ADVISOR EVIDENCE PORTFOLIO
Dr. Hassan Ghasemzadeh — EMIL Lab, ASU College of Health Solutions
Participant-sovereign records · L1/L2/L3 biochemical targeting · biosensor consent scaffold · ML-ready schema
The schema covers the full participant lifecycle across 5 platforms: session management (fsl_sessions, sovereign_sessions, session_bookings, session_logs, session_notes, session_reminders, session_attestations, session_superbills), consent governance (consent_grants, consent_records, consent_policies, onboarding_agreements), health data (fsl_health_data, lab_results, nutrition_logs, biometric_readings, biotype_assessments, food_allergies, wellness_records), token economics (mint_logs, participant_welcome_grants, revenue_splits, user_subscriptions), provider infrastructure (provider_accounts, provider_availability, provider_schedules, provider_credentials, provider_verifications, practitioner_profiles), and platform operations (trial_access, cta_analytics, mood_logs, avatar_sessions, alchemist_forge_events).
42 tables contain live data from testnet activity: session records, consent grants, mood logs, AlchemistForge events, welcome grants, provider accounts, and operational metrics. These represent real system interactions on Sepolia, not seed data.
30 tables are defined with full column schemas but contain no rows yet. These represent planned integrations: group sessions, biosensor tokens, supplement protocols, nutrition protocols, provider referrals, eligibility checks, and program enrollments. The schemas are defined and waiting for feature activation or biosensor partner integration.
No table contains any of the 18 HIPAA Safe Harbor identifiers. Participants are identified solely by pseudonymous Ethereum wallet addresses. The schema includes email columns in two operational tables (session_reminders, operator_applications) and phone/address in practitioner_profiles — these contain operator/practitioner contact info, not participant health data. No patient names, dates of birth, SSNs, medical record numbers, or clinical diagnoses are stored anywhere in the schema.
Three-layer wellness engagement progression: quiet the mind, understand food/mood, explore inner patterns. Self-directed educational activities informed by established modalities. 4 flagship demos built as wired prototypes for full build by 3D game designers.
Demo: Threshold Bloom · Approach: Relaxation and mind-quieting engagement informed by hypnosis, EMDR, and bilateral tapping principles · Guided visualization for self-directed wellness · Entry-level engagement designed for first-contact participants
Demo: Amino Resonance Weaver · Approach: Educational content informed by nutritional psychiatry and orthomolecular principles · All amino acid, food-compound, neurotransmitter-precursor, and supplement educational content consolidates here · Endocannabinoid system (ECS) tone as the orthomolecular baseline · Precursors (tryptophan, tyrosine, GABA precursors) mapped to dietary sources and neurotransmitter synthesis pathways
Demo: Neural Bloom · Approach: Reflective engagement informed by inner child and regression principles · Somatic awareness and safety-boundary exploration · The deepest engagement layer, accessed after L1 mind-quieting and L2 nutritional education
ECS tone regulation is the orthomolecular baseline for ALL participants, not a per-layer target. The framework positions ECS modulation as the biochemical foundation — a nutritional and lifestyle-based approach to endocannabinoid tone that underlies all three engagement layers.
L2 consolidates all biochemical educational content: amino acid pathways map to neurotransmitter precursors, dietary compounds map to receptor activation profiles, and supplement protocols target specific synthesis pathways. This is domain-specific feature engineering for wellness engagement, informed by orthomolecular principles — not generic wellness tracking.
Current status: Static educational guidance — the framework maps precursors to neurotransmitter systems, but measurement is manual (participant-reported intake, lab uploads).
This is where Dr. Ghasemzadeh’s biosensor expertise extends the orthomolecular framework — from educational precursor mapping to measured precursor detection recorded in the sovereign record.
| Phase | Layer | Status |
|---|---|---|
| Now | L2 orthomolecular mapping — educational precursor → NT system guidance | Built |
| Phase 1 | Orthomolecular AI — interpret user-reported intake + uploaded lab results against precursor pathways | Schema-ready |
| Phase 2 | Biosensor detection — amino-acid precursor levels measured in sweat/saliva → fed into participant’s sovereign record as longitudinal data | Pending |
The arc: static precursor education (now) → AI-interpreted self-reported + lab data (Phase 1) → continuous biosensor-measured precursor levels (Phase 2). Measured readings stored in participant-controlled records (biometric_readings, lab_results, biotype_assessments — schema-ready), wallet-gated via NeuroBalanceConsent, zero PHI custody by FSL. The participant owns the longitudinal precursor data.
