Kansas State University (KSU) researchers have developed mastitis detection technology that has the potential to detect early signs of preclinical and subclinical mastitis in dairy cows. The technology will not only speed up test times, but will also reduce the financial losses associated with disease spread, including herd and yield loss.
As most dairy producers well know, subclinical mastitis can be difficult to detect. This is mostly because there is an absence of visible indicators. But in order to avoid the substantial financial losses associated with the disease, it must be detected and treated at the subclinical stage. For this reason, technology that enables early detection could be huge for dairy producers around the world.
Designed by professors Stefan Bossmann and Deryl Troyer, the technology is a spin-off from cancer technology. Bossmann’s focus is chemistry and nanoparticle design, while Troyer focuses on the biological aspects.
Since acute and/or chronic inflammatory conditions, such as mastitis and arthritis often release proteases from leukocytes, the assay can be used to detect them. The assay is made up of protease-sensitive cleavage sequences, which are used to link two fluorophores – in this case, an iron nanoparticle and a fluorescent dye. The dye will then show the activity of the protease.
The assay’s central nanoparticle is tiny – about 15 nanometers wide, and attached with fluorescent dye. “Once the dyes are attached, they do not show fluorescence because the nanoparticle is like a big sink,” explains Bossmann. “It’s a big hole for fluorescence. All the light that is going there is absorbed by the nanoparticle, the dye gets away… and starts showing fluorescence.”
In simpler terms: If the fluorescent dye can be seen, mastitis-causing enzymes are present.
The final product, when ready, won’t take a bite of producers’ pocketbooks either. Each chip will have the ability to test about 32 cows, and at $2 to $5 each, producers will find the chips relatively inexpensive.
Troyer and Bossmann will begin testing prototypes next year. If all goes as planned the technology could be on the market in as little as two years. The researchers have been working collaboratively for over a year now.