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Published in Ronald Fayer, Lihua Xiao, Cryptosporidium and Cryptosporidiosis, 2007
Beginning with the development of the first PCR assay for the diagnosis of Cryptosporidium in stool specimens (Laxer et al., 1991), many PCR techniques have been described for the detection of Cryptosporidium oocysts in clinical and environmental samples (Egyed et al., 2003; Smith et al., 2006b). The use of molecular techniques in the diagnosis of cryptosporidiosis, however, became popular only after the incorporation of genotyping capabilities. Since the description of the first PCR-based tool for the differentiation of C. parvum and C. hominis (Morgan et al., 1995), many genotyping tools have been developed for the characterization of Cryptosporidium epidemiology. The PCR primers are based on various antigenic, structural, housekeeping genes and unknown genomic fragments, and include various formats of detection and differentiation, including single-round and nested PCR, random amplified polymorphic DNA PCR (RAPD-PCR), arbitrary primed PCR (AP-PCR), reverse transcription PCR (RT-PCR), real-time PCR, followed by restriction fragment length polymorphism (RFLP) analysis, singlestrand conformation polymorphism (SSCP) analysis, melting curve analysis, enzyme-linked immunosorbent assay (ELISA), microarray, or DNA sequencing (Egyed et al., 2003). With few exceptions, most of these techniques can efficiently differentiate C. parvum and C. hominis in stool samples, and have played a major role in understanding the transmission of human Cryptosporidium infections (Peng et al., 1997; McLauchlin et al., 2000). Their ability to detect and differentiate other Cryptosporidium species that may infect humans is largely unknown. Most of them can probably amplify DNA from C. meleagridis, but are unlikely to amplify some of the more divergent members (such as C. canis, C. felis, C. muris, and C. andersoni) of Cryptosporidium species because of the nature of most targets used by these techniques (Sulaiman et al., 1999; Jiang and Xiao, 2003; Muthusamy et al., 2006). Therefore, the use of these first-generation genotyping tools has decreased significantly. One such target, the Cryptosporidium oocyst wall protein (COWP), is still used by many researchers as a primary or confirmative genotyping method, largely because of the robustness of the technique (Pedraza-Diaz et al., 2001b; Feltus et al., 2006; Goncalves et al., 2006; Nichols et al., 2006; Soba et al., 2006; Trotz-Williams et al., 2006).
Serum miRNA-146a and vitamin D values in chronic renal ailment with and without comorbid cardiovascular disease
Published in Egyptian Journal of Basic and Applied Sciences, 2023
Fatma K. A. Hamid, Alshaymaa M. Alhabibi, Mona A. Mohamed, Hanaa Hussein El-Sayed, Nehad Rafaat Ibrahim, Ghadir Mohamed Hassan Elsawy, Entsar M. Ahmad
Circulating miR-146a was detected by reverse transcriptase polymerase chain reaction (RT-PCR), where plasma samples were used to extract RNA using miRNeasy Mini Kits (cat. no. 217004, QIAGEN, Germany). The extracted RNA was then reverse transcribed into complementary DNA using miScript II RT kits (cat. no. 218161, QIAGEN, Germany) according to the manufacturer’s instructions. Human MI script SYBER green Master Mix, primers for miR-146a-5p (cat. no. MS00003535, Lot no. 20160510009s) and SNORD 68 (a housekeeping miR utilized as an important marker; cat. no. MS00033712, Lot no. 201601113022s, QIAGEN, Germany) were used in the quantification of miR-146a by q-PCR. The following PCR cycling conditions were used, including a denaturation phase lasting 15 s at 94°C. After an initiating active reaction at 95°C), annealing at 55°C for 30 s, and then extension at 70°C for 30 s. After analyzing the thermal profile, a melting curve analysis with the temperature gradually increased between 65°C and 95°C was carried out outside with a fluorescence monitor to ensure amplification of the transcript of interest. A single sharp peak corresponding to the target transcript was observed on melting curve analysis. Cycle threshold (CT) values were computed by deducting target miRs CT values from SNORD 68 CT values. By deducting the average normalized CT of the control from all other CT values, including control values, the resulting standardized CT values were used to manually compute CT. The two CT approach was used to calculate the relative expression of each miR (fold-change in expression). Up-regulation was determined by a fold-change value greater than 1. All miR transcripts were up-regulated in the present study.