Uploaded October 2025 | Updated September 2026, 3 weeks ago
In this episode, I speak with Sho Joseph Ozaki Tan, founder of the Public Longevity Group (PLG), about an often overlooked challenge in longevity research: public perception and trust. Sho explains how PLG uses cultural intelligence to map sentiment across the US, analysing everything from Google search trends to Reddit discussions and media coverage. We explore their fascinating findings: longevity interest clusters heavily in wealthy, educated, liberal regions, while vast swaths of America remain sceptical or unengaged.
We discuss the challenges of data reliability in the age of bots and AI, the five narrative archetypes PLG has identified, and whether cultural change should precede or follow scientific breakthroughs. This conversation reveals how public trust might be the missing ingredient for bringing longevity advances to society.
Support PLG's campaign: lifespan.io/plgcampaign?utm_source=youtube&utm_medium=shikiscience&utm_campaign=plg_2025)
Learn more / contact: plg@lifespan.io
LinkedIn: linkedin.com/company/public-longevity-group
X: https://x.com/PublicLongevity
Find me on Twitter - twitter.com/EleanorSheekey
Support the channel
through PayPal - paypal.me/sheekeyscience?country.x=GB&locale.x=en_GB
through Patreon - patreon.com/TheSheekeyScienceShow
TIMESTAMPS
00:00 – Intro & Project Overview
00:35 – What problem is PLG solving?
05:55 – How to measure “cultural intelligence”
10:40 – Early U.S. findings: demographics & geography
17:45 – Media and Reddit sentiment analysis
20:55 – Twitter/X and “hot moments” in longevity
30:20 – Turning insights into A/B-tested narratives
36:30 – Research vs. cultural challenges
40:30 – Closing thoughts & where to learn more
Please note that The Sheekey Science Show is distinct from Eleanor Sheekey's teaching and research roles. The information provided in this show is not medical advice, nor should it be taken or applied as a replacement for medical advice. The Sheekey Science Show and guests assume no liability for the application of the information discussed.
Icons in intro; "freepik.com/free-photos-vectors/background"Background vector created by freepik - freepik.com
In this episode, I speak with Sho Joseph Ozaki Tan, founder of the Public Longevity Group (PLG), about an often overlooked challenge in longevity research: public perception and trust. Sho explains how PLG uses cultural intelligence to map sentiment across the US, analysing everything from Google search trends to Reddit discussions and media coverage. We explore their fascinating findings: longevity interest clusters heavily in wealthy, educated, liberal regions, while vast swaths of America remain sceptical or unengaged.
We discuss the challenges of data reliability in the age of bots and AI, the five narrative archetypes PLG has identified, and whether cultural change should precede or follow scientific breakthroughs. This conversation reveals how public trust might be the missing ingredient for bringing longevity advances to society.
Support PLG's campaign: lifespan.io/plgcampaign?utm_source=youtube&utm_medium=shikiscience&utm_campaign=plg_2025)
Learn more / contact: plg@lifespan.io
LinkedIn: linkedin.com/company/public-longevity-group
X: https://x.com/PublicLongevity
Find me on Twitter - twitter.com/EleanorSheekey
Support the channel
through PayPal - paypal.me/sheekeyscience?country.x=GB&locale.x=en_GB
through Patreon - patreon.com/TheSheekeyScienceShow
TIMESTAMPS
00:00 – Intro & Project Overview
00:35 – What problem is PLG solving?
05:55 – How to measure “cultural intelligence”
10:40 – Early U.S. findings: demographics & geography
17:45 – Media and Reddit sentiment analysis
20:55 – Twitter/X and “hot moments” in longevity
30:20 – Turning insights into A/B-tested narratives
36:30 – Research vs. cultural challenges
40:30 – Closing thoughts & where to learn more
Please note that The Sheekey Science Show is distinct from Eleanor Sheekey's teaching and research roles. The information provided in this show is not medical advice, nor should it be taken or applied as a replacement for medical advice. The Sheekey Science Show and guests assume no liability for the application of the information discussed.
Icons in intro; "freepik.com/free-photos-vectors/background"Background vector created by freepik - freepik.com

![the 3 levels of aging therapeutics
Why do so many anti-aging drugs work in mice but fail in humans? It turns out, we might be aging in fundamentally different ways. A new minimal model from physicists Peter Fedichev and Jan Gruber suggests that aging isnt a chaotic mess of billions of problems, but a process governed by just three macroscopic variables.
In this video, I break down their paper to explain why current Level 1 interventions (like senolytics and cellular reprogramming) might only improve healthspan, not maximum lifespan. Well explore the difference between stable species (humans) and unstable ones (mice), and reveal the physics-based roadmap—Level 2 and Level 3—required to actually extend the human lifespan limit beyond 120 years.
Find me on Twitter - https://twitter.com/EleanorSheekey
Support the channel
through PayPal - https://paypal.me/sheekeyscience?country.x=GB&locale.x=en_GB
through Patreon - https://www.patreon.com/TheSheekeyScienceShow
TIMESTAMPS
0:00 – Why 300 aging theories might be wrong (The Physics of Universality)
3:45 – The 3 variables that control your lifespan
7:12 – Why mice are biologically broken compared to humans
9:58 – The 3 Levels of Intervention: Why reprogramming isnt enough (yet)
13:20 – The only way to actually break the 120-year limit
REFERENCES
Fedichev, P. & Gruber, J. (2024). A Minimal Model Explains Aging Regimes and Guides Intervention Strategies. bioRxiv. [Preprint]
Pyrkov, T. V., et al. (2021). Longitudinal analysis of blood markers reveals progressive loss of resilience and predicts ultimate limit of human lifespan. Nature Communications, 12, 2765.
Avchaciov, K., et al. (2022). Unsupervised learning of aging principles from longitudinal data. Nature Communications, 13, 6529.
Perevoshchikova, K. & Fedichev, P. O. (2024). Differential Responses of Dynamic and Entropic Aging Factors to Longevity Interventions. bioRxiv.
Tarkhov, A. E., et al. (2024). Universal transcriptomic signature of age reveals the temporal scaling of Caenorhabditis elegans aging trajectories. AgingBio, 2, 1–16.
Tong, H., et al. (2024). Quantifying the stochastic component of epigenetic aging. Nature Aging, 4, 886–897.
Meyer, D. H. & Schumacher, B. (2024). Biologically informative or merely random? The stochastic nature of epigenetic clocks. Nature Aging, 4, 871–885.
Sinclair, D. A. & Guarente, L. (1997). Extrachromosomal rDNA circles—a cause of aging in yeast. Cell, 91(7), 1033–1042.
Medvedev, Z. A. (1990). An attempt at a rational classification of theories of ageing. Biological Reviews, 65(3), 375–398.
Please note that The Sheekey Science Show is distinct from Eleanor Sheekeys teaching and research roles. The information provided in this show is not medical advice, nor should it be taken or applied as a replacement for medical advice. The Sheekey Science Show and guests assume no liability for the application of the information discussed.
Icons in intro; https://www.freepik.com/free-photos-vectors/backgroundBackground vector created by freepik - www.freepik.com the 3 levels of aging therapeutics](https://i.ytimg.com/vi/c-_Pdp5IIvw/mqdefault.jpg)








