Development and application of an ELISA kit for the detection of milk progesterone in dairy cows

Steroid Hormone Exposure as a Potential Hazard in Milk Consumers: A Significant Health Challenge in Iran

The presence of steroid hormones in milk is inevitable, and they can be considered as potential carcinogenic agents for consumers. The purpose of this study was to evaluate the level of 17β-estradiol, progesterone, and hydroxyprogesterone in sixty-nine bovine milk samples, collected from April to September 2020, in Hamadan, Iran. The milk samples were analyzed using enzyme-linked immunosorbent assay (ELISA). In this study, the mean contents of 17β-estradiol, progesterone, and hydroxyprogesterone in the milk samples were determined to be 330.5 ± 190.2 pg/ml, 3.57 ± 2.47 ng/ml, and 1.54 ± 0.41 ng/ml, respectively. However, the content of these steroid hormones in milk samples could be considered safe in children and adults, if the milk consumption is assumed to be in the reported range (175–240 ml/daily). Due to the effects of steroid hormones, especially 17β-estradiol, in the etiology of various cancers, regular monitoring of these hormones is recommended in milk and its dairy products in Iran.

GryphSens: A Smartphone-Based Portable Diagnostic Reader for the Rapid Detection of Progesterone in Milk

Enzyme-linked immunosorbent assay (ELISA) is a popular assay technique for the detection and quantification of various biological substances due its high sensitivity and specificity. More often, it requires large and expensive laboratory instruments, which makes it difficult to conduct when the tests must be performed quickly at the point-of-care (POC). To increase portability and ease of use, we propose a portable diagnostic system based on a Raspberry Pi imaging sensor for the rapid detection of progesterone in milk samples. We designed, assembled, and tested a standalone portable diagnostic reader and validated it for progesterone detection against a standard ELISA assay using a commercial plate reader. The portable POC device yielded consistent results, regardless of differences in the cameras and flashlights between various smartphone devices. An Android application was built to provide front-end access to users, control the diagnostic reader, and display and store the progesterone measurement on the smartphone. The diagnostic reader takes images of the samples, reads the pixel values, processes the results, and presents the results on the handheld device. The proposed POC reader can perform to superior levels of performance as a plate reader, while adding the desirable qualities of portability and ease of use.

Competitive inhibition assay for the detection of progesterone in dairy milk using a fiber optic SPR biosensor

Analytical methods that are often used for the quantification of progesterone in bovine milk include immunoassays and chromatographic techniques. Depending on the selected method, the main disadvantages are the cost, time-to-result, labor intensity and usability as an automated at-line device. This paper reports for the first time on a robust and practical method to quantify small molecules, such as progesterone, in complex biological samples using an automated fiber optic surface plasmon resonance (FO-SPR) biosensor. A FO-SPR competitive inhibition assay was developed to determine biologically relevant concentrations of progesterone in bovine milk (1-10 ng/mL), after optimizing the immobilization of progesterone-bovine serum albumin (P4-BSA) conjugate, the specific detection with anti-progesterone antibody and the signal amplification with goat anti-mouse gold nanoparticles (GAM-Au NPs). The progesterone was detected in a bovine milk sample with minimal sample preparation, namely ½ dilution of the sample. Furthermore, the developed bioassay was benchmarked against a commercially available ELISA, showing excellent agreement (R² = 0.95). Therefore, it is concluded that the automated FO-SPR platform can combine the advantages of the different existing methods for quantification of progesterone: sensitivity, accuracy, cost, time-to-result and ease-of-use.