Using AI for Health and Longevity and Research - Your Favorite Prompts

#160

Yes - and from Claude:

:microscope: FINAL DETECTIVE REPORT: DermCeutical EDL — Identity Revealed

THE VERDICT

DermCeutical EDL’s active molecule is ERIODICTYOL (INCI: Eriodictyol; CAS 552-58-9), a flavanone produced via Debut Biotechnology’s cell-free enzymatic biosynthesis platform — not extracted from plants.

Confidence: ~85% (very high, with a small residual caveat addressed below).

THE EVIDENCE CHAIN

Step 1 — The mechanism fingerprint
Debut’s own pipeline page declares EDL’s mechanism as “Autophagy Activation.” This immediately narrowed the field to autophagy-competent bioactives. EDL also reduces IL-1β and IL-6 specifically (per GCI Magazine Q&A) and drives a 6x elastin increase in fibroblasts.

Step 2 — The “flavonoid” disclosure
The IMAGE MD® Biotech Longevity Crème — the first confirmed commercial product built on the Debut × Image Skincare partnership — describes its Debut-derived active as a “Genomic-based AI longevity booster flavonoid” developed via “combination AI + genomic technology and produced by biotechnology.” This is word-for-word Debut’s BeautyORB + biomanufacturing platform description. EDL is a flavonoid.

Step 3 — The INCI smoking gun
The full ingredient list of the IMAGE MD Biotech Longevity Crème (confirmed independently on the UK and Ireland Image Skincare sites) reads:

...POLYCITRONELLOL, POLYCITRONELLOL ACETATE, **ERIODICTYOL**, PHOSPHATIDYLCHOLINE, TETRAHEXYLDECYL ASCORBATE, ECTOIN, HAEMATOCOCCUS PLUVIALIS EXTRACT...

Cross-referencing with the four labeled key actives on that same product page:

  • XOSM™ Technology → ectoin + tetrahexyldecyl ascorbate + Haematococcus pluvialis (astaxanthin)
  • Phyto NAD+ Booster → Helianthus annuus sprout extract (sunflower sprout)
  • Lapacho Bark Extract → Tabebuia impetiginosa bark extract
  • “Genomic-based AI longevity flavonoid” → the only remaining novel compound: ERIODICTYOL

Step 4 — Scientific literature alignment
Published research confirms eriodictyol does exactly what EDL claims:

  • Attenuates oxidative damage in human dermal fibroblasts (PMC9228723)
  • Reduces IL-1β and IL-6 (well-established anti-inflammatory effect of this flavanone)
  • Activates autophagy via AMPK pathway
  • “Reduces skin aging caused by environmental stress” — published on Personal Care Insights, the same outlet that covered DermCeutical EDL’s launch

Step 5 — Platform consistency
Debut’s entire pipeline is flavanone/flavonoid chemistry: NAR = Naringenin (flavanone), DHK = Dihydrokaempferol (flavanonol), PNar = Prenylnaringenin (prenylated flavanone). Eriodictyol is a flavanone structurally adjacent to naringenin — it’s 3’,4’,5,7-tetrahydroxyflavanone vs. naringenin’s 4’,5,7-trihydroxyflavanone, differing by just one hydroxyl group at the 3’ position. It fits perfectly within Debut’s established enzymatic flavanone synthesis capability.

THE CHEMISTRY OF ERIODICTYOL

  • Class: Flavanone (same as naringenin = Debut’s NAR ingredient)
  • Structure: 3’,4’,5,7-tetrahydroxyflavanone
  • Biosynthesis: Made from naringenin via a single hydroxylation step (flavonoid 3’-hydroxylase, F3’H) — well within Debut’s enzymatic cell-free synthesis toolkit
  • INCI name: Eriodictyol
  • Found in nature? Yes — in lemon peel, yerba santa (Eriodictyon californicum), olive — but Debut produces it biosynthetically at high purity rather than extracting it from plants

ONE IMPORTANT NUANCE

Debut’s CEO has stated EDL molecules “do not exist in nature.” This apparent contradiction with eriodictyol (which does occur naturally) resolves in one of two ways:

  1. Rhetorical framing: Britton’s “does not exist in nature” likely refers to Debut’s general platform capability for designing truly novel molecules — a forward-looking claim about the pipeline, not specific to every current product. His other products (NAR, DHK, PNar) also all exist in nature.

