Saturday, May 10, 2025

Astragalus microcephalus: #DramaticallyMindEXPANDING from https://aistudio.google.com/ - https://scott-macleod.blogspot.com/2025/05/astragalus-how-best-to-create-beginning.html re #AgingReversalMachine re in #GStreetViewRoom as #WUaSMultimediaHospitalRoom https://youtu.be/q9FBhEAXRY4 re @geochurch - https://youtu.be/bnCEIPQFNnk & re https://x.com/amplesa/status/1737852952407326906 & https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/ ~ * How best to create the beginning of #RealisticVirtualEarth for Aging Reversal and Extreme Longevity genetic drug therapies emerging & add it to or code it with Google Street View with time slider, in 2007, 2012, and 2024 per the Harbin Gatehouse here - https://goo.gl/maps/7gSsSTweRCBo9gf87 & to our avatar agent electronic health records via little Pegman in Google Street View with time slider ... and to our body minds via a molecular semiconductor chip such as "eRapid: Multiplexed Electrochemical Sensors for Fast, Accurate, Portable Diagnostics A fouling-resistant, affinity-based platform enables cheap, simultaneous sensing of multiple biomarkers" - https://wyss.harvard.edu/technology/erapid-multiplexed-electrochemical-sensors-for-fast-accurate-portable-diagnostics - and as an #AgingReversalMachine & as #WUaSMultimediaHospitalRoom regarding viewing #GStreetViewRoom -https://youtu.be/q9FBhEAXRY4 & for all ~200 countries' hospitals & the online #WUaSAcademicMedicalCenter in online #WUaSHospitals too & with Harvard Professor of Genetics' George Church @geochurch in the Harvard Medical School "How soon could humans reverse the aging process with genetic engineering?" - https://youtu.be/bnCEIPQFNnk ~ and see too. https://twitter.com/WorldUnivAndSch/status/1774820030326149187 ~



#DramaticallyMindEXPANDING from https://aistudio.google.com/ - https://scott-macleod.blogspot.com/2025/05/astragalus-how-best-to-create-beginning.html re #AgingReversalMachine re in #GStreetViewRoom as #WUaSMultimediaHospitalRoom https://youtu.be/q9FBhEAXRY4 re @geochurch - https://youtu.be/bnCEIPQFNnk & re https://x.com/amplesa/status/1737852952407326906 & https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/ ~


https://x.com/WorldUnivAndSch/status/1921340889424703575

https://x.com/TheOpenBand/status/1921342536951480678

https://x.com/HarbinBook/status/1921342415283065274

https://x.com/WUaSPress/status/1921341313871470803

https://x.com/sgkmacleod/status/1921341076947825144

https://x.com/Q_YogaMacFlower/status/1921340942423839190

https://x.com/scottmacleod/status/1921340793739940270



* 

How best to create the beginning of #RealisticVirtualEarth for Aging Reversal and Extreme Longevity genetic drug therapies emerging & add it to or code it with Google Street View with time slider, in 2007, 2012, and 2024 per the Harbin Gatehouse here - https://goo.gl/maps/7gSsSTweRCBo9gf87 & to our avatar agent electronic health records via little Pegman in Google Street View with time slider ... and to our body minds via a molecular semiconductor chip such as 

"eRapid: Multiplexed Electrochemical Sensors for Fast, Accurate, Portable Diagnostics
A fouling-resistant, affinity-based platform enables cheap, simultaneous sensing of multiple biomarkers" - https://wyss.harvard.edu/technology/erapid-multiplexed-electrochemical-sensors-for-fast-accurate-portable-diagnostics

 - and as an #AgingReversalMachine & as #WUaSMultimediaHospitalRoom regarding viewing #GStreetViewRoom -https://youtu.be/q9FBhEAXRY4 &  for all ~200 countries' hospitals & the online #WUaSAcademicMedicalCenter in online #WUaSHospitals too & with Harvard Professor of Genetics' George Church @geochurch in the Harvard Medical School "How soon could humans reverse the aging process with genetic engineering?" - https://youtu.be/bnCEIPQFNnk ~ and see too. https://twitter.com/WorldUnivAndSch/status/1774820030326149187 ? 


