Development of ion torrent-based targeted next-generation sequencing panel for identification of animal species in pet foods.

Manufacturers may intentionally or unintentionally incorporate ingredients not specified on the label of canned pet foods. Including any unacknowledged ingredients in a food product is considered food fraud or misbranding. Contamination of pet foods may occur in the processing of the foods, including potential cross-contamination in packaging facilities. Of the methods available to identify meat species in food products, Sanger sequencing and several next-generation sequencing methods are available, but there are limitations including the number of targets analyzed at a time and the method specificity. In this study, we developed a targeted next-generation sequencing panel to detect meat species in canned pet foods using Ion Torrent technology. The panel contains multiple primers targeting mitochondrial genes from as many as 27 animal species, of which 7 major animal species were validated. The meat species targets could be identified from samples spiked with as low as 0.01% w/w of the contaminating meat species in a vegetarian food matrix material. Targeted NGS in the current study enriches species-specific multiple target areas in the mitochondrial genome of the target material, which gives high accuracy in the sequencing results.

Development and validation of a magneto-hydrodynamic solver for blood flow analysis.

The objective of this study was to develop a numerical solver to calculate the magneto-hydrodynamic (MHD) signal produced by a moving conductive liquid, i.e. blood flow in the great vessels of the heart, in a static magnetic field. We believe that this MHD signal is able to non-invasively characterize cardiac blood flow in order to supplement the present non-invasive techniques for the assessment of heart failure conditions. The MHD signal can be recorded on the electrocardiogram (ECG) while the subject is exposed to a strong static magnetic field. The MHD signal can only be measured indirectly as a combination of the heart's electrical signal and the MHD signal. The MHD signal itself is caused by induced electrical currents in the blood due to the moving of the blood in the magnetic field. To characterize and eventually optimize MHD measurements, we developed a MHD solver based on a finite element code. This code was validated against literature, experimental and analytical data. The validation of the MHD solver shows good agreement with all three reference values. Future studies will include the calculation of the MHD signals for anatomical models. We will vary the orientation of the static magnetic field to determine an optimized location for the measurement of the MHD blood flow signal.