Quantitative PCR (qPCR) is one of the most common techniques for quantification of nucleic acid molecules in biological and environmental samples. Although the methodology is perceived to be ...relatively simple, there are a number of steps and reagents that require optimization and validation to ensure reproducible data that accurately reflect the biological question(s) being posed. This review article describes and illustrates the critical pitfalls and sources of error in qPCR experiments, along with a rigorous, stepwise process to minimize variability, time, and cost in generating reproducible, publication quality data every time. Finally, an approach to make an informed choice between qPCR and digital PCR technologies is described.
qPCR is more complex than perceived by many scientists.
The production of an amplification curve and an associated quantitative cycle value does not necessarily mean interpretable data.
The MIQE guidelines and associated methodology articles published thereafter, underline the ongoing drive to help scientists produce reproducible data from qPCR, culminating in a simple, stepwise methodology to ensure high-quality, reproducible data from qPCR experiments.
The concept of data normalization has led to the ongoing publication of articles solely focused on this subject for various sample types and experimental parameters.
The analysis of qPCR data can be challenging, especially as experiments grow in sample number and complexity of biological groups. A defined approach to qPCR data analysis is necessary to clarify gene expression analysis.
Low biomass in the bacterial lung tissue microbiome utilizes quantitative PCR (qPCR) 16S bacterial assays at their limit of detection. New technology like droplet digital PCR (ddPCR) could allow for ...higher sensitivity and accuracy of quantification. These attributes are needed if specific bacteria within the bacterial lung tissue microbiome are to be evaluated as potential contributors to diseases such as chronic obstructive pulmonary disease (COPD). We hypothesize that ddPCR is better at quantifying the total bacterial load in lung tissue versus qPCR.
Control (n = 16) and COPD GOLD 2 (n = 16) tissue samples were obtained from patients who underwent lung resection surgery, were cut on a cryotome, and sections were assigned for use in quantitative histology or for DNA extraction. qPCR and ddPCR were performed on these samples using primers spanning the V2 region on the 16S rRNA gene along with negative controls. Total 16S counts were compared between the two methods. Both methods were assessed for correlations with quantitative histology measurements of the tissue.
There was no difference in the average total 16S counts (P>0.05) between the two methods. However, the negative controls contained significantly lower counts in the ddPCR (0.55 ± 0.28 16S/uL) than in the qPCR assay (1.00 ± 0.70 16S copies) (P <0.05). The coefficient of variation was significantly lower for the ddPCR assay (0.18 ± 0.14) versus the qPCR assay (0.62 ± 0.29) (P<0.05).
Overall the ddPCR 16S assay performed better by reducing the background noise in 16S of the negative controls compared with 16S qPCR assay.
•Advanced chelate compound-based trace minerals (OTM) were evaluated in laying hens.•Replacement of inorganic minerals with 33% OTM supported laying performance.•66% and 100% OTM supplementation ...increased Se and Zn concentrations in yolk.•66% and 100% OTM supplementation improved lipid profiles and oxidative status.
The study aimed to determine the efficiency of advanced chelate compounds-based trace minerals (OTM) in laying hens. Laying hens (240, 32 weeks old) were assigned to one of the following five groups: NOTM (no added trace minerals), CONTM (standard mineral salts), and three experimental groups in which chelates were used to replace 33, 66, and 100% of mineral salts (OTM33, OTM66, and OTM100, respectively). Each treatment had six replicates with eight hens per replicate. After 18 weeks, performance and physicochemical properties of eggs in all experimental groups was better than those in the NOTM group. Among the treatments, OTM66 and OTM100 produced the best results in terms of laying performance, yolk PUFA/SFA ratio, Zn and Se contents, and malondialdehyde concentration in both serum and yolk. In conclusion, up to 66% OTM supplementation was beneficial for performance, lipid and mineral composition of yolk, and oxidative status.