Dr. Meg is Participant. Shadow aspect recorded on-chain via AlchemistForge (0x5e17...3c3B). Voluntary, permissionless, immutable, Blockscout-verifiable. Demonstrates the ethics model: the architect used the system before asking anyone else to.
The Transmuted and Celebrated events are publicly queryable on Blockscout. Any reviewer can independently verify that a shadow integration ritual occurred for this wallet address. No centralized database, no deletable records, no administrative override.
| Credential | Full Name | Relevance |
|---|---|---|
| D.N.Psy. | Doctor of Naturopathic Psychology | Clinical practice authority — understands the therapeutic context the architecture serves |
| BCHN | Board Certified in Holistic Nutrition | Orthomolecular framework expertise — amino acid pathways, ECS modulation, nutritional neuroscience |
| CBHP | Certified Blockchain Healthcare Professional — Blockchain Council | Blockchain healthcare domain knowledge — consent topology, session dynamics, participant safety |
| NPI 1497696264 | National Provider Identifier (Naturopath, 175F00000X) | NPPES-registered clinical provider — verifiable active practitioner status in the national registry |
Credential stack spans clinical practice + technical architecture. The system was designed by someone who has practiced on both sides: as a clinician navigating the broken consent systems and as an engineer building the replacement.
Credentialed wellness facilitators are Sovereign Guides (never “providers”). Users are Participants (never “patients”). The system operates in the wellness and educational engagement space, not as a clinical treatment platform.
Participants engage with neurotransmitter systems and symptom-pathway exploration — not diagnostic labels or clinical categories. The L1/L2/L3 framework organizes self-directed wellness engagement informed by established modalities (hypnosis, orthomolecular principles, inner child work) with neurotransmitter-precursor educational content consolidated in L2. No psychiatric diagnoses are recorded, stored, or implied.
Participant-initiated sessions from the dashboard write a wallet-gated room record and open the SovereignSession peer-to-peer video app; the SovereignSession contract is deployed on Sepolia but is not invoked by any code path. Session completion records on-chain attestation via SovereignLedger v2 registerClaim() (deployer-signed), with HNT engagement rewards minted server-side. Bilateral co-signing (Guide + Participant) is the Phase 5 doctoral contribution — not yet implemented.
NEAR-TERM ASKS — BUILDS ON DEPLOYED INFRASTRUCTURE
Participants upload records (labs, medication lists) via the deployed Lighthouse/IPFS pipeline. The ask: domain-specific AI agents that provide educational information — what a record is, documented side effects in plain language, mechanism/ingredient context, and naturopathic-perspective neurotransmitter framing.
Example: Participant uploads a medication list containing Bupropion (Wellbutrin). The AI surfaces: “Bupropion is a norepinephrine-dopamine reuptake inhibitor. Documented side effect: seizure risk increases at higher doses (FDA black-box context). Naturopathic lens: dopamine pathway — amino acid precursor: L-tyrosine.” This is educational information about a documented risk, sourced to legitimate references — not clinical guidance, not a recommendation to change medication.
Each company runs its own domain-specific AI agents. FSL’s naturopathic-informed lens is the domain specialization. Framed educational, sourced to legitimate references, NOT medical advice.
Realize the NeuroBalance scaffold: wearable/biosensor data detecting early physiological precursor signals — HRV patterns, sleep architecture, stress markers — relevant to mental-wellness states. Maps directly to Dr. Ghasemzadeh’s published research (AI-Powered Wearable Sensors, CAN-STRESS). Early-warning wellness signals, not diagnostic output.
NeuroBalanceConsent contract deployed at 0x2157...96b8 (scaffolded). 42 schema-ready PostgreSQL tables purpose-built for ML pipeline consumption.
Participant uploads a record (lab/medication list) → AI surfaces educational content → participant completes a 3-question comprehension quiz proposed extension → quiz completion fires a smart contract → recorded on-chain + added to chart → HNT reward → HNT redeemable for practitioner-service discounts.
Quiz structure (3 questions, AI-determined from the uploaded record):
Supplements coordinated through your practitioner if recommended — discuss with your practitioner.
HNT anchor: The upload itself is the existing locked HNT trigger (lab upload → 10 HNT). The quiz-gated comprehension layer is a proposed extension to the ratified earning model.
Practitioner protection: Guides are paid from the full session value; FSL treasury absorbs the discount (≤22%). HNT discounts are an engagement/acquisition cost absorbed by FSL’s operations margin; sustains at scale where paid volume exceeds redemption.
If the platform progresses through clinical/regulatory validation gates: deeper clinical informatics integration, structured medication-depth features, and research-grade outcome measurement. This is future-conditional — gated on progression, IRB approval, and clinical validation. Not current capability.
