Sunday, Oct 25, 2026
Dear George (Church), Akram (Baharlouei, Palo Alto Labs), Alex (Zhavoronkov), Petrina (Kamya), both of InSilico Medicine, Kejun (Ying, Stanford), Le Song (UAE Professor), Leanne Ye (Shanghai, China, and with possible Longevity Genetics' coding team / company, in Chinese, and re building a #RealsiticVirtualEarth for this) Peter (Norvig, Google AI), All,And further regarding this thread on thursday - https://scott-macleod.blogspot.com/2026/10/korean- white-forsythia-abeliophyllum. html - a forked Google Street View with a time slider, Harbin Hot Springs' gate house - and at the cell level especially and newly, and a#RealisticVirtualEarthFor #RVEforGenetics, a #RVEforAgingReversal a #RVEforExtremeLongevity ... &
a #DigitalPhysical #RealisticVirtualEarthForOSK, a forked #GStreetView w #GTimeslider & @ #Gcell, #Gmolecule, #Ggene #Gqubit etc view levels, how to #ReverseAging w emerging timelines pot. to 75%, 50% & 25% of 100 mice, dogs, humans that are old?
https://share.google/aimode/DFQL166bCi2vKz5rb ~
https://x.com/WorldUnivAndSch/status/2109334818643386471
https://x.com/WUaSPress/status/2109338601293283594
https://x.com/Q_YogaMacFlower/status/2109335267551383974
https://x.com/scottmacleod/status/2109338746135228547
Retweeting -
How with #YamanakaFactorsOSK to #ReverseAging w emerging timelines pot. to say the 75%, 50% & 25% ages of say 100 mice that are one year old, 100 dogs that are 10 years old, & then 100 humans that are around 100 years of age? https://share.gemini.google/tWnlx9sCEYjv ! https://wiki. worlduniversityandschool.org/ wiki/Aging_Reversal_Genetics ~
https://x.com/WorldUnivAndSch/status/2109312098744549497 ... how tointeroperate with the 11 million UAE Avatar Agent Electronic Medical Records .... at the GCellView level, and to help them live with aging reversal and extreme longevity genetic drug therapies as they emerge?(And see the Stanford Law CodeX presentation email re Project Fungzhao, " the prototype of a tool for visualizing data from the USPTO Patent File Wrapper API," from Th 10/22/26, and in the PS).ScottScott GK MacLeodThursday, October 22, 2026
Korean White Forsythia (Abeliophyllum distichum): 10/2 - Harvard ARDD Aging Research and Drug Development conference 10/1/26-10/3/26 in the TreeHouse in Allston, MA * Dear Dr Chang Eui Song DDS (in Korea, now) & re your hormone in brain for longevity hypothesizing at Harvard ARDD (& not knowing English, so this email will be translated), George (Church), Akram (Baharlouei), Alex (Zhavoronkov), Petrina (Kamya), Kejun (Ying), Le Song, Peter (Norvig), All, * * * Stanford [codex_group_meetings] TODAY! CodeX Mtg (10/22@1.30p PT): Project Fungzhao (via Zoom)
PSDear #YinHuang, #RolandVogl, all,
Thanks for your great #StanfordLawCodeX 2/22 #ProjectFungzhao presentation. How best to interoperate this in an emerging #RealisticVirtualEarthForPatent ing a #RVEforPharma a #RVEforEverything & see re #HarvardARDD2026: https://scott-macleod. blogspot.com/2026/10/korean- white-forsythia-abeliophyllum. html ~
https://x.com/WorldUnivAndSch/status/2113380093326512505
https://x.com/WUaSPress/status/2113380273803133177
https://x.com/sgkmacleod/status/2113380450270052398
https://x.com/HarbinBook/status/2113380658437575159
https://x.com/Q_YogaMacFlower/status/2113380593044168730
https://x.com/scottmacleod/status/2113381415220043818
https://x.com/TheOpenBand/status/2113381607465975821
https://lnkd.in/p/gfRpHHcb
* *
10/2 - Harvard ARDD Aging Research and Drug Development conference 10/1/26-10/3/26 in the TreeHouse in Allston, MA
How best to develop a #RealisticVirtualEarthFor, a #RVEforHypothesisTesting - eg think a forked #GoogleStreetView with #TimeSlider & with #Gpegman becoming our #AvatarAgentElectronicMedicalRecords or #DigitalHealthTwins ? https://t.co/riX0twp9wz & see: https://t.co/vNHkatL2Zc ~
— WorldUnivandSch (@WorldUnivAndSch) October 3, 2026
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Thursday, October 22, 2026
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To build a Realistic Virtual Earth (RVE) for Hypothesis Testing—combining spatial geospatial rendering, time-slider environmental contexts, and personal digital health twins—you can leverage Google Cloud’s geospatial stack, Google DeepMind’s biological AI tools, and medical language models.
