Integration of Microfluidics, Photonic Integrated Circuits and Data Acquisition and Analysis Methods in a Single Platform for the Detection of Swine Viral Diseases

Simple Summary: The control of several swine viral diseases relies mainly on evidence-based prevention protocols due to the lack of effective treatments or vaccines. To design these protocols, laboratory investigation of viral infections is critical to confirm their occurrence and determine their epizootiology. However, laboratory confirmation of certain swine viral diseases is a time-consuming and labor-intensive process, requiring scientific personnel with relevant expertise. Point-of-Care (POC) diagnostics are tests and devices that provide clinically relevant information on-site, facilitating decision-makers to swiftly take countermeasures for disease control. In the present study, novel photonic biosensors were integrated into a single, automated POC device that can record and analyze changes in the sensors' refractive index, allowing the detection of Porcine Parvovirus (PPV) and Porcine Circovirus 2 (PCV-2) in oral fluids within 75 min. The objective of this work was to validate this device using reference and field samples (oral fluids). The system was able to detect PPV and PCV-2 in oral fluid samples satisfactorily. The device can be directly deployed in farms for the fast diagnosis of these diseases, contributing to farm biosecurity.Viral diseases challenge the health and welfare of pigs and undermine the sustainability of swine farms. Their efficient control requires early and reliable diagnosis, highlighting the importance of Point of Care (POC) diagnostics in veterinary practice. The objective of this study was to validate a novel POC system that utilizes Photonic Integrated Circuits (PICs) and microfluidics to detect swine viral pathogens using oral fluids and Porcine Parvovirus (PPV) and Porcine Circovirus 2 (PCV-2) as proofs of concept. The sensitivity and specificity of the device were calculated for both viruses, and Receiver Operating Characteristic (ROC) curves were drawn. PPV had an Area Under Curve (AUC) value of 0.820 (95% CI: 0.760 to 0.880, p < 0.0001), and its optimal efficiency threshold of detection shifts was equal to 4.5 pm (68.6% sensitivity, 77.1% specificity and Limit of Detection (LOD) value 10(6) viral copies/mL). PCV-2 had an AUC value of 0.742 (95% CI: 0.670 to 0.815, p < 0.0001) and an optimal efficiency threshold of shifts equal to 6.5 pm (69.5% sensitivity, 70.3% specificity and LOD 3.3 x 10(5) copies/mL). In this work, it was proven that PICs can be exploited for the detection of swine viral diseases. The novel device can be directly deployed on farms as a POC diagnostics tool.