Abstract One of the major challenges of customer-centric organizations is the recognition of customers, the distinction between different groups of customers and their ranking. Clustering is one of ...the data mining techniques used to group customers into their various characteristics. The main purpose of the research is to customer clustering based on the Recency, Frequency and Monetary indicators using the fuzzy c-means algorithm. The study was conducted on 76379 registered transactions from customers of Zahedan City Refah Chain Store. The results of this research provide a framework for developing customer relationship management programs for each customer group. Introduction Today, the importance of customer relationship management is not hidden from anyone and all service and product companies are trying to understand more of their customers. Understanding the various groups of customers and building effective relationships with them in a way that guarantees the economic benefits of companies in the future is an important issue in today's businesses. Maintaining valuable customers and attracting profitable customers is both important and it is possible to accurately identify their features. Clustering is one way that helps companies recognize their profitable customers. In the clustering of the elements within each cluster, the most similarities are found, and there is a significant difference between clusters. By introducing the fuzzy theory by Lotfi zadeh, the application of this idea in various sciences quickly expanded and the fuzzy clustering method was widely used by researchers in various fields (De Oliveira & Pedrycz, 2007). In fact, the main difference between the classic clustering and fuzzy clustering is that an instance can belong to more than one cluster (Khoshnazar, 2013). Companies with customer clustering and behavior analysis of each group will provide a platform for optimal allocation of resources and developing customer relationship management strategies. The customer lifetime value (CLV), reflects the value that can help companies in this field. Customer lifetime value is the value of the customer creates throughout his lifetime and is determined by using different models (Boroufar, Rezaeian & Shokohyar, 2017). The RFM model is one of the most popular and effective methods for analyzing customer life value. This model uses three variables Recency, Frequency, Monetary to express the difference between customers and the customer lifetime value is calculated from the sum of the values of the model's indexes. It is also assumed that customers who are worth a lot on any of the model's indicators are the most profitable customers. Of course, they will behave like they were in the future. Case study Rafah Chain Store Company is one of the most comprehensive distribution networks in Iran with the aim of supplying and distributing basic goods. Materials and methods In this research, transactions recorded in the database of the Zahedan Refah chain store have been used in a seven-month period. After receiving the data and performing the preparation process, 76,379 transactions were used as the final input. The preparation process consists of two steps. In the first step, the data was cleared, so some data with invalid values were identified and deleted. In the second stage, RFM model indices were calculated using SPSS Modeler 18 software. There is a difference in the RFM model index unit so these values should be normalized to the same unit. For this purpose, these values were normalized using the Min-Max method. To determine the number of clusters, the Xie and Beni index were used. After calculating the value of this index, 7 clusters were determined as the optimal number of clusters. Fuzzy C-means algorithm is used to cluster customers based on RFM model indicators. All stages of fuzzy clustering and determination of the number of clusters were done using MATLAB software. After fuzzy clustering is done, we will determine the weights of RFM model indices. For this purpose, Fuzzy AHP method was used. Finally, Customer lifetime value for each cluster was calculated from customers and clusters were ranked. Discussion and Conclusion By calculating the lifetime value for each cluster, companies can use their limited resources for a group of customers who have the highest value. According to the results, the fifth cluster with 0.16624 is the most valuable group of store customers. The services provided to this group should not be limited to regular programs, but should be tailor made for them. In fact, the store should allocate more funds to these customers. On the other hand, the third cluster with 0.01482 is the least valuable group of store customers. In developing customer relationship management strategies for this group, there should be a proper balance between the costs associated with the revenue that these customers receive from the store. The results of this research can be used to develop customer relationship management strategies for each customer cluster.
Thrombosis and pulmonary embolism appear to be major causes of mortality in hospitalized coronavirus disease 2019 (COVID-19) patients. However, few studies have focused on the incidence of venous ...thromboembolism (VTE) after hospitalization for COVID-19.
In this multi-center study, we followed 1529 COVID-19 patients for at least 45 days after hospital discharge, who underwent routine telephone follow-up. In case of signs or symptoms of pulmonary embolism (PE) or deep vein thrombosis (DVT), they were invited for an in-hospital visit with a pulmonologist. The primary outcome was symptomatic VTE within 45 days of hospital discharge.