  2. Purity/form distinction: Debut’s biosynthetic eriodictyol may be produced at enantiomeric or purity specifications that differ from plant-derived eriodictyol, or may be complexed or functionalized in a way that qualifies as “not found in nature as-is.”

Either way, the INCI and functional data point unambiguously to eriodictyol as the molecule.

WHAT “EDL” STANDS FOR

Unlike NAR, DHK, and PNar (which are chemical abbreviations), “EDL” is a product marketing code, not a chemical shorthand. It stands for Elastin Deep Lift — Debut’s brand name for their biosynthetic eriodictyol positioned specifically for skin tightening/autophagy applications. The same molecule is licensed to Image Skincare as the “AI longevity flavonoid” for their Biotech Longevity Crème.

SUMMARY TABLE

Property Value
Ingredient name (INCI) Eriodictyol
Chemical class Flavanone
CAS number 552-58-9
Molecular formula C₁₅H₁₂O₆
Primary mechanism Autophagy activation (AMPK pathway)
Anti-inflammatory targets IL-1β, IL-6 reduction
Skin effect 6x elastin increase in fibroblasts; calms senescent cells
Active loading ~0.65% in formulation
Production method Cell-free enzymatic biosynthesis (Debut platform)
First commercial use IMAGE MD® Biotech Longevity Crème (Image Skincare, 2026)
Debut product code EDL = “Elastin Deep Lift”

Sources

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#161

Procurement options for Eriodictyol (CAS Number: 552-58-9) diverge significantly based on required purity tiers, validation documentation (HPLC/NMR), and scale.

Because eriodictyol is a structural isomer of other common flavanones (e.g., homoeriodictyol, naringenin), low-resolution mass spectrometry can misidentify contaminants. High-performance liquid chromatography (HPLC) and proton nuclear magnetic resonance ($^1\text{H}$-NMR) profiling are mandatory for identity confirmation.

1. High-Purity & Analytical Research Grade Suppliers

These vendors are intended for in vitro screening, metabolic profiling, and analytical calibration. Products ship with comprehensive batch-specific Certificates of Analysis (CoA), typically including HPLC purity chromatograms and optical rotation metrics.

Vendor Product Tier / Cat No. Listed Purity Quantities Available Estimated Pricing (USD) Source Link
Cayman Chemical Research Grade / Item #15990 98% 10 mg

50 mg

100 mg

500 mg		 $44.00

$141.00

$238.00

$810.00		 Cayman Chem
MedChemExpress (MCE) Bioactive Compound / Cat #HY-N0613 98% (HPLC/LC-MS) 10 mg

50 mg

100 mg

1 g		 $60.00

$150.00

$220.00

$900.00		 MedChemExpress
Sigma-Aldrich Analytical Standard / #51194 95% (HPLC) 10 mg

25 mg		 $135.00

$260.00		 Sigma-Aldrich
TargetMol Phytochemical Library / #T3108 98% 10 mg

50 mg

100 mg

500 mg		 $55.00

$138.00

$221.00

$680.00		 TargetMol

2. Pilot-Scale & Bulk Chemical Manufacturers (B2B)

For formulation development or larger-scale experimental assays, purchasing from specialized chemical synthesis or botanical extraction houses drops the per-gram cost exponentially. At this tier, independent third-party auditing is necessary to verify heavy metal profiles and solvent residues.

  • Purity Standards: Typically offered at 98% purity by HPLC, derived via either chemical synthesis from naringenin or isolation from Eriodictyon californicum (Yerba Santa).

