Evaluation of MChip with historic subtype H1N1 influenza A viruses, including the 1918 "Spanish Flu" strain

Area of Science:

• Virology
• Bioinformatics
• Medical Diagnostics

Background:

• Influenza A virus (IAV) poses a significant global health threat.
• Accurate and rapid diagnostics are crucial for influenza surveillance and control.
• Historic influenza strains provide valuable insights into viral evolution and pandemic potential.

Purpose of the Study:

• To evaluate the robustness of the MChip, a novel diagnostic microarray for influenza.
• To assess the MChip's capability in detecting matrix gene segments from diverse historic H1N1 influenza A virus strains.
• To determine the efficacy of an artificial neural network in classifying historic H1N1 influenza A viruses using MChip data.

Main Methods:

• The MChip diagnostic microarray was utilized to analyze 16 historic subtype H1N1 influenza A viruses (A/H1N1).
• Matrix gene segments from all tested viruses were targeted for detection on the microarray.
• An artificial neural network (ANN) was trained using temporally related A/H1N1 virus data.
• The trained ANN was employed to classify the historic A/H1N1 strains, including A/Brevig Mission/1/1918.

Main Results:

• The MChip successfully detected the matrix gene segments from all 16 historic A/H1N1 influenza A viruses.
• The artificial neural network achieved a 94% probability in identifying the A/Brevig Mission/1/1918 strain as influenza A/H1N1.
• The results indicate high sensitivity and specificity of the MChip for H1N1 detection.

Conclusions:

• The MChip demonstrates robust performance in detecting influenza A virus matrix gene segments from historic strains.
• The integration of MChip data with artificial neural networks offers a promising approach for accurate influenza virus identification and classification.
• This diagnostic platform holds potential for enhanced influenza surveillance and research.