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Semi-Quantitative Method for Streptococci Magnetic Detection in Raw Milk.
- Source :
-
Biosensors [Biosensors (Basel)] 2016 Apr 27; Vol. 6 (2), pp. 19. Date of Electronic Publication: 2016 Apr 27. - Publication Year :
- 2016
-
Abstract
- Bovine mastitis is the most costly disease for dairy farmers and the most frequent reason for the use of antibiotics in dairy cattle; thus, control measures to detect and prevent mastitis are crucial for dairy farm sustainability. The aim of this study was to develop and validate a sensitive method to magnetically detect Streptococcus agalactiae (a Group B streptococci) and Streptococcus uberis in raw milk samples. Mastitic milk samples were collected aseptically from 44 cows with subclinical mastitis, from 11 Portuguese dairy farms. Forty-six quarter milk samples were selected based on bacterial identification by conventional microbiology. All samples were submitted to PCR analysis. In parallel, these milk samples were mixed with a solution combining specific antibodies and magnetic nanoparticles, to be analyzed using a lab-on-a-chip magnetoresistive cytometer, with microfluidic sample handling. This paper describes a point of care methodology used for detection of bacteria, including analysis of false positive/negative results. This immunological recognition was able to detect bacterial presence in samples spiked above 100 cfu/mL, independently of antibody and targeted bacteria used in this work. Using PCR as a reference, this method correctly identified 73% of positive samples for streptococci species with an anti-S. agalactiae antibody, and 41% of positive samples for an anti-GB streptococci antibody.
- Subjects :
- Animals
Cattle
Food Safety
Humans
Microfluidics
Reproducibility of Results
Sensitivity and Specificity
Streptococcus agalactiae genetics
Streptococcus agalactiae isolation & purification
Biosensing Techniques
Food Microbiology methods
Magnetite Nanoparticles
Milk microbiology
Streptococcus genetics
Streptococcus isolation & purification
Subjects
Details
- Language :
- English
- ISSN :
- 2079-6374
- Volume :
- 6
- Issue :
- 2
- Database :
- MEDLINE
- Journal :
- Biosensors
- Publication Type :
- Academic Journal
- Accession number :
- 27128950
- Full Text :
- https://doi.org/10.3390/bios6020019