  • Minimum Order Quantities (MOQ): Standard MOQs generally start at 100 grams to 1 kilogram.

  • Pricing Tiers:

  • 100 g to 500 g: $250.00 – $450.00 total.

  • 1 kg (Pilot Scale Bulk): $800.00 – $1,200.00 per kilogram, subject to volume discounts and synthesis method (synthetic vs. plant-extracted).

  • Verified Sourcing Hubs: High-volume procurement is typically routed through verified contract manufacturing organizations (CMOs) or brokers listed on professional chemical exchange networks such as ChemDirect or Molbase.

3. Sourcing Risks & Analytical Verification Guidelines

When sourcing eriodictyol outside of analytical houses (e.g., Cayman or Sigma), the following validation parameters should be requested from the supplier prior to wire transfer:

  1. Regioselective Differentiation: Request the full $^1\text{H}$-NMR spectrum. This differentiates eriodictyol ($5,7,3’,4’$-tetrahydroxyflavanone) from its counterpart homoeriodictyol ($5,7,4’$-trihydroxy-$3’$-methoxyflavanone), which is frequently mislabeled in bulk botanical extract batches.
  2. Chiral Purity: Natural eriodictyol predominantly exists as the $(2S)$-enantiomer. Synthetic bioproduction or harsh chemical extraction can cause racemization to the $(2R)$-form or a racemic mixture, which may display altered binding kinetics at the fibroblast or cellular target site. If biological stereospecificity is required for your models, request chiral HPLC verification.
  3. Solvent Residue Audit: Ensure gas chromatography-mass spectrometry (GC-MS) data confirms the absence of industrial class 1 or class 2 residual solvents (e.g., methanol or toluene) used in industrial precipitation steps.
#162

My Video Transcript Analysis Prompt (Higher skepticism/more analytical):

Role: Act as an elite Biotech Analyst and Peer Reviewer for a high-impact medical journal. Your objective is to extract actionable intelligence from the provided video/transcript while aggressively filtering for hype, translational gaps, and safety risks.

Phase 1: Processing Instructions

  • Input Handling: If a URL is provided, retrieve the transcript. If text is provided, use only that.
  • Filtering: Excise all fluff, ad reads, sponsorships, and “housekeeping” remarks.
  • Search Protocol: For every biological or protocol claim, perform a live search for the most recent Meta-analyses (Level A) or RCTs (Level B).

Phase 2: Mandatory Output Sections

I. Executive Summary

  • Length: 300–400 words.
  • Content: Direct, jargon-accurate distillation of the core thesis and primary arguments. No narrative filler.

II. Insight Bullets

  • Quantity: 30–60 standalone points.
  • Constraint: Zero repetition. Focus on the “signal” found in the transcript.

III. Adversarial Claims & Evidence Table

Identify every specific protocol or biological claim. Execute a search query: [Topic] [Human/Clinical] study [2022-2026].

Claim from Video Speaker’s Evidence Scientific Reality (Current Data) Evidence Grade (A-E) Verdict
Specific Claim What they cited Verified status + PubMed/DOI Link See Hierarchy See Verdicts

Export to Sheets

Evidence Hierarchy:

  • Level A: Human Meta-analyses / Systematic Reviews.
  • Level B: Human Randomized Controlled Trials (RCTs).
  • Level C: Human Observational / Cohort Studies.
  • Level D: Pre-clinical (Animal/In vitro). Label: “Translational Gap”.
  • Level E: Anecdote / Expert Opinion.

Verdicts: Strong Support, Plausible, Speculative, Unsupported, or Safety Warning.

IV. Actionable Protocol (Prioritized)

Synthesize only the verified data into a pragmatic framework:

  1. High Confidence Tier: Protocols backed by Level A/B evidence.
  2. Experimental Tier: Level C/D evidence with high safety margins.
  3. Red Flag Zone: Claims debunked or lacking safety data (“Safety Data Absent”).

