Point-of-Care Diagnostics for Farm Animal Diseases: From Biosensors to Integrated Lab-on-Chip Devices

Biosensors (Basel). 2022 Jun 26;12(7):455. doi: 10.3390/bios12070455.

Abstract

Zoonoses and animal diseases threaten human health and livestock biosecurity and productivity. Currently, laboratory confirmation of animal disease outbreaks requires centralized laboratories and trained personnel; it is expensive and time-consuming, and it often does not coincide with the onset or progress of diseases. Point-of-care (POC) diagnostics are rapid, simple, and cost-effective devices and tests, that can be directly applied on field for the detection of animal pathogens. The development of POC diagnostics for use in human medicine has displayed remarkable progress. Nevertheless, animal POC testing has not yet unfolded its full potential. POC devices and tests for animal diseases face many challenges, such as insufficient validation, simplicity, and portability. Emerging technologies and advanced materials are expected to overcome some of these challenges and could popularize animal POC testing. This review aims to: (i) present the main concepts and formats of POC devices and tests, such as lateral flow assays and lab-on-chip devices; (ii) summarize the mode of operation and recent advances in biosensor and POC devices for the detection of farm animal diseases; (iii) present some of the regulatory aspects of POC commercialization in the EU, USA, and Japan; and (iv) summarize the challenges and future perspectives of animal POC testing.

Keywords: biosensors; challenges of point-of-care testing; farm animal diseases; future perspectives; lab-on-chip devices; lateral flow assays; legislation and regulation; micro total analysis systems; microfluidics; point-of-care diagnostics.

Publication types

  • Review

MeSH terms

  • Animal Diseases* / diagnosis
  • Animals
  • Animals, Domestic
  • Biosensing Techniques*
  • Farms
  • Humans
  • Lab-On-A-Chip Devices
  • Laboratories
  • Point-of-Care Systems
  • Point-of-Care Testing

Grants and funding

This work was funded by the EU’s H2020 SWINOSTICS project under the grant agreement ID 771649.