Serum HER2 Level Measured by Dot Blot: A Valid and Inexpensive Assay for Monitoring Breast Cancer Progression

Human epidermal growth factor receptor 2 (HER2) is one of the most important prognostic and predictive factors for breast cancer patients. Recently, serum HER2 ECD level of patients detected by enzyme-linked immunoabsorbent assay (ELISA