An assay for the enzyme N-acetyl-Î²-d-glucosaminidase (NAGase) based on electrochemical detection using screen-printed carbon electrodes (SPCEs)

Pemberton, R. M.; Hart, John P.; Mottram, Toby

doi:10.1039/b104874k

Cited by 30 publications

(21 citation statements)

References 28 publications

(32 reference statements)

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“…Enzymes Pemberton et al (2001) reported laboratory experiments with SPCE, having a square working area of 9 mm were printed from D14 ink formulation (Gwent Electronic Materials Ltd, Gwent, Wales) onto a PVC base. Automatic On-Line Analysis Of Milk Constituents Using Biosensors Electrochemical measurements were made under hydrodynamic conditions using a three-electrode system contained within a modi¢ed cylindrical oxygen cell and using an analyte volume of 2 ml.…”

Section: Ionic Concentrationmentioning

confidence: 99%

Automatic On-Line Analysis Of Milk Constituents (Urea, Ketones, Enzymes And Hormones) Using Biosensors

Mottram

Velasco-Garcia

Berry

et al. 2002

Comparative Clinical Pathology

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Traditional methods of monitoring health changes in animals are based entirely on the human senses. However, in modern dairy production systems humans are rarely present, this is particularly the case with the introduction of robotic milking. In these systems all the functions of milking are automated and cows visit at times of their own choosing. Systems of automatic health monitoring are therefore a priority for research to ensure that the health and reproductive status of the animals can be assessed for management purposes. These systems must be automatic, work in ¢eld conditions without technical support and cost a few pence per analysis. The ¢rst task is to obtain representative biological samples automatically and non-invasively. As milk is £owing into the milking machine from the cow this can be achieved with ease, except that milk is non-homogeneous with a changing lipid fraction during milking. Lipid soluble components such as progesterone and vitamin A are a¡ected by this change and a model has to be established to determine thresholds at di¡erent times during milking. Our main interests in dairy cows are in predicting ovulation, detecting metabolic imbalance and detecting preclinical mastitis in£am-matory response. Our team is developing a fully automated ovulation prediction system based on the screen-printed carbon electrode biosensor for progesterone demonstrated by Pemberton et al. (1998). In recent experiments the automated system was able to detect concentrations of progesterone between 2 and 30 ng/ml in stored milk samples (r 2 = 0.96). The results of ¢eld tests are presented showing a good correlation between ELISA and the biosensor (r 2 = 0.91) on samples of fresh milk. The results of the recent ¢eld tests show the ability of the biosensor to characterise ovulation cycles of cows and to detect pregnancy. We have identi¢ed a major lack of other biological models to detect disease with on-line sensors. Our next objective is to create an integrated system for biological research with sensor systems for urea, ketones, lipids and enzymes in milk. This will allow the development of diagnostic models based on analysing numerical sensor-derived data rather than human visual observations for signs of ill health in dairy cows.

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Section: Ionic Concentrationmentioning

confidence: 99%

Automatic On-Line Analysis Of Milk Constituents (Urea, Ketones, Enzymes And Hormones) Using Biosensors

Mottram

Velasco-Garcia

Berry

et al. 2002

Comparative Clinical Pathology

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“…Those that do exist mostly automate or augment manual serological tests for a single disease; examples include "electronic noses" for detecting bovine tuberculosis [11] or ketosis [12]; a chemical sensor to detect mastitis from milk samples [13]; and detection of ovulation in dairy cows using chemical sensors in milk samples [14] or via transrectal ultrasonography [15]. There are no sensor systems that respond to a wide variety of diseases in the literature on cattle production; this, however, is precisely what the feedlot operator needs.…”

Section: A Livestock Disease Managementmentioning

confidence: 99%

Learning to Detect the Onset of Disease in Cattle from Feedlot Watering Behavior

Dick

Bracho

2007

NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society

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Intensive farming practices in North America create ideal conditions for disease outbreaks in cattle feedlots. A disease outbreak causes economic loss to the farmer, through the reduced weight gain of sick cattle and the spread of the infection to other cattle. Livestock disease management is a loss-reduction process, where sick cattle are quarantined and treated as early as possible. Disease detection today, however, still relies on behavioral observation and weekly checkups. There is an obvious opportunity for a real-time sensor platform, coupled with intelligent sensor fusion, to significantly improve on the current system. We report on preliminary experiments in developing just such a sensor-fusion system, focusing on a sensor to detect water intake. ROC analysis shows that this sensor does predict disease in cattle. However, a significant skewness in the observed data does degrade the classifier's performance, even when cost-sensitive classification is used.Index Terms -Data mining, time series classification, machine learning, livestock disease management, learning from imbalanced data.

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“…For instance, oxido/reduction process of 1-naphthol, which is a substrate of NAGase can be easily detected and quantified using carbon electrodes. Pamberton et al [ 67 ] have reported an electrochemical sensor based on a screen-printed carbon electrode which detected NAGase protein in milk samples with a limit of detection of 10 mU/mL. Surface plasmon resonance was employed to monitor binding of haemoglobin to haptoglobin immobilized on the surface of the chip [ 68 ].…”

Section: Detection Of Mycoplasma and Other Bovine mentioning

confidence: 99%

Advanced biosensors for detection of pathogens related to livestock and poultry

Vidić

Manzano

Chang

et al. 2017

Vet Res

200

124

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Infectious animal diseases caused by pathogenic microorganisms such as bacteria and viruses threaten the health and well-being of wildlife, livestock, and human populations, limit productivity and increase significantly economic losses to each sector. The pathogen detection is an important step for the diagnostics, successful treatment of animal infection diseases and control management in farms and field conditions. Current techniques employed to diagnose pathogens in livestock and poultry include classical plate-based methods and conventional biochemical methods as enzyme-linked immunosorbent assays (ELISA). These methods are time-consuming and frequently incapable to distinguish between low and highly pathogenic strains. Molecular techniques such as polymerase chain reaction (PCR) and real time PCR (RT-PCR) have also been proposed to be used to diagnose and identify relevant infectious disease in animals. However these DNA-based methodologies need isolated genetic materials and sophisticated instruments, being not suitable for in field analysis. Consequently, there is strong interest for developing new swift point-of-care biosensing systems for early detection of animal diseases with high sensitivity and specificity. In this review, we provide an overview of the innovative biosensing systems that can be applied for livestock pathogen detection. Different sensing strategies based on DNA receptors, glycan, aptamers and antibodies are presented. Besides devices still at development level some are validated according to standards of the World Organization for Animal Health and are commercially available. Especially, paper-based platforms proposed as an affordable, rapid and easy to perform sensing systems for implementation in field condition are included in this review.

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An assay for the enzyme N-acetyl-Î²-d-glucosaminidase (NAGase) based on electrochemical detection using screen-printed carbon electrodes (SPCEs)

Cited by 30 publications

References 28 publications

Automatic On-Line Analysis Of Milk Constituents (Urea, Ketones, Enzymes And Hormones) Using Biosensors

Automatic On-Line Analysis Of Milk Constituents (Urea, Ketones, Enzymes And Hormones) Using Biosensors

Learning to Detect the Onset of Disease in Cattle from Feedlot Watering Behavior

Advanced biosensors for detection of pathogens related to livestock and poultry

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