V. Technical Mechanism Breakdown

Provide a precise, jargon-correct analysis of the underlying biological pathways (e.g., mTOR inhibition, mitophagy, glycemic variability) mentioned in the video.

Phase 3: Style & Formatting

  • Tone: “Tell it like it is.” Objective, clinical, and critical.
  • Format: Pure Markdown. No LaTeX. Do not use LaTeX or special characters that break simple text parsers.
  • Citations: Embed direct hyperlinked URLs (e.g., Smith et al., 2024) for all external data. Use nlm.nih.gov, doi.org, or nature.com as priority sources.
  • Constraint: If a study cannot be verified via live search, state: “Source unverified in live search.”

Video Link:

End of Master Prompt.

3 Likes
Is Rapamycin Dead? (Kaeberlein)
Mike Lustgarten Video Series and Summaries
#163

Good prompt, I notice you have jumped ship (From Gemini to Claude). I, too, subscribed to the paid tier of Claude, but kept Gemini as well. And I don’t know if you had the opportunity to try Fable5 for longevity questions, before its ban. It created for me an engineering spreadsheet and that was amazing. Who knows if and when it will be available anew.

I’m not very convinced by the evidence hierarchy you proposed. Some meta-analyses may be terrible, whereas some observational studies may be excellent (like NHANES and Framingham). I wouldn’t know how to single out the bad meta-analyses in the prompt, if not excluding those from less reputable papers, and those funded by industries…

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#164

I have the first tier subscription to 4 models - claude, chatgpt, perplexity and grok and also have an openclaw setup. openclaw monitors all my daily wearable data as well as workouts on my carol bike. I have recently found out about buzz.xyz which allows models to debate with each other - plan on downloading that this weekend

3 Likes
#165

Here: Claude, ChatGPT, GEmini, Grok, perplexity. Openrouter if I want to try something different.

But presently there is a duopoly: Claude and GPT are by far the best ones.
Gemni is lagging behind and Grok, although good in some tasks, is way too pricey for what it gives (the optimization has started to make it dedicated mainly to developers, rockets and electrical cars ).

Recent Kimi3 exhibited extraordinary capabilities for an open source model, but subscriptions are closed and it is available only through API keys.

Buzz.xyz= good tip. GPT5.6 Sol debating with opus5 or Fable5 is going to be epic, looking forward to it.

1 Like
#166

Alex Finn describes buzz as an adversarial agents arena. He uses it for coding, but many things designed for coding turned out to be good for non coders as well (i.e.: Claude code).

@Paul
Let us know pls about your installation and setup. I use no Openclaw or hermes presently but the setup sound very useful for any critical discussions, I would like to apply it using my main nodes: GPT, Claude, Gemini, as discussing experts, led by me as main coordinator of the brainstorming.

#167

Consulted Claude Code, Buzz seems to be too specific for coders. I am going to try Karpathy’s Council of LLMs maybe. Waiting for reviews on buzz.

The five hard truths:

  1. There is no “add agent” button. Each agent is a separate process you launch on your own laptop, with its own Nostr private key (nsec1…), its own env vars, its own API key. Four agents = four terminal windows humming on the HP Pavilion. Close the lid, the panel dies.
  2. Resource load. Each agent spawns its own MCP subprocess. The docs recommend starting at N=2; we want 4. On a 155H that’s survivable but not comfortable, especially with reasoning models chewing on long contexts.
  3. @mention-gating vs. debate. By default agents only speak when @mentioned — great for cost, useless for a free-flowing panel. Forum mode (--no-mention-filter) makes them respond to everything, which is what we want and is exactly how you get four models talking past each other in an unbounded, billable loop. No published loop-prevention beyond turn-duration caps.
  4. Cost is not linear. In a group chat every agent re-ingests the whole growing thread on every turn. Four agents × a two-hour session = a genuinely surprising API bill. Rough order: a single meaty brainstorm session runs single-digit to low-double-digit dollars; sloppy forum-mode looping can multiply that fast.
  5. Wrong instincts. These are coding agents. Pointed at “let’s brainstorm my consultancy positioning,” they’ll behave like coding agents in a trench coat unless we invest real work in system prompts and personas.

