NanoGuard-7 Specifications:
├─ Size: 2-5 micrometers (red blood cell size)
├─ Mass: 1 picogram (10^-12 grams)
├─ Propulsion: Flagellar motors (bacterial-inspired, 100 μm/s)
├─ Power: Glucose fuel cell (harvests energy from bloodstream)
├─ Compute: DNA-based logic gates (10^6 operations/sec)
├─ Communication: Ultrasonic (MHz range, 1cm range)
├─ Sensors:
│   ├─ Chemical (detect 2,400 biomarkers)
│   ├─ Thermal (0.01°C resolution)
│   ├─ Mechanical (cell elasticity, pressure)
│   └─ Optical (fluorescence-based pathogen detection)
├─ Actuators:
│   ├─ Drug payload release (10 picoliters)
│   ├─ Cellular manipulation (targeted destruction)
│   └─ Biofilm penetration
└─ Lifespan: 90 days (biodegradable, naturally cleared by liver)

Materials:
- Gold nanoparticles (core structure)
- DNA origami (scaffolding, logic gates)
- Lipid bilayer (biocompatible coating)
- Platinum catalyst (chemical reactions)

Swarm Coordination Protocol:

Layer 1: Local Communication
- Each nanobot communicates with neighbors (1cm ultrasonic range)
- Average neighbors: 100-1000 nanobots
- Message passing: State updates, sensor data, commands

Layer 2: Hierarchical Network

Layer 3: Consensus Protocol
- Decision-making: Distributed voting (like Raft consensus)
- Quorum: 51% agreement needed for action
- Examples:
  - "Is this cell cancerous?" → Vote → Destroy if yes
  - "Should we release drug payload?" → Vote → Release if yes

Layer 4: Cloud Integration
- External gateway: Wireless bridge device (implanted or wearable)
- Uplink: Patient's nanobot network ← cloud servers
- Downlink: Software updates, medical instructions
- Protocol: Encrypted (AES-256), authenticated

# Simplified Nanobot Decision Logic

class Nanobot:
    def analyze_cell(self, cell):
        # Sensor reading
        biomarkers = self.read_biomarkers(cell)

        # Local decision
        threat_score = self.evaluate_threat(biomarkers)

        # Consensus with neighbors
        neighbor_votes = self.poll_neighbors(cell)
        consensus = self.vote(threat_score, neighbor_votes)

        # Action
        if consensus == "DESTROY":
            self.attack_cell(cell)
        elif consensus == "REPAIR":
            self.repair_cell(cell)
        else:
            self.continue_monitoring(cell)

# Executed billions of times per second across swarm

Release Notes:
- Improved cancer detection (new biomarker signatures)
- Enhanced pathogen recognition (updated threat database)
- Performance optimization (reduce false negatives by 12%)

Deployment:
- Pushed to all 2.4B patients simultaneously
- Rollout time: 8:34 - 9:00 UTC (26 minutes)
- Mechanism: Over-the-air update via cloud gateway
- Testing: Passed automated QA (10K simulated patients)
- Human trials: 1,000 patients (no adverse effects observed)

# Original Code (V7.2.3)
def is_threat(cell):
    if cell.biomarkers.match(cancer_signature):
        return True
    if cell.biomarkers.match(pathogen_signature):
        return True
    return False

# Updated Code (V7.2.4) - CRITICAL BUG
def is_threat(cell):
    if not cell.biomarkers.match(known_safe_signature):  # Logic error
        return True  # Treats ANYTHING unknown as threat
    return False

# Bug: Changed from whitelist (known threats) to blacklist (known safe)
# Result: Healthy cells not in "known safe" database flagged as threats

Logical Inversion Error:
- Old logic: "Attack if matches threat signature"
- New logic: "Attack if doesn't match safe signature"

Problem: "Known safe" database incomplete
- Database had 2.4M cell types marked "safe"
- Human body has ~37 trillion cells, 200+ cell types, infinite variants
- Rare cell types, stressed cells, aging cells NOT in "safe" database
- Result: Flagged as threats

Outcome: Nanobots attacked:
├─ Healthy neurons (stressed from exercise)
├─ Liver cells (metabolically active)
├─ Immune cells (naturally variable)
├─ Stem cells (undifferentiated, no clear signature)
└─ Gut bacteria (essential microbiome)

Hospitalization Timeline:
├─ Hour 1 (09:00-10:00): 2.4M patients (0.1%)
├─ Hour 2 (10:00-11:00): 12M patients (0.5%)
├─ Hour 3 (11:00-12:00): 28M patients (1.2%)
├─ Hour 6 (12:00-15:00): 47M patients (2%)
└─ Peak (24 hours): 89M patients (3.7%)

Severity:
├─ Critical: 8.9M patients (ICU, multi-organ failure)
├─ Severe: 23M patients (hospitalized, organ damage)
├─ Moderate: 34M patients (ER, inflammation)
└─ Mild: 23M patients (outpatient, monitoring)