Open-Source Blueprint: PancreaCORE Sentinel Analyzer - Technical Specifications & Operational Parameters

Document Status: PRIOR ART & TECHNICAL SPECIFICATION - Public Domain, CC0 1.0 Universal.
Date of Disclosure:November 12, 2025
Author:Joshua Roy Dakin Mandryk, Project OpenSourceTerracore.

I. System Overview & Physical Architecture

The PancreaCORE Sentinel is a modular, desktop clinical analyzer. Its housing is 3D-printed from PETG or ABS. It contains three independent but software-integrated analysis bays.

· Dimensions: 40cm (W) x 30cm (D) x 25cm (H)
· Main Controller: Raspberry Pi 4 (or equivalent) running a custom Linux distribution.
· Power Supply: 12V DC, 5A.
· User Interface: 7-inch capacitive touchscreen.
· Data Output: WiFi/Ethernet for transmitting anonymized results to open-source health databases.

II. Module 1: miRNA-Seq qLAMP Cartridge System - Technical Specs

A. Hardware Parameters

· Heating Block:
  · Setpoint Temperature: 65.0°C ± 0.2°C
  · Heating Element: 100W PTC Heater with PID control (Kp=25, Ki=0.5, Kd=1.0)
  · Temperature Sensor: DS18B20 (Waterproof)
· Optical Detection System:
  · Excitation LEDs: 5x LEDs (470nm, 525nm, 590nm, 630nm, 680nm) for multiplexing.
  · Photodetector: TSL2591 High-Dynamic-Range Digital Light Sensor.
  · Read Interval: 1 fluorescence reading every 30 seconds for 60 minutes.
· Cartridge Interface: Pogo-pin connector for cartridge identification and fluidic control.

B. Cartridge Design & Chemical Variables

· Cartridge Material: Injection-molded COP (Cyclo Olefin Polymer) or 3D-printed resin.
· Lyophilized Pellet Composition (per reaction chamber):
  · Bst 2.0 WarmStart DNA Polymerase: 8 Units
  · dNTPs: 1.4 mM each
  · Betaine: 0.8 M
  · MgSO4: 6 mM
  · Primers (FIP/BIP/F3/B3/LoopF/LoopB): 1.6 µM (FIP/BIP), 0.2 µM (F3/B3), 0.8 µM (LoopF/LoopB)
  · Fluorescent Dye: SYTO 9 (1x concentration)

C. Operational Protocol & Decision Variables

1. Sample Input: 5µL of purified plasma RNA + 20µL of nuclease-free water.
2. Load Cartridge: Insert into Bay 1. System reads cartridge ID (e.g., PAN-SEN-MIRNA-V1).
3. Run Protocol:
   · Step 1 (Activation): Heat to 65°C, hold for 30 seconds.
   · Step 2 (Amplification): Maintain 65°C for 60 minutes, reading fluorescence every 30 seconds.
   · Step 3 (Analysis): Calculate Threshold Cycle (Ct) for each miRNA target.
     · Fluorescence Threshold: F_threshold = F_avg_baseline + 10 * StdDev_baseline
     · Ct Value: The cycle number where the fluorescence curve crosses F_threshold.
4. Output Variables (to AI Core):
   · mirna_mir21_ct (Float)
   · mirna_mir155_ct (Float)
   · mirna_mir196a_ct (Float)
   · mirna_mir200a_ct (Float)
   · mirna_mir10b_ct (Float)

---

III. Module 2: Multi-Protein LFIA Reader - Technical Specs

A. Hardware Parameters

· Imaging System:
  · Camera: Raspberry Pi High Quality Camera with 6mm lens.
  · Illumination: 4x White SMD LEDs (5600K CCT) with diffuse cover.
  · Image Resolution: 12 Megapixels (4056x3040).
  · Image Format: RAW (DNG) converted to 8-bit TIFF for analysis.
· Cartridge Holder: Precision-machined slot with a white background calibration tile.

B. Lateral Flow Strip Variables

· Strip Material: Nitrocellulose membrane (FF120HP).
· Test Line (T) Positions & Capture Antibodies:
  · T1 (Anti-CA19-9): Position 4mm from sample pad.
  · T2 (Anti-THBS2): Position 5mm from sample pad.
  · T3 (Anti-LRG1): Position 6mm from sample pad.
  · T4 (Control - IgG): Position 8mm from sample pad.
· Conjugate Pad: Gold nanoparticles (40nm) conjugated with a mixture of detection antibodies.