Part 1: System Architecture for the RVE Platform
+-----------------------------1. Spatial Geospatial & Temporal Rendering Engine
Base Viewer: Fork an open-source 3D geospatial engine such as CesiumJS or Unreal Engine 5 (via the Cesium plugin) integrated with Google Photorealistic 3D Tiles API.
Temporal Layer (Time Slider): Connect the temporal slider to Google Earth Engine API and historical street-level imagery archives. This supplies time-series environmental data (e.g., air quality indices, temperature variations, UV index, urban greenspace density) mapped to specific geographic coordinates.
2. Avatar Pegman as an EMR-Linked Digital Health Twin
Data Standard: Map the avatar agent to standard HL7 FHIR resources or an OMOP Common Data Model (CDM) schema.
Spatial Agent Logic: Pegman acts as an autonomous agent representing a cohort or individual. As Pegman moves through space and time on the RVE, environmental exposures dynamically update the agent's physiological baseline parameters.
3. Google AI / ML Middleware Integration
Vertex AI Pipelines: Orchestrate workflows between spatial queries, biological modeling, and inference tasks.
Gemini / MedLM: Drive automated literature retrieval, hypothesis synthesis, and natural-language query interfaces in the sidebar.
AlphaFold 3 & AlphaProteo: Simulate 3D protein structures, ligand-receptor binding interactions, and custom biological agent modifications.
Part 2: Testing the Longevity Hormone Hypothesis
Here is a step-by-step workflow using Klotho (a well-established lifespan-extending hormone) targeting FGF21 / FGFR1c-Klotho receptor complexes in the brain’s choroid plexus and hippocampus.
[Phase 1: Lit Review] ---> [Phase 2: UI Context Integration] ---> [Phase 3: Multi-Scale Brain Mapping]
|
[Phase 6: In-Vivo Protocol] <--- [Phase 5: In-Silico Experiment] <----------------+
Step 1: AI Scientific Literature Review (Gemini / MedLM Agent)
The agent executes an automated pipeline querying PubMed, PMC, and bioRxiv to extract molecular pathways, target receptors, and dosage-response metrics.
Key Extracted Synthesis:
Hormone Target: Recombinant $\alpha$-Klotho ($\text{sKL}$).
Primary Brain Target: Hippocampal dentate gyrus (neural stem cell niche) and choroid plexus.
Receptor Mechanism: Forms a binary complex with FGFR1c, triggering the extracellular signal-regulated kinase ($\text{ERK1/2}$) and $\text{Akt}$ phosphorylation cascades, upregulating $\text{NMDAR}$ subunit GluN2B, and decreasing neuroinflammation ($\text{TNF-}\alpha$, $\text{IL-6}$).
Step 2: Sidebar UI Integration (Spatial & Biological Mapping)
When selecting Pegman or clicking a target brain region in the RVE interface:
Left Main Window: RVE environment (e.g., Street View at a specific geographic location with environmental stress sliders like particulate matter $\text{PM}_{2.5}$).
Right Sidebar:
Molecular View: Embedded 3Dmol.js or NGL Viewer showing the interactive 3D co-crystal structure of Klotho bound to FGFR1c (PDB ID derived via AlphaFold 3).
Spatial Health Panel: Real-time physiological telemetry of the Pegman avatar (e.g., baseline plasma Klotho concentration $c_0 = 500 \text{ pg/mL}$, biological age vs. chronological age).
Step 3: Multi-Scale Brain Mapping (Molecular to Cellular Levels)
| Scale | Biological Domain | Mathematical / Machine Learning Representation |
| Molecular | Hormone-Receptor Binding Kinetics | Rate equations: $\frac{d[RL]}{dt} = k_{\text{on}} [R][L] - k_{\text{off}} [RL]$ with $K_d = \frac{k_{\text{off}}}{k_{\ |
| Cellular | Neural Stem Cell Proliferation & Microglial Activation | Systems biology differential equations (ODE/SDE) predicting $\text{p16}^{\text{INK4a}}$ senescence reduction |
| Tissue / Organ | Hippocampal Long-Term Potentiation (LTP) & Synaptic Density | Neural network surrogate modeling synaptic transmission efficiency ($V_m$) |
| Organism / Population | Survival Function & Cox Proportional Hazard | Survival model: $h(t \mid X) = h_0(t) \exp(\beta_1 X_{\text{Klotho}} + \beta_2 X_{\text{environment}})$ |
Step 4: Designing and Executing the Virtual (In-Silico) Experiment
A. Virtual Cohort & Experimental Groups
Define two avatar agent populations within the RVE across environmental conditions (e.g., low vs. high urban pollution exposure):
Control Group ($N=1,000$): Vehicle administration ($c_{\text{Klotho}} = \text{baseline}$).
