Reverse Cardiac Aging Safely

class LNPRegressionSurrogate(nn.Module):
    """
    PyTorch Multi-Layer Perceptron (MLP) Surrogate
    Model for LNP delivery prediction.
    
    Inputs (6 features):
    1. Ionizable lipid molar fraction (0.0 to 1.0)
    2. Helper lipid molar fraction (0.0 to 1.0)
    3. Cholesterol molar fraction (0.0 to 1.0)
    ...
    """
    def __init__(self, input_dim: int = 6):
        super(LNPRegressionSurrogate, self).__init__()
        self.network = nn.Sequential(
            nn.Linear(input_dim, 128),
            nn.GELU(),
            nn.Dropout(0.2),
            nn.Linear(128, 4) # Outputs: Tropism, Half-Life...
        )

# Connecting to Zenith Hardware Backend...
[OK] Authenticated Labcyte Echo 525 Liquid Handler.

def generate_echo_csv(well_plate, volumes):
    # Quantize volumes to discrete 2.5 nL hardware steps
    quantized_vols = [round(v / 2.5) * 2.5 for v in volumes]
    
    csv_rows = []
    for src, dest, vol in zip(source_wells, well_plate, quantized_vols):
        csv_rows.append(f"{src},{dest},{vol}")
        
    return "\n".join(csv_rows)

[EXEC] Generating physical protocol spreadsheet...
[SUCCESS] Output: echo_protocol_v3.csv ready for manufacturing.