AI Studio replied - 

 

This is an incredibly ambitious and visionary concept, weaving together cutting-edge technologies and forward-thinking ideas for healthcare and longevity! Let's break down how to approach the beginning of this #RealisticVirtualEarth, focusing on the specified components.

Core Concept:

You're envisioning a dynamic, personalized virtual representation of Earth, deeply integrated with individual health data and therapeutic interventions, specifically for aging reversal. This platform would serve as a visualization tool, a diagnostic aid, a research platform, and potentially a therapeutic environment.

Phase 1: Foundational Layer - The Time-Traveling Harbin Gatehouse

This is a great, tangible starting point.

  1. Google Street View with Time Slider Integration:

    • Understanding the Data: Google Street View does store historical imagery. When you use the time slider on Google Maps for a location like the Harbin Gatehouse, you're accessing different image sets captured at different times.

    • API Access: The Google Maps JavaScript API is your primary tool here.

      • StreetViewService: Allows you to search for Street View panoramas at a given location.

      • StreetViewPanorama object: Represents the Street View display. You can set its pano (panorama ID) or position (lat/lng).

    • Accessing Historical Imagery Programmatically:

      • This is the trickiest part. While the Google Maps interface has a time slider, directly querying "Street View for 2007, 2012, 2024" via API for a specific date isn't straightforward.

      • Approach 1 (Manual Pano ID Discovery):

        1. Go to the Harbin Gatehouse link on Google Maps: https://goo.gl/maps/7gSsSTweRCBo9gf87

        2. Use the time slider in the top-left corner to select imagery closest to 2007, 2012, and 2024.

        3. For each selected year, you need to extract the Panorama ID (Pano ID). This can sometimes be found in the URL when sharing, or by inspecting network requests, or using browser developer tools to inspect the Street View elements. (This is more of a hack for initial setup).

        • Example: If you find a Pano ID XYZ123 for 2007, you can then load it directly.

      • Approach 2 (Programmatic Search & Filtering - More Robust):

        1. The Street View API doesn't explicitly let you say "give me imagery from July 2007". However, panorama metadata (which isn't always easily accessible via the public API) contains capture dates.

        2. You might need to use the StreetViewService.getPanorama() method. The StreetViewPanoramaData returned might contain image date information or copyright information that includes a year.

        3. You could potentially iterate through nearby panoramas (using getLinks()) to find ones with desired capture dates, but this is complex and not guaranteed.

        4. Most Pragmatic Initial Step: For a prototype, manually identify the Pano IDs for the desired years (2007, 2012, and a recent one for "2024") for the Harbin Gatehouse.

    • Coding Example Snippet (Conceptual JavaScript):

      let map;
let panorama;
const harbinGatehouseLocation = { lat: 45.7500, lng: 126.6333 }; // Approx. coordinates

// Pano IDs - you'd need to find these manually for the specific dates
const panoIDsByYear = {
    '2007': 'PANO_ID_FOR_2007_IMG', // Replace with actual Pano ID
    '2012': 'PANO_ID_FOR_2012_IMG', // Replace with actual Pano ID
    '2024': 'PANO_ID_FOR_LATEST_IMG' // Replace with actual Pano ID (or use position)
};

function initMap() {
    panorama = new google.maps.StreetViewPanorama(
        document.getElementById('pano'), {
            position: harbinGatehouseLocation, // Initial position
            pov: { heading: 34, pitch: 10 },
            visible: true
        }
    );
    // Initially load the latest (or a default)
    setStreetViewYear('2024');
}

function setStreetViewYear(year) {
    const panoId = panoIDsByYear[year];
    if (panoId && panorama) {
        // Option 1: If you have specific Pano IDs
        panorama.setPano(panoId);
        panorama.setVisible(true);
    } else if (panorama) {
        // Option 2: Fallback to position if Pano ID for that year isn't found
        // This will load the default (usually latest) Street View for the location
        panorama.setPosition(harbinGatehouseLocation);
        panorama.setVisible(true);
        console.warn(`Pano ID for year ${year} not found. Using location default.`);
    }
    // Update UI to reflect the current year
    document.getElementById('currentYearDisplay').textContent = `Displaying: ${year}`;
}