FUTURE VISION — NOT BUILT, NOT NEAR-TERM
Each of the 27 designed sessions ends with the same 3-question structure: top documented side effect, orthomolecular food + amino acid recommendation, and an inner-child stillness activity. Across the full library this creates spaced repetition + associative conditioning — the participant repeatedly associates internal state with food, mood, amino-acid pathways, and embodied practices. The repetition is the pedagogy; the token reward reinforces; the game wrapper makes it interactive rather than clinical.
This is behavioral-health learning architecture — Dr. Ghasemzadeh’s health-informatics domain. The 27-session designed catalog is the vehicle; the spaced-repetition pedagogical layer is the research contribution his lab would shape.
As FSL expands beyond naturopathic methodologies into real-world clinical practice: professional game/VR designers build immersive therapeutic experiences on the platform — notably VR-delivered EMDR. EMDR’s core mechanism is bilateral stimulation (alternating left-right sensory input); VR is an ideal medium for delivering precisely-timed, controlled bilateral visual/auditory/haptic stimulation. Running this on FSL’s decentralized infrastructure means the immersive therapeutic session inherits the sovereign-consent architecture: bilateral wallet-signed consent (Phase 5), session attestation on-chain, participant-controlled data — the same rails, now carrying VR therapeutic content.
Research convergence: this unites three threads — VR therapeutics + EMDR clinical protocol + decentralized consent/data-sovereignty infrastructure. VR adds another sensor/delivery modality to the EMIL Lab’s digital-health, behavioral-intervention, and wearable/biosensor research portfolio. Positions FSL’s game framework as a serious health-technology research vehicle Dr. Ghasemzadeh’s lab could help shape, validate, and publish on.
Post-doctoral horizon — aspirational, not current scope. The deployed platform (4 playable demos, consent infrastructure, on-chain attestation) is the foundation; VR-EMDR is the research trajectory that makes leading the clinical arm of a growing platform compelling.
Both elements are proposed/future. They show where the research could go with Dr. Ghasemzadeh’s involvement — the runway, not the runway lights that are already on.
Schema-ready tables designed for ML pipeline consumption. Currently holding schema definitions with minimal seed data. Ready for sensor data integration.
| Table | Status | ML Relevance |
|---|---|---|
| biometric_readings | 5 rows | Primary sensor ingestion — timestamped biometric values with device metadata |
| biometric_tokens | Schema-ready | Consent token mapping — which participant authorized which data stream |
| lab_results | Schema-ready | Orthomolecular lab values — amino acid panels, hormone levels, metabolic markers |
| nutrition_logs | Schema-ready | Dietary intake tracking — food-compound-neurotransmitter correlation data |
| nutrition_protocols | Schema-ready | Practitioner-designed nutrition plans — amino acid supplementation schedules |
| supplement_protocols | Schema-ready | Supplement tracking — dosage, timing, neurotransmitter target mapping |
Objective (co-advisor domain — operational validation methodology): The ecosystem architecture (Dr. Bošković, primary advisor) and the consent formalization (Dr. Ahn, co-advisor) produce a protocol. Your domain is the empirical question: does it work in live behavioral-health sessions? Session workflow impact, completion rates, latency, and participant comprehension are the operational metrics. Your EMIL Lab’s methodology for deploying and validating health-technology systems in real-world settings turns the architecture into evidence. Patents: U.S. Provisional 64/063,037 + 64/106,748.
The Ask: Two Near-Term Deliverables + Future Scope
Near-term 1 — AI Educational Interpretation: Domain-specific AI agents providing educational information on uploaded records (labs, medications) — documented side effects in plain language, mechanism context, naturopathic neurotransmitter framing. Example: Bupropion/Wellbutrin → seizure risk (documented, recognizable) + dopamine pathway + L-tyrosine precursor. Educational, sourced, NOT clinical guidance.
Near-term 2 — Biosensor Precursors: Realize the NeuroBalance scaffold with wearable data detecting early physiological precursor signals (HRV, sleep, stress markers). Maps to published EMIL Lab research. Early-warning wellness signals, not diagnostic output.
Engagement loop: Participant reads educational content → completion fires smart contract → on-chain attestation + chart update + HNT reward → HNT redeemable for practitioner discounts. Practitioners never lose money (FSL treasury absorbs discount up to 22%; Guide payout = full session value). The AI layer drives the economic loop.
Scope boundary: Both near-term asks are non-clinical educational support. Clinical/medication depth is future-conditional, gated on progression + IRB + regulatory validation.