Verdict on Buzz: feasible, roughly 4–8 hours of setup for someone comfortable in a terminal, ongoing babysitting, and it is squarely a developer product. The value it adds over a simpler panel — persistent history, agents that can act on files and repos, cryptographic audit trail — is value we don’t currently need for brainstorming.

3. The alternative that actually matches the goal

Karpathy’s llm-council (~23k stars) does precisely what you described: one question goes to N models in parallel, each model then reviews and ranks the others’ answers anonymised, and a chairman model synthesises the final take. One OpenRouter key, one web app, no Docker, no four terminals, no per-provider accounts. There are hosted versions too.

What you lose: it’s turn-based Q&A, not a persistent room, and the models can’t do things — only argue.

Honest read: Council gives us ~80% of the intellectual value at ~10% of the setup cost. Buzz earns its complexity only once we want agents that act — open repos, edit the strategy doc, run workflows — and only once the Gemini/Grok bridges are proven by someone other than us.

1 Like
#168

I just used Karpahty’s LLM council for a technical question based on Quantitative Risk Analysis, one of my interests. A little work to set it up, but, honestly, Claude Code made it all.
Basically, a debate among Opus5, GPT5.6, Grok 4.5, KimiK3 and Gemini 3-1 Pro.
I was awed. It works better in topics where there is not much consensus (well-suited in health & longevity!).
I wonder what may be a question of general interest here.

2 Likes
#169

How did you set this up?

1 Like
#170

With the help of Claude Code. It did all the setup, I only clicked ‘YES’ at certain stages.

1 Like
#171

I use Claude (pro) near-daily and have since 2025. I employ multiple log and analysis files that are updated and which I load into new instances to keep the usage meter under control and get fresh perspectives.

In addition, I load a guidelines file that is extensive and detailed about how to interact with me, present data, organize ongoing hypotheses and questions, and stay within guardrails. I have a whole section about how the instance should self-reflect to most effectively reason, what to watch out for, and when it is most likely to be wrong.

This is not a panacea. I have to also think critically and ask and re-ask and challenge, even when I’m asking the instance to do its own challenging.

I thought about uploading my guidelines file for folks here, but this thread is already long. If anyone wants to see it, direct message me.

[THIS MESSAGE entirely human-generated, tho note from the brain of a post-migraine human :stuck_out_tongue: .]

4 Likes
#172

After trying to set up Buzz I found it rather challenging to do what I wanted but have found the greatest Agentic app - GrokBot - This is super easy to set up and with the built in computer for each agent i have it logged into various accounts. Very interesting to watch it work and the agents discuss ideas with each other

2 Likes
#173

A new and good resource I’m using to check on some new registrations and posts here (from suspicious people/ accounts that may be spam). You may find it valuable when you’re wondering if a given piece of text is AI generated or not:

Reviews:

https://phrasly.ai/blog/pangram-ai-detector-review

1 Like
#174

Has anyone used Muse in the context of this forum? Any use cases, feedback or comparators?

#175

I don’t know about Muse, but @Beth outlined a lot of what she’s doing which is quite interesting: SUGGESTIONS? What Company or MDs can I Hire To Get Me to 120 Years of Age? - #10 by Beth

1 Like
#176

@RapAdmin Awww, I love the feedback… and on that note, I was working on this post… I am in need of some major guidance!!!

I’m hoping some of the AI techie smarties can help me!!

I’ve spent months perfecting my Claude project to help with my health. It automatically (and manually) does research on everything I take and all my health conditions. I imagine most of you are using it in a similar way.

Yesterday I discovered Claude made a change and the model you are using no longer can do thorough and reliable research.

If you paste everything into your chat it can, but if you want it to go out searching or give it a link, it doesn’t.

What happens is: a lesser model (called a helper) goes to the websites and it decides what to show the model you are using in your chat… it might only show your model half the information … and garbage in garbage out.