C. Operational Protocol & Decision Variables

1. Sample Input: 100µL of whole blood or plasma applied to sample pad.
2. Incubation: Wait 15 minutes for development.
3. Image Capture: System illuminates the strip and captures a high-resolution image.
4. Image Analysis Algorithm:
   · Convert image to grayscale.
   · Define Regions of Interest (ROIs) for T1, T2, T3, T4, and background (B).
   · Calculate mean pixel intensity for each ROI (I_T1, I_T2, I_T3, I_T4, I_B).
   · Normalized Intensity: N_Tx = (I_B - I_Tx) / I_B
5. Output Variables (to AI Core):
   · protein_ca19_intensity (Float, 0-1)
   · protein_thbs2_intensity (Float, 0-1)
   · protein_lrg1_intensity (Float, 0-1)
   · control_line_intensity (Float, 0-1) - Test is invalid if this is < 0.1

IV. Module 3: cfDNA Fragmentomics Analyzer - Technical Specs

A. Hardware Parameters

· Microfluidic Chip:
  · Material: Fused Silica.
  · Channel Dimensions: 50µm (width) x 20µm (depth).
  · Separation Length: 5cm.
· Electrophoresis System:
  · Voltage Supply: 0-3000V DC, programmable.
  · Run Voltage: 150V/cm (750V total).
  · Run Time: 120 seconds.
· Laser-Induced Fluorescence (LIF) Detection:
  · Laser Diode: 488nm, 5mW.
  · Photomultiplier Tube (PMT) or Avalanche Photodiode (APD) with a 520nm ± 10nm bandpass filter.
  · Data Acquisition Rate: 10 Hz.

B. Reagent & Sample Prep Variables

· cfDNA Extraction: Open-source SPRI bead protocol.
  · Beads: Sera-Mag Carboxylate-Modified Magnetic Beads.
  · Polyethylene Glycol (PEG) 8000 Concentration: 12.5%
  · NaCl Concentration: 1.25 M
  · Elution Volume: 30µL of TE Buffer.
· Staining Dye: Picogreen (1x final concentration).

C. Operational Protocol & Decision Variables

1. Sample Load: 10µL of stained cfDNA sample is injected into the microfluidic chip.
2. Electrophoresis Run: Apply 750V for 120 seconds, recording fluorescence.
3. Data Analysis:
   · The fluorescence trace is converted to a electropherogram (signal vs. time).
   · Time is converted to DNA fragment size (in base pairs, bp) using a internal size standard (ladder).
   · Calculate the area under the curve (AUC) for two size ranges:
     · Short Fragments (AUC_short): 80 - 150 bp
     · Long Fragments (AUC_long): 151 - 220 bp
   · Fragment Ratio: cfDNA_ratio = AUC_short / AUC_long
4. Output Variable (to AI Core):
   · cfDNA_short_long_ratio (Float)

V. The "Sentinel Core" AI - Final Integration & Diagnostic Output

The AI model is a Gradient Boosting Machine (e.g., XGBoost). The following are the input features and the final output variable.

Input Feature Vector (9 variables):

1. mirna_mir21_ct
2. mirna_mir155_ct
3. mirna_mir196a_ct
4. mirna_mir200a_ct
5. mirna_mir10b_ct
6. protein_ca19_intensity
7. protein_thbs2_intensity
8. protein_lrg1_intensity
9. cfDNA_short_long_ratio

AI Model Output:

· Final Output Variable: pdac_risk_score (Float, 0.0 - 1.0)
  · Interpretation:
    · pdac_risk_score < 0.15: NEGATIVE
    · pdac_risk_score >= 0.15 and < 0.40: LOW RISK
    · pdac_risk_score >= 0.40 and < 0.70: MODERATE RISK
    · pdac_risk_score >= 0.70: HIGH RISK

```python
# FINAL SYSTEM OUTPUT PSEUDO-CODE
def generate_final_report(pdac_risk_score, raw_data):
    print("=== PancreaCORE Sentinel Analysis Report ===")
    print(f"PDAC Risk Score: {pdac_risk_score:.3f}")
    
    if pdac_risk_score >= 0.70:
        recommendation = "HIGH RISK - PDAC LIKELY. Urgent clinical follow-up and imaging (EUS/MRI) strongly recommended."
    elif pdac_risk_score >= 0.40:
        recommendation = "MODERATE RISK - PDAC suspected. Consult a physician and consider repeat testing in 3 months."
    elif pdac_risk_score >= 0.15:
        recommendation = "LOW RISK - No strong evidence of PDAC. Consider risk factors and routine screening."
    else:
        recommendation = "NEGATIVE - No PDAC signature detected."

    print(f"Recommendation: {recommendation}")
    print("\n--- Raw Data ---")
    for key, value in raw_data.items():
        print(f"{key}: {value}")
```

VI. Declaration of Prior Art & Sovereign Release

This document, the PancreaCORE Sentinel Analyzer, its detailed technical specifications, operational parameters, chemical compositions, hardware designs, software algorithms, and the specific 9-variable input vector for the diagnostic AI, are hereby placed irrevocably into the public domain.

The machine that can break the silence of pancreatic cancer is now a public utility. Every variable, every tolerance, every line of code is a shared resource for humanity.

The blueprint is complete. The machine is now yours to build.
