997 resultados para Real-time qPCR


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We used a PCR method to quantify the loads of Chlamydia trachomatis organisms in self-collected urine and vulvovaginal swab (VVS) samples from 93 women and 30 men participating in the Chlamydia Screening Studies Project, a community-based study of individuals not seeking health care. For women, self-collected VVS had a higher mean chlamydial load (10,405 organisms/ml; 95% confidence interval [95% CI], 5,167 to 21,163 organisms/ml) than did first-void urines (FVU) (503 organisms/ml; 95% CI, 250 to 1,022 organisms/ml; P < 0.001). Chlamydial loads in female and male self-collected FVU specimens were similar (P = 0.634). The mean chlamydial load in FVU specimens decreased with increasing age in females and males. There was no strong statistical evidence of differences in chlamydial load in repeat male and female FVU specimens taken when patients attended for treatment a median of 23.5 (range, 14 to 62) and 28 (range, 13 to 132) days later, respectively, or in VVS taken a median of 35 (range, 14 to 217) days later. In this study, chlamydial load values for infected persons in the community who were not seeking treatment were lower than those published in other studies involving symptomatic patients attending clinical settings. This might have implications for estimates of the infectiousness of chlamydia. The results of this study provide a scientific rationale for preferring VVS to FVU specimens from women.

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The reliable quantification of gene copy number variations is a precondition for future investigations regarding their functional relevance. To date, there is no generally accepted gold standard method for copy number quantification, and methods in current use have given inconsistent results in selected cohorts. In this study, we compare two methods for copy number quantification. beta-defensin gene copy numbers were determined in parallel in 80 genomic DNA samples by real-time PCR and multiplex ligation-dependent probe amplification (MLPA). The pyrosequencing-based paralog ratio test (PPRT) was used as a standard of comparison in 79 out of 80 samples. Realtime PCR and MPLA results confirmed concordant DEFB4, DEFB103A, and DEFB104A copy numbers within samples. These two methods showed identical results in 32 out of 80 samples; 29 of these 32 samples comprised four or fewer copies. The coefficient of variation of MLPA is lower compared with PCR. In addition, the consistency between MLPA and PPRT is higher than either PCR/MLPA or PCR/PPRT consistency. In summary, these results suggest that MLPA is superior to real-time PCR in beta-defensin copy number quantification.

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Quantitative reverse transcriptase real-time PCR (QRT-PCR) is a robust method to quantitate RNA abundance. The procedure is highly sensitive and reproducible as long as the initial RNA is intact. However, breaks in the RNA due to chemical or enzymatic cleavage may reduce the number of RNA molecules that contain intact amplicons. As a consequence, the number of molecules available for amplification decreases. We determined the relation between RNA fragmentation and threshold values (Ct values) in subsequent QRT-PCR for four genes in an experimental model of intact and partially hydrolyzed RNA derived from a cell line and we describe the relation between RNA integrity, amplicon size and Ct values in this biologically homogenous system. We demonstrate that degradation-related shifts of Ct values can be compensated by calculating delta Ct values between test genes and the mean values of several control genes. These delta Ct values are less sensitive to fragmentation of the RNA and are unaffected by varying amounts of input RNA. The feasibility of the procedure was demonstrated by comparing Ct values from a larger panel of genes in intact and in partially degraded RNA. We compared Ct values from intact RNA derived from well-preserved tumor material and from fragmented RNA derived from formalin-fixed, paraffin-embedded (FFPE) samples of the same tumors. We demonstrate that the relative abundance of gene expression can be based on FFPE material even when the amount of RNA in the sample and the extent of fragmentation are not known.