Treatment Group ($N=1,000$): Systemic or central administration of elevated $\alpha$-Klotho ($c_{\text{Klotho}} = 2.5 \times \text{baseline}$).
B. Simulation Execution
Molecular Dynamics (AlphaFold 3 / GROMACS): Calculate binding free energy ($\Delta G_{\text{bind}}$) of Klotho variants to brain FGFR1c receptors.
Cellular Pathway Simulation: Integrate drug delivery kinetics across the Blood-Brain Barrier (BBB) into a differential kinetic solver:
$$\frac{d[\text{p-ERK}]}{dt} = \frac{V_{\max} \cdot [RL]}{K_m + [RL]} - k_{\text{dephos}} [\text{p-ERK}]$$Lifespan Acceleration Simulation: Project survival curves using a Monte Carlo Markov Chain (MCMC) engine over a virtual 30-month rodent equivalent timeframe.
100% |-------------\
| \=== Treatment (Klotho Boosted)
Survival | \--- Control
Rate | \
0% +-----------------------------Step 5: Translating Virtual Results into an In-Vivo Protocol
Based on the virtual experiment yielding a statistically significant hazard ratio reduction ($\text{HR} = 0.74, p < 0.001$), the RVE exports a laboratory-ready in vivo translation guide:
==============================Kejun Ying — Postdoctoral Scholar, Stanford University School of Medicine
14:40–15:00 · "An open world model for human health and mortality"
Key takeaway: World models borrowed from video generation and protein language models can forecast individual disease trajectories and link protein structure to species lifespan.
• Treats health as a "movie," not a sentence: 11 data domains (EHR, labs, MRI, proteomics, genetics and more) merge into a joint health state evolving in continuous time.
• State-of-the-art prediction for more than 1,000 diseases up to 20 years out; predicts unseen diseases from a text description; simulated drug effects (e.g., statins) were checked against 286 real trials.
• Structural model of 2M proteins from 287 species found 170 proteins whose structural features track maximum lifespan, independent of expression; it predicts lifespan about as well as methylation clocks (Cape golden mole predicted at ~30 years).
• Starting his own lab in January 2027.
Virtual Cell in Time Forum — Session II
15:15–16:45 · Cedar Grove · Parallel track · Chair: Max Unfried, The Thalion Initiative
Le Song — Professor, Mohamed bin Zayed University of Artificial Intelligence
15:15–15:35 · "A World Model of the Virtual Cell"
Key takeaway: A true virtual cell should be multiscale, multimodal and stateful, and should drive generative design toward desired cell states.
• Current models handle one perturbation and one response in one cell type.
• Needed: multiscale simulation (DNA to cell), a persistent state for sequential perturbations, and "inverse design" of molecules for a target cell state rather than a binding target.
• Hybrid architecture: graph neural networks on pathway knowledge (Reactome, KEGG) plus transformers to learn what is missing.
• Multi-stage training on single modalities, then paired data; temporal live imaging and perturbation data are crucial.
• The field is at the "GPT-1 or AlphaFold 1" stage; GenBio AI released a model (transcribed "iCell") pitched as the first world model of a human cell.
Petrina Kamya — Global Head of AI Platforms, Insilico Medicine
15:35–15:55 · "Introducing Insilico Medicine's Virtual Aging Cell"
Key takeaway: The Virtual Aging Cell is a multi-agent system that simulates interventions across five biological scales, now undergoing prospective lab validation.
• Insilico has 10 clinical-stage programs (one in Phase 3), 18 IND-enabling studies and more than 47 internal programs.
• Frontier LLMs do well at single-step retrosynthesis but poorly at ab initio tasks; Insilico's Pharmaceutical Superintelligence framework trains and benchmarks models on 12 years of data.
• Lineage: 2016 embryonic-development AI (COX7A1) and the Precious1GPT–3GPT models.
• ~43 specialized agents per layer across molecular, intracellular, intercellular, tissue/organ and organism scales; a patient-seeded baseline graph is compared with the post-intervention graph.
• Q&A: prospective validation on molecules with unknown mechanism, in Insilico's robotic lab, is the only way to rule out literature leakage.
Multiplex testing of de-aging polypharmacy & targeted delivery
12:10–12:30 · Canopy Hall
George Church — Professor, Harvard Medical School
Key takeaway: Multi-gene therapies, ML-designed delivery vectors and engineered organs aim to address aging across several pathways at once.
• Single-pathway interventions may yield only ~2–20% effects; combinations (e.g., alpha-Klotho, TGF-beta receptor 2, FGF21) showed synergy in mouse diabetes and kidney models.
• OSK delivered in a single AAV to mice late in life (after half the cohort had died) increased longevity.
• Machine learning plus multiplex libraries: Dyno Therapeutics AAV capsids reported with 280× higher brain delivery and 50× lower liver targeting.
