A Method for the Detection of Cryptosporidium parvum Oocysts in Milk Based on Microfiltration and Real-Time Polymerase Chain Reaction

Comparison of ordinary reverse transcription real-time polymerase chain reaction (qRT-PCR) with a newly developed one-step single-tube nested real-time RT-PCR (OSN-qRT-PCR) for sensitive detection of SARS-CoV-2 in wastewater

Wastewater monitoring has proven to be an important approach to detecting and controlling the development of the SARS-CoV-2 pandemic. Various tests based on reverse transcription real-time PCR (qRT-PCR) have been developed and used for the detection of SARS-CoV-2 in wastewater samples. In this study, we attempted to increase the sensitivity of qRT-PCR by developing a one-step single-tube nested qRT-PCR assay (OSN-qRT-PCR). Two variants were developed, oriented to nucleocapsid phosphoprotein gene (N) and to spike protein gene (S), respectively. The performance of conventional qRT-PCR assays oriented to these genes with two novel OSN-qRT-PCR assays were firstly optimized using wastewater artificially contaminated with two encapsidated RNA mimic systems harboring a portion either N or S gene (ENRM and ESRM, respectively). The assays were coupled to a polyethylene glycol-based RNA precipitation/extraction method and applied to detect SARS-CoV-2 in wastewater samples from four cities in Slovakia. Both novel OSN-qRT-PCR assays demonstrated higher detection rates than the ordinary qRT-PCR counterparts. The virus levels in the analyzed wastewater samples had a high or very high relation with the numbers of clinical cases in the monitored regions. In fact, correlation with a 3-, 4-, or 5-day temporal offset was revealed. The OSN-qRT-PCR assays demonstrated robustness, mainly in samples with low viral loads.

An overview of methods/techniques for the detection of Cryptosporidium in food samples

Cryptosporidium is one of the most important parasitic protozoa of concern within the food production industry, worldwide. This review describes the evolution and its development, and it monitors the methodology that has been used for Cryptosporidium in food material since 1984, when the first publication appeared regarding the detection of Cryptosporidium parvum in food materials. The methods that are currently being used for the detection of Cryptosporidium oocysts in food material (mainly vegetables) and all of the other available published methods are discussed in this review. Generating more consistent and reliable data should lead to a better understanding of the occurrence, transport and fate of the oocysts in food material. Improvements in monitoring and developing effective methodology, along with food security, offer more practical possibilities for both the developed and developing worlds.

Assessment of an extraction protocol to detect the major mastitis-causing pathogens in bovine milk

Despite all efforts to control its spread, mastitis remains the most costly disease for dairy farmers worldwide. One key component of better control of this disease is identification of the causative bacterial agent during udder infections in cows. Mastitis is complex, however, given the diversity of pathogens that must be identified. Development of a rapid and efficient bacterial species identification tool is thus necessary. This study was conducted to demonstrate the feasibility of bacterial DNA extraction for the automated molecular detection of major mastitis-causing pathogens directly in milk samples to complement traditional microbiological identification. Extraction and detection procedures were designed and optimized to achieve detection in a respectable time frame, at a reasonable cost, and with a high throughput capacity. The following species were identified: Staphylococcus aureus, Escherichia coli, Streptococcus uberis, Streptococcus agalactiae, Streptococcus dysgalactiae, and Klebsiella spp. (including Klebsiella oxytoca and Klebsiella pneumoniae). The detection procedure includes specific genomic DNA amplification by multiplex PCR for each species, separation by capillary electrophoresis, and laser-assisted automated detection. The specificity of the primers was assessed with a panel of bacteria representing mastitis-negative control species. The extraction protocol comprised multiple steps, starting with centrifugation for fat removal, followed by heating in the presence of a cation exchange resin to trap divalent ions. The analytical sensitivity was 100 cfu/mL for milk samples spiked with Staph. aureus, Strep. dysgalactiae, and E. coli, with a tendency for K. pneumoniae. The detection limit was 500 cfu/mL for Strep. uberis and Strep. agalactiae. The overall diagnostic sensitivity (95.4%) and specificity (97.3%) were determined in a double-blind randomized assay by processing 172 clinical milk samples with microbiological characterization as the gold standard. When the physical nature of the milk samples was too altered, DNA purification with a magnetic bead-based system was used. Of the apparent false-positive samples, 5 were identified by specific microbiological analysis as true-positive Staph. aureus co-infections, with further confirmation by ribosomal 16S sequencing. The proposed methodology could, therefore, become an interesting tool for automated PCR detection of major mastitis pathogens in dairy cattle.