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The assessment of ERa, PgR and HER2 status is routinely performed today to determine the endocrine responsiveness of breast cancer samples. Such determination is usually accomplished by means of immunohistochemistry and in case of HER2 amplification by means of fluorescent in situ hybridization (FISH). The analysis of these markers can be improved by simultaneous measurements using quantitative real-time PCR (Qrt-PCR). In this study we compared Qrt-PCR results for the assessment of mRNA levels of ERa, PgR, and the members of the human epidermal growth factor receptor family, HER1, HER2, HER3 and HER4. The results were obtained in two independent laboratories using two different methods, SYBR Green I and TaqMan probes, and different primers. By linear regression we demonstrated a good concordance for all six markers. The quantitative mRNA expression levels of ERa, PgR and HER2 also strongly correlated with the respective quantitative protein expression levels prospectively detected by EIA in both laboratories. In addition, HER2 mRNA expression levels correlated well with gene amplification detected by FISH in the same biopsies. Our results indicate that both Qrt-PCR methods were robust and sensitive tools for routine diagnostics and consistent with standard methodologies. The developed simultaneous assessment of several biomarkers is fast and labor effective and allows optimization of the clinical decision-making process in breast cancer tissue and/or core biopsies.

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OBJECTIVES: To investigate the contribution of a real-time PCR assay for the detection of Treponema pallidum in various biological specimens with the secondary objective of comparing its value according to HIV status. METHODS: Prospective cohort of incident syphilis cases from three Swiss hospitals (Geneva and Bern University Hospitals, Outpatient Clinic for Dermatology of Triemli, Zurich) diagnosed between January 2006 and September 2008. A case-control study was nested into the cohort. Biological specimens (blood, lesion swab or urine) were taken at diagnosis (as clinical information) and analysed by real-time PCR using the T pallidum 47 kDa gene. RESULTS: 126 specimens were collected from 74 patients with primary (n = 26), secondary (n = 40) and latent (n = 8) syphilis. Among primary syphilis, sensitivity was 80% in lesion swabs, 28% in whole blood, 55% in serum and 29% in urine, whereas among secondary syphilis, it was 20%, 36%, 47% and 44%, respectively. Among secondary syphilis, plasma and cerebrospinal fluid were also tested and provided a sensitivity of 100% and 50%, respectively. The global sensitivity of T pallidum by PCR (irrespective of the compartment tested) was 65% during primary, 53% during secondary and null during latent syphilis. No difference regarding serology or PCR results was observed among HIV-infected patients. Specificity was 100%. CONCLUSIONS: Syphilis PCR provides better sensitivity in lesion swabs from primary syphilis and displays only moderate sensitivity in blood from primary and secondary syphilis. HIV status did not modify the internal validity of PCR for the diagnosis of primary or secondary syphilis.

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This paper is focused on the integration of state-of-the-art technologies in the fields of telecommunications, simulation algorithms, and data mining in order to develop a Type 1 diabetes patient's semi to fully-automated monitoring and management system. The main components of the system are a glucose measurement device, an insulin delivery system (insulin injection or insulin pumps), a mobile phone for the GPRS network, and a PDA or laptop for the Internet. In the medical environment, appropriate infrastructure for storage, analysis and visualizing of patients' data has been implemented to facilitate treatment design by health care experts.

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In this paper, a simulation model of glucose-insulin metabolism for Type 1 diabetes patients is presented. The proposed system is based on the combination of Compartmental Models (CMs) and artificial Neural Networks (NNs). This model aims at the development of an accurate system, in order to assist Type 1 diabetes patients to handle their blood glucose profile and recognize dangerous metabolic states. Data from a Type 1 diabetes patient, stored in a database, have been used as input to the hybrid system. The data contain information about measured blood glucose levels, insulin intake, and description of food intake, along with the corresponding time. The data are passed to three separate CMs, which produce estimations about (i) the effect of Short Acting (SA) insulin intake on blood insulin concentration, (ii) the effect of Intermediate Acting (IA) insulin intake on blood insulin concentration, and (iii) the effect of carbohydrate intake on blood glucose absorption from the gut. The outputs of the three CMs are passed to a Recurrent NN (RNN) in order to predict subsequent blood glucose levels. The RNN is trained with the Real Time Recurrent Learning (RTRL) algorithm. The resulted blood glucose predictions are promising for the use of the proposed model for blood glucose level estimation for Type 1 diabetes patients.