• Herpesvirus-based vectors under development to carry dozens of genes.
• iPSC-derived oligodendrocyte precursor cells rescued mice from an MS-like demyelinating disease; heading to clinical trials.
• Xenotransplantation: pig organs with 69 edits (retrovirus knockouts, glycan removal, seven human transgenes) in an FDA-cleared trial; later patients have fared better than the first.
• Allogeneic CAR-T programs from companies co-founded by Luhan Yang (e.g., QT-019B, QN-139b) are in FDA clinical trials for autoimmune disease.
Leveraging Glycans in Precision Longevity: From Individualized Monitoring to Targeted Intervention
18:20–18:40 · Canopy Hall
Gordan Lauc — Professor, University of Zagreb; Co-founder, GlycanAge
Key takeaway: IgG glycans are modifiable biomarkers that track biological age, predict disease years ahead and act as effectors of inflammation.
• His lab has profiled glycans in >300,000 people across major cohorts.
• Glycan profiles become more pro-inflammatory with age: linearly in men, while women are protected until menopause and then age rapidly.
• Each additional year of GlycanAge corresponds to ~1% higher all-cause mortality hazard; largely additive to standard blood biomarkers and uncorrelated with epigenetic clocks.
• Pro-inflammatory glycan changes appear up to a decade before diagnosis of diabetes, cardiovascular and inflammatory disease.
• Weight loss and calorie restriction improve GlycanAge; hormone therapy reverses the perimenopausal rise in most women; metformin, SGLT2 inhibitors and GLP-1s help some but not all patients.
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Retweeting -
How with #YamanakaFactorsOSK to #ReverseAging w emerging timelines pot. to say the 75%, 50% & 25% ages of say 100 mice that are one year old, 100 dogs that are 10 years old, & then 100 humans that are around 100 years of age? https://share.gemini.google/
https://x.com/WorldUnivAndSch/
Dear Chang Eui Song,
- Scott GK MacLeod
Founder, President, CEO & Professor
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building on CC-4 licensed MIT OCW in 7 languages -
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https://wiki.
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* * *
Stanford [codex_group_meetings] TODAY! CodeX Mtg (10/22@1.30p PT): Project Fungzhao (via Zoom)
Thank you, Yin Huang, so much! Are you focusing on even patents for aging reversal genetic drug therapy emerging, possibly FDA approved first by 2050? - Scott
Life Biosciences :)? - with its ER-100 gene drug therapy for 2 glaucoma conditions, which seems to be reversing aging potentially, & re epigenetic reprogramming, and which is going through Phase 1 clinical trials presently and which seems promising ... is a potentially fascinating company to explore in some of Project Fungzhao's regards.
Thanks for your great #StanfordLawCodeX 2/22 #ProjectFungzhao presentation. How best to interoperate this in an emerging #
AAAB
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- Scott GK MacLeod
Founder, President, CEO & Professor
at / of best STEAM CC licensed OCW, Wiki,
World University & School (WUaS)
- USPS US Post Office, PO Box 132, General Delivery, Canyon, CA 94516
- https://www.youtube.com/@
building on CC-4 licensed MIT OCW in 7 languages -
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- Scott GK MacLeod
Founder, President, CEO & Professor
at / of best STEAM CC licensed OCW, Wiki,
World University & School (WUaS)
- USPS US Post Office, PO Box 132, General Delivery, Canyon, CA 94516
- https://www.youtube.com/@
building on CC-4 licensed MIT OCW in 7 languages -
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.jpg?utm_source=en.wikipedia.org&utm_campaign=index&utm_content=thumbnail_unscaled)
https://plants.thefarmatgreenvillage.com/12150032/Plant/1699/White_Forsythia/
https://en.wikipedia.org/wiki/Abeliophyllum
https://commons.wikimedia.org/wiki/Category:Abeliophyllum
https://commons.wikimedia.org/wiki/Abeliophyllum_distichum
https://www.wikidata.org/wiki/Q310370
....
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![Korean White Forsythia (Abeliophyllum distichum): 10/2 - Harvard ARDD Aging Research and Drug Development conference 10/1/26-10/3/26 in the TreeHouse in Allston, MA * Dear Dr Chang Eui Song DDS (in Korea, now) & re your hormone in brain for longevity hypothesizing at Harvard ARDD (& not knowing English, so this email will be translated), George (Church), Akram (Baharlouei), Alex (Zhavoronkov), Petrina (Kamya), Kejun (Ying), Le Song, Peter (Norvig), All, * * * Stanford [codex_group_meetings] TODAY! CodeX Mtg (10/22@1.30p PT): Project Fungzhao (via Zoom)](https://plants.thefarmatgreenvillage.com/Content/Images/Photos/A127-21.jpg){kind=link}
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