context Choose when to prioritize reasoning versus other goals However, this comparison raises an important question: Is full reward function adaptability actually desirable? The alignment problem - ensuring AI systems remain aligned with human values and interests - suggests that allowing models to modify their own reward functions could be risky. O1's rigid focus on reasoning steps might be a feature, not a bug. The human limbic system's flexibility is both a strength and a weakness. While it allows us to adaptively respond to diverse situations, it can also lead us to prioritize immediate satisfaction over logical rigor, or novelty over accuracy. O1's fixed reward structure, in contrast, maintains a consistent focus on sound reasoning. Perhaps the ideal lies somewhere in between. We might want systems that can flexibly allocate attention and adjust their evaluation criteria within carefully bounded domains, while maintaining rigid alignment with core objectives like logical consistency and truthfulness. This would combine the benefits of adaptive assessment with the safety of constrained optimization. submitted by /u/PianistWinter8293 [link] [comments]
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