Of 1529 COVID-19 patients discharged from hospital, a total of 228 (14.9%) reported potential signs or symptoms of PE or DVT and were seen for an in-hospital visit. Of these, 13 and 12 received Doppler ultrasounds or pulmonary CT angiography, respectively, of whom only one patient was diagnosed with symptomatic PE. Of 51 (3.3%) patients who died after discharge, two deaths were attributed to VTE corresponding to a 45-day cumulative rate of symptomatic VTE of 0.2% (95%CI 0.1%–0.6%; n = 3). There was no evidence of acute respiratory distress syndrome (ARDS) in these patients. Other deaths after hospital discharge included myocardial infarction (n = 13), heart failure (n = 9), and stroke (n = 9).
We did not observe a high rate of symptomatic VTE in COVID-19 patients after hospital discharge. Routine extended thromboprophylaxis after hospitalization for COVID-19 may not have a net clinical benefit. Randomized trials may be warranted.
•In a large multi-center study, the incidence of venous thromboembolism in COVID-19 patients followed for 45 days after hospitalization was relatively low.•Routine extended thromboprophylaxis in COVID-19 patients who have been discharged from the hospital may not have a net clinical benefit.•Older age, history of recent cancer, and history of recent diabetes were associated with higher risk of mortality after hospital discharge
Accurate identification, attracting, and keeping the customers particularly loyal Customer Relationship Management (CRM) with the goal of optimum allotment of resources and achievement to higher ...profit is not a competitive profit, but it is a life persistence necessity of companies in virtual space. One of the challenges of companies in this part is how to identify the customer’s traits and the separation of different segments of them. Now Customer Lifetime Value (CLV) is the comparison priority in the segmentation of customers to congruous segments. The main goal of this research is to identify key or strategic customers using the RFM model. In this part after determining the amount of Recency, Frequency and Monetary (RFM) in, registered transactions of one store in Iran (Refah Chain Store) at a time about seven months from 23 September 2017 to 20 April 2018 (71161 transactions as final inputs were used), the weight of each variable according to the fuzzy Analytic Hierarchy Process (AHP) was determined. At the next stage customers using the K-means and Two-step’s algorithms were clustered and K-means the method according to the Silhouette index was the better algorithm of this letter. According to the results, customers were segmented into three parts and CLV was calculated and for identifying key or strategic customer segmentation, the clustering process was repeated and priorities of all clusters were indicated. Results of data analysis are below: Segment 3: customers of this segment were 3425 members and 11.5% of all company customers were the most loyal customers those are identified as golden customer's segment and all of the variables were higher than average of all data. This research identified the valuable customers for the shop, and it gives them a chance to choose goal customers and invest in them.
ABSTRACT
Purpose
The efficacy of chemotherapy is decreased due to over-expression of the drug transporter P-glycoprotein (P-gp). This study was conducted to determine the feasibility of ...down-regulating tumor P-gp levels with non-viral siRNA delivery in order to sensitize the tumors to drug therapy.
Methods
P-gp over-expressing MDA435/LCC6 MDR1 cells were used to establish xenografts in NOD-SCID mouse. Cationic polymers polyethylenimine (PEI) and stearic acid-substituted poly-L-lysine (PLL-StA) were formulated with P-gp- specific siRNAs and delivered intratumorally to explore the feasibility of P-gp down-regulation in tumors. Intravenous Doxil™ was administered to investigate tumor growth.
Results
PEI and PLL-StA effectively delivered siRNA to MDA435/LCC6 MDR1 cells
in vitro
to reduce P-gp expression for 3 days. Intratumoral injection of siRNA with the carriers resulted in 60-80% and 20–32% of siRNA retention in tumors after 24 and 96 hr, respectively. This led to ~29.0% and ~61.5% P-gp down-regulation with PEI- and PLL-StA-mediated siRNA delivery, respectively. The P-gp down-regulation by intratumoral siRNA injection led to better response to systemic Doxil™ treatment, resulting in slowed tumor growth in originally doxorubicin-resistant tumors.
Conclusion
Effective P-gp down-regulation was feasible with polymeric siRNA delivery in a xenograft model, resulting in an enhanced response to the drug therapy.
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