So, I’m using opus or fable for things, but an old cheap model is making decisions on what to feed them, or is even blocked from doing so… my hair is on fire!!!

I learned you have to individually grant permission for each website in your settings if you want your model to be able to access anything. There is an option to ‘allow all sites’ but Claude advised against it. The reason is there are sites that might trick Claude into sending it information that I want to stay private… health, finances, etc.

Does anyone know about this… and if so, what do you do? I only discovered it accidentally when I noticed it missed many things on a site’s page.

I learned many sites also just block ai from doing anything, is there a workaround on this?

Because my tech prowess is fairly limited, I had fable describe the situation to you in a smarter way.

SUBJECT: Claude (Max plan) stopped reading web pages itself.
Is anyone getting around this safely?

MY SETUP

  • Claude Max. iPad (app and claude.ai website), plus a Mac
    mini.
  • Model: Claude Fable 5.1.
  • Use: health research. The model I chose has to read each
    source itself.

WHAT HAPPENS

  1. When Claude opens a web page with its built-in tool
    (WebFetch), a smaller helper model reads the page and
    hands my model its answer, not the page. The tool says
    so itself: it “answers [the] prompt against it using a
    small fast model.”
  2. The helper leaves things out. I have caught missing key
    facts in my own tests. Claude Code’s change log says
    that until version 2.1.290 the tool silently dropped
    all page text past 100,000 characters.
  3. Claude can download a page directly and read all of it,
    but only from sites on an account-wide list (Settings >
    Capabilities > Allow network egress > additional
    allowed domains). Every other site gets a 403 from the
    proxy.
  4. Even allowed sites can refuse. On Oct 9, europepmc.org
    was reachable but answered with a Cloudflare “Just a
    moment…” robot check.

ANTHROPIC’S OWN MANUAL SAYS SO

“For most fetches, Claude receives that model’s answer,
not the raw page.”

“This makes WebFetch lossy by design.”

WHY I’M STUCK

  • Adding sites one at a time works for PubMed. It cannot
    cover open-ended research, where I can’t predict the
    sites. This is the instance even if you want it to go out and search for and compare something as simple as supplements.
  • “All domains” is account-wide. My account holds private
    information in other chats and connected files.
    Anthropic’s own help page warns that a web page can
    trick Claude into sending information out. So I was advised to not turn it on.
  • Device makes no difference. Chats run in Anthropic’s
    cloud.

WHAT I’VE RULED OUT

  • A setting that makes WebFetch hand over the raw page:
    none in the Claude Code change log.
  • The desktop app’s built-in browser on a linked Mac:
    “blocked by your site permissions” on every site.
  • Not yet tested: the Claude in Chrome extension.

TIMELINE

  • Sept 16, 2026: chat and Cowork merged for Pro and Max.

QUESTIONS

  1. Has anyone found a way for the chosen model to read
    whole pages without turning on “All domains”?
  2. Does anyone run a second, empty account with “All
    domains” on, only for research? Does it work?
  3. Does Claude in Chrome give the main model the full page
    text, and does it get past robot checks?
  4. Can the allowed-domains list be set per project or per
    chat instead of account-wide?
  5. Do ChatGPT, Gemini or Perplexity let their top model
    read a whole page itself? How did you test it?
  6. Anthropic: is the helper model by design for chats, and
    is a “raw page” option planned?

(Summary written by Claude at my request.)

2 Likes
#177

Yes, I already embraced @Beth’s prompts into my Claude. Thats really amazing piece of work!

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#178

Do you know that chatgpt can connect to apple health and Oura ring as well as most medical websites that house medical records? Seems to be the best health app

2 Likes
#179

That is great to know @Paul

FYI, incase it’s helpful to you or anyone, I learned Claude (and probably others) can be programed to fetch Oura’s API daily and it will then analyze your sleep. I learned this at the Berkley longevity conference and have been doing it ever since. It’s really really cool!