Magnetoresistive sensors using spin valves and magnetic tunnel junctions are reviewed,
considering applications as readers in hard disk drives, as well as applications where the
ultimate field detection limits are required (from nT down to pT). The sensor noise level in
quasi-DC or high-frequency applications is described, leading to sensor design
considerations concerning biomedical and read head applications. Magnetic tunnel junction
based sensors using MgO barriers appear as the best candidates for ultra-low field
(pT) detection, either in the high-frequency regime, or for quasi-DC applications.
Bovine mastitis is an inflammation of the mammary gland caused by a multitude of pathogens with devastating consequences for the dairy industry. Global annual losses are estimated to be around €30 bn and are caused by significant milk losses, poor milk quality, culling of chronically infected animals, and occasional deaths. Moreover, mastitis management routinely implies the administration of antibiotics to treat and prevent the disease which poses serious risks regarding the emergence of antibiotic resistance. Conventional diagnostic methods based on somatic cell counts (SCC) and plate-culture techniques are accurate in identifying the disease, the respective infectious agents and antibiotic resistant phenotypes. However, pressure exists to develop less lengthy approaches, capable of providing on-site information concerning the infection, and in this way, guide, and hasten the most adequate treatment. Biosensors are analytical tools that convert the presence of biological compounds into an electric signal. Benefitting from high signal-to-noise ratios and fast response times, when properly tuned, they can detect the presence of specific cells and cell markers with high sensitivity. In combination with microfluidics, they provide the means for development of automated and portable diagnostic devices. Still, while biosensors are growing at a fast pace in human diagnostics, applications for the veterinary market, and specifically, for the diagnosis of mastitis remain limited. This review highlights current approaches for mastitis diagnosis and describes the latest outcomes in biosensors and lab-on-chip devices with the potential to become real alternatives to standard practices. Focus is given to those technologies that, in a near future, will enable for an on-farm diagnosis of mastitis.
Salmonellosis, one of the most common food and water-borne diseases, has a major global health and economic impact. Salmonella cells present high infection rates, persistence over inauspicious conditions and the potential to preserve virulence in dormant states when cells are viable but non-culturable (VBNC). These facts are challenging for current detection methods. Culture methods lack the capacity to detect VBNC cells, while biomolecular methods (e.g. DNA- or protein-based) hardly distinguish between dead innocuous cells and their viable lethal counterparts. This work presents and validates a novel bacteriophage (phage)-based microbial detection tool to detect and assess Salmonella viability. Salmonella Enteritidis cells in a VBNC physiological state were evaluated by cell culture, flow-cytometry and epifluorescence microscopy, and further assayed with a biosensor platform. Free PVP-SE1 phages in solution showed the ability to recognize VBNC cells, with no lysis induction, in contrast to the minor recognition of heat-killed cells. This ability was confirmed for immobilized phages on gold surfaces, where the phage detection signal follows the same trend of the concentration of viable plus VBNC cells in the sample. The phage probe was then tested in a magnetoresistive biosensor platform allowing the quantitative detection and discrimination of viable and VBNC cells from dead cells, with high sensitivity. Signals arising from 3 to 4 cells per sensor were recorded. In comparison to a polyclonal antibody that does not distinguish viable from dead cells, the phage selectivity in cell recognition minimizes false-negative and false-positive results often associated with most detection methods.
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