// HTML would have buttons or a slider:
// <div id="pano" style="width: 100%; height: 500px;"></div>
// <button onclick="setStreetViewYear('2007')">2007</button>
// <button onclick="setStreetViewYear('2012')">2012</button>
// <button onclick="setStreetViewYear('2024')">2024</button>
// <div id="currentYearDisplay"></div>

      You will need a Google Maps API Key enabled for the JavaScript API and Street View API.

Phase 2: Integrating Avatar Agent EHR via Pegman

This involves linking the visual Pegman to a conceptual Electronic Health Record (EHR).

  1. Conceptual EHR Data:

    • For now, this will be mock data. Define a simple JSON structure for an avatar's EHR.

    • Example:

      {
    "avatarId": "pegman_harbin_user001",
    "name": "User 1",
    "birthDate": "1970-01-01",
    "biomarkers": {
        "telomereLength": { "value": 6.5, "unit": "kbp", "timestamp": "2024-03-15T10:00:00Z" },
        "inflammationMarkers": { "CRP": 1.2, "unit": "mg/L", "timestamp": "2024-03-15T10:00:00Z" }
    },
    "geneticTherapies": [
        { "therapyName": "Telomerase Gene Activation", "startDate": "2023-01-01", "status": "Ongoing" }
    ],
    "locationHistory": [ // Tying to Street View time slider
        { "year": 2007, "healthSnapshot": "Baseline pre-awareness" },
        { "year": 2012, "healthSnapshot": "Early intervention research phase" },
        { "year": 2024, "healthSnapshot": "Post-initial therapy, monitoring" }
    ]
}

  2. Linking Pegman to EHR:

    • Pegman in Street View is primarily a UI element controlled by Google. You can't directly modify Google's Pegman to store EHR data.

    • Your RVE's Pegman: You'll create your own representation or overlay related to Pegman's position.

    • When the user "interacts" with the Street View (e.g., Pegman is dropped, or the view changes), your application would:

      1. Get the current lat/lng and panoID.

      2. Query your mock EHR datastore for the user associated with this session.

      3. Display relevant EHR snippets (e.g., health status for the year currently shown in Street View) in a separate UI panel alongside the Street View.

  3. Visualizing EHR Data:

    • When setStreetViewYear(year) is called, also update a separate div to show the healthSnapshot for that year from the avatar's EHR.

Phase 3: Integrating Molecular Semiconductor Chip Data (e.g., eRapid)

This involves simulating or (eventually) integrating real-time biomarker data.

  1. Data Flow Concept:

    • Real Chip: eRapid chip -> Reader Device -> Bluetooth/Wi-Fi -> Smartphone App/Gateway -> Cloud Backend API -> Your RVE Frontend.

    • Simulated Chip (for now): Your RVE frontend can have a "Simulate Biomarker Update" button.

  2. Updating EHR:

    • When new biomarker data comes in (simulated or real), it updates the avatar's EHR (your mock JSON object or a more robust backend datastore).

    • Example:

      function simulateBiomarkerUpdate() {
    // In a real app, this data would come from an API call to your backend
    const newTelomereLength = Math.random() * 2 + 6; // Random value between 6.0 and 8.0
    avatarEHR.biomarkers.telomereLength = {
        "value": parseFloat(newTelomereLength.toFixed(2)),
        "unit": "kbp",
        "timestamp": new Date().toISOString()
    };
    console.log("Biomarker updated:", avatarEHR.biomarkers.telomereLength);
    // Refresh any UI elements displaying biomarkers
    updateBiomarkerDisplay();
}

  3. Visualization:

    • Display current biomarker readings in a dedicated UI panel.

    • Potentially show trends over time if you store historical biomarker data.

Phase 4: Conceptualizing #AgingReversalMachine & #WUaSMultimediaHospitalRoom

These are higher-level UI/UX concepts built upon the foundational layers.

  1. #AgingReversalMachine:

    • This could be a dashboard within your RVE.

    • It visualizes:

      • The avatar's current aging biomarkers (from EHR/chip).

      • Progress on genetic therapies.

      • Personalized recommendations (e.g., "Environmental scan of current Street View location suggests high pollution. Consider air filtration.").

      • Simulations: "If therapy X continues, projected biomarker Y improvement in 6 months."

    • The Street View component (Harbin Gatehouse) could be one "window" in this machine, showing an environmental context.

  2. #WUaSMultimediaHospitalRoom / #GStreetViewRoom:

    • Imagine a patient in a hospital room (physical or virtual).

    • One "wall" or screen shows the #RealisticVirtualEarth.

    • They can navigate to locations relevant to their health or history (e.g., where they grew up, pollution hotspots, serene environments for mental well-being).

    • Their EHR data (from Pegman avatar) is overlaid or presented alongside.

    • Doctors can use this for remote consultations, explaining environmental factors, or showing progress.

    • The Harbin Gatehouse view could be a specific "scene" loaded into this room, with the time slider allowing discussion of changes over time and potential health impacts.

Phase 5: Expanding to Global Scale and Integrating Research

  1. #WUaSAcademicMedicalCenter & Hospitals:

    • The RVE becomes a shared platform for ~200 countries' hospitals.

    • Requires robust, secure, and scalable cloud infrastructure.

    • Standardized data formats for EHRs (like FHIR) are crucial.

    • Localization (language, regional health concerns).

  2. George Church & Genetic Engineering Research:

    • Integrate links to research papers, datasets, and videos (like Prof. Church's) directly into the RVE.

    • Users could click on a "research node" within the RVE to access relevant information.

    • The RVE could visualize the potential impact of emerging genetic therapies discussed by researchers like Prof. Church on the avatar's biomarkers.

Implementation Strategy (High-Level):

  1. Frontend Prototype (Harbin Focus):

    • HTML, CSS, JavaScript.

    • Google Maps JavaScript API.

    • Manually find Pano IDs for Harbin Gatehouse for 2007, 2012, latest.

    • Build simple UI for time selection.

    • Create a mock JSON EHR for one avatar.

    • Display basic EHR info linked to the selected year.

  2. Biomarker Simulation:

    • Add a button to simulate biomarker updates and reflect them in the mock EHR and UI.

  3. Backend (Later Stage):

    • For persistent EHRs, user accounts, and real chip data:

      • Choose a backend language/framework (Node.js, Python/Django, Java/Spring).

      • Database (PostgreSQL, MongoDB).

      • REST APIs for frontend to communicate with.

  4. 3D Environment/RVE (Advanced):

    • Beyond Street View, if you want a fully navigable 3D Earth, platforms like CesiumJS, Unity, or Unreal Engine would be needed, integrating map tiles and 3D models. This is a significant step up in complexity.

  5. Security and Privacy: Absolutely paramount for health data. HIPAA, GDPR, etc., compliance. End-to-end encryption, robust authentication and authorization.

Key Technologies to Consider:

  • Web Technologies: HTML, CSS, JavaScript, (React/Angular/Vue for complex UIs).

  • Mapping/GIS: Google Maps API, CesiumJS, Mapbox.

  • 3D Engines (Optional for full RVE): Unity, Unreal Engine.

  • Backend: Node.js, Python, Java, Go.

  • Databases: PostgreSQL, MongoDB, specialized graph databases.

  • Cloud Platforms: AWS, Google Cloud, Azure (for scalability, managed services, AI/ML).

  • Health Data Standards: FHIR (Fast Healthcare Interoperability Resources).

  • IoT (for eRapid): MQTT, CoAP, Bluetooth LE.

This is a grand vision! Starting with the Harbin Gatehouse time slider and a mock EHR is a manageable and excellent first step to demonstrate the core concept. Good luck!




* * 


Further regarding the question I just asked about an Aging Reversal Machine -https://scott-macleod.blogspot.com/2025/05/astragalus-how-best-to-create-beginning.html - and your reply Google AI Studio:  How best to create the beginning of #RealisticVirtualEarth for Aging Reversal and Extreme Longevity genetic drug therapies emerging and combine this with Alex Plesa's research regarding -

Can we reverse aging? 
7 yrs ago, Ocampo et al. showed that partial reprogramming can ameliorate aging hallmarks. But, OSKM induction has safety limitations & no new factors have emerged since. Here, biorxiv.org/content/10.110… we used 🧬 screens to find new rejuvenation factors🧵1/8


AND with AlphaFold 3 Server for predicting with machine learning all molecular compounds ever - https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/ - and here for aging reversal and extreme longevity and interoperable with Google Street View with Time Slider with Pegman as individuals' avatar agent electronic health records? 


Okay, this is a fantastic expansion, layering in cutting-edge biological research and AI-driven molecular modeling onto the #RealisticVirtualEarth concept. Let's break down how Alex Plesa's research and AlphaFold 3 can be woven into your #AgingReversalMachine.

Core Idea Refined:

Your #RealisticVirtualEarth is evolving into a multi-layered platform:

  1. Environmental/Temporal Layer: Google Street View with Time Slider (Harbin Gatehouse as a pilot).

  2. Personal Health Layer: Pegman avatar as the interface to individual EHRs (including biomarker data from sources like eRapid).

  3. Intervention/Research Layer: Incorporating knowledge of specific aging reversal compounds (like Astragalus) and novel rejuvenation factors (from Plesa's research).

  4. Predictive/Molecular Layer: Leveraging AlphaFold 3 to understand and design molecular interactions for therapies.

Integrating Alex Plesa's Research (New Rejuvenation Factors):

Alex Plesa's work focuses on discovering new genetic factors (beyond the Yamanaka factors/OSKM) that can induce rejuvenation with potentially better safety and efficacy.

  1. Adding to the EHR (Pegman Avatar):

    • "Potential Rejuvenation Factors" Section: The avatar's EHR could have a dynamic section listing novel rejuvenation factors identified by research like Plesa's.

    • Relevance Score: For each factor, a (simulated for now) "relevance score" could be calculated based on the avatar's specific aging hallmarks (e.g., if the avatar shows significant epigenetic clock acceleration, factors known to impact this would score higher).

    • Status: "Experimental," "Pre-clinical," "Clinical Trial Phase X."

    • Links to Research: Direct links to papers/summaries (like Plesa's tweet/bioRxiv paper).

  2. Visualizing in the #AgingReversalMachine Interface:

    • Factor Explorer: A UI element where users (or their clinicians) can explore these new factors.

    • Mechanism of Action (Simplified): For each factor, a simplified visual or textual explanation of its proposed mechanism (e.g., "Targets Pathway X to reduce cellular senescence").

    • Impact Simulation: When a factor is "selected" (hypothetically), the #AgingReversalMachine could simulate its potential impact on the avatar's biomarkers (e.g., "Applying Factor Y could potentially improve telomere length by Z% over 6 months"). This would initially be based on published research effects, not direct AlphaFold 3 computation.

  3. Connecting to Genetic Drug Therapies:

    • Plesa's factors are genes. Therapies would involve delivering these genes (e.g., via viral vectors) or delivering proteins/mRNA, or small molecules that mimic/activate these pathways.

    • The RVE can track the development pipeline of therapies based on these new factors.

Integrating AlphaFold 3 Server:

AlphaFold 3's ability to predict structures of proteins, DNA, RNA, ligands, and their interactions is a game-changer for designing and understanding therapies.

  1. Understanding Plesa's Factors & Existing Drugs (e.g., Astragalus):

    • Molecular Visualization: For any protein-based rejuvenation factor (from Plesa's work or others), or target proteins for compounds like those in Astragalus, AlphaFold 3's predictions can be used to visualize their 3D structure within the #AgingReversalMachine.

    • Binding Site Prediction: AlphaFold 3 can predict how compounds (e.g., active ingredients in Astragalus like Cycloastragenol or Astragaloside IV) bind to target proteins (e.g., telomerase). This can help understand efficacy and optimize drug design.

      • EHR Integration: "Compound [Astragalus-X] predicted by AlphaFold 3 to bind to [Target Protein Y] with affinity score [Z]. This interaction is expected to [upregulate/downregulate pathway A]."

  2. Designing Novel Therapies:

    • De Novo Design: While the RVE itself won't run AlphaFold 3 for de novo design, it can be a platform to visualize and evaluate candidate molecules designed using AlphaFold 3 by researchers.

    • Personalized Ligand Design: In a very advanced future, if an individual has a specific genetic variant of a target protein, AlphaFold 3 could help predict how standard drugs bind or even help design personalized ligands for their specific protein variant. This would be reflected in the Pegman EHR.

  3. Safety and Off-Target Effects:

    • AlphaFold 3 can predict interactions with many proteins. This could be used to predict potential off-target effects of a proposed therapy, flagging them in the #AgingReversalMachine.

    • "Therapy Z, while targeting Protein A, is also predicted by AlphaFold 3 to have moderate interaction with Protein B, potentially leading to [side effect]."

  4. Interoperability with RVE & Pegman EHR:

    • "AlphaFold 3 Insights" Panel: Within the #AgingReversalMachine, alongside the Street View display and EHR, there could be a panel showing:

      • 3D models of relevant molecules (avatar's proteins, drug compounds, rejuvenation factors).

      • Predicted interaction animations.

      • Affinity scores and confidence levels from AlphaFold 3.

    • Input to AlphaFold 3 (Conceptual): The avatar's genetic data (which specific protein variants they have) and current therapies could be conceptual inputs for which AlphaFold 3 predictions are relevant.

    • Output from AlphaFold 3 (Consumed by RVE): The RVE would consume pre-computed or API-accessible AlphaFold 3 results to inform its simulations and visualizations. It's unlikely the RVE itself will be running the massive computations of AF3 directly. It would query a service or database of AF3 results.

Example Workflow within the #RealisticVirtualEarth:

  1. User/Avatar Context:

    • Pegman is at the Harbin Gatehouse location in Street View, time slider set to 2024.

    • The avatar's EHR shows current biomarkers (e.g., from eRapid), including some accelerated aging signs (e.g., high epigenetic age).

    • The avatar is considering Astragalus and has heard about Plesa's new factors.

  2. #AgingReversalMachine Analysis:

    • Astragalus Check:

      • The user selects "Astragalus."

      • The system pulls up AlphaFold 3 data: "Active compounds in Astragalus are predicted to bind to telomerase, potentially increasing telomere length. Predicted affinity: High."

      • A 3D visualization of the compound binding to telomerase is shown.

    • Plesa's Factors Exploration:

      • The user explores "New Rejuvenation Factors."

      • The system highlights factors relevant to the avatar's epigenetic age. Let's say "Factor X" is identified.

      • Link to Plesa's research on Factor X is provided.

      • AlphaFold 3 data (if available/simulated): "Factor X is a protein. Its predicted structure suggests it interacts with the epigenetic machinery at site Y."

    • Therapeutic Simulation:

      • The machine simulates the combined hypothetical effect of optimized Astragalus dosage and a future therapy based on Factor X on the avatar's biomarkers over the next 5 years.

      • It flags potential interactions or contraindications based on AlphaFold 3's broader predictive power (e.g., "Factor X therapy may interact with current medication B. Consult physician.").

  3. Environmental Link (Street View):

    • The system might overlay data on the Street View: "Air quality at Harbin Gatehouse location in 2024 is [moderate/poor]. Chronic exposure can accelerate epigenetic aging. Consider interventions." This ties the molecular/personal data back to the real-world environment.

Implementation Steps (Building on Previous):

  1. Data Modeling for New Factors & AlphaFold Insights: Extend your mock EHR JSON schema to include fields for:

    • rejuvenationFactors: [{ name, source, mechanism_summary, relevance_score, af3_protein_id }]

    • molecularInteractions: [{ compound_name, target_protein, af3_interaction_id, affinity_score, visualization_url }]

  2. UI Mockups for Plesa/AlphaFold Panels: Design how this information will be displayed within your #AgingReversalMachine interface.

  3. Conceptual API for AlphaFold 3:

    • You won't be calling the live AlphaFold 3 for every user. Instead, assume there's a curated database of relevant AlphaFold 3 predictions for aging-related proteins and common compounds.

    • Your RVE would query this hypothetical "AlphaFold Aging DB API": GET /af3_interaction?protein=TERT&ligand=Cycloastragenol.

  4. Visualization (Simplified):

    • For 3D molecular models, you can embed existing web-based viewers like Mol* (Molstar) or NGL Viewer and load PDB files (many of which will be AlphaFold 3 predictions). You'd link to the specific AlphaFold Protein Structure Database entries.

    • The new Google DeepMind "AlphaFold Server" might eventually offer APIs or embeddable components that simplify this. Watch its development closely.

Connecting to #WUaSMultimediaHospitalRoom & Global Hospitals:

  • This enhanced #AgingReversalMachine, informed by Plesa's factors and AlphaFold 3, becomes an even more powerful tool for clinicians in the #WUaSMultimediaHospitalRoom.

  • They can use it for:

    • Patient education about cutting-edge therapies.

    • Exploring personalized therapeutic options (based on individual genetics and AlphaFold predictions).

    • Visualizing how proposed treatments might work at a molecular level.

  • The #WUaSAcademicMedicalCenter could become a hub for curating and disseminating validated AlphaFold 3 insights relevant to aging, and tracking clinical trials for new factors like those from Plesa's lab.

This integration makes the #RealisticVirtualEarth not just a record-keeper and environmental correlator, but a forward-looking predictive and personalized medicine discovery/education tool. It's incredibly ambitious and sits at the confluence of several major scientific and technological breakthroughs.




* * * 


#DramaticallyMindEXPANDING fr https://aistudio.google.com/ - https://scott-macleod.blogspot.com/2025/05/astragalus-how-best-to-create-beginning.html re #AgingReversalMachine re in #GStreetViewRoom as #WUaSMultimediaHospitalRoom https://youtu.be/q9FBhEAXRY4 re @geochurch - https://youtu.be/bnCEIPQFNnk & re https://x.com/amplesa/status/1737852952407326906 & https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/ ~

https://x.com/WorldUnivAndSch/status/1921340889424703575

https://x.com/TheOpenBand/status/1921342536951480678

https://x.com/HarbinBook/status/1921342415283065274

https://x.com/WUaSPress/status/1921341313871470803

https://x.com/sgkmacleod/status/1921341076947825144

https://x.com/Q_YogaMacFlower/status/1921340942423839190

https://x.com/scottmacleod/status/1921340793739940270



* 



https://en.wikipedia.org/wiki/Category:Astragalus

https://commons.wikimedia.org/wiki/Category:Astragalus




*


* 

Several plant species in Pakistan are considered endangered. Examples include Acanthophyllum kotschyi, Arnebia euchroma, and Astragalus microcephalus. Additionally, the Geranium wallichianum (a medicinal herb) is also listed as an endangered species in the Himalayan region of Pakistan. 




https://en.wikipedia.org/wiki/Astragalus_microcephalus




https://www.inaturalist.org/taxa/866199-Astragalus-microcephalus




https://www.researchgate.net/figure/A-general-view-of-Astragalus-microcephalus-Willd-which-is-wide-spread-in-the-rangelands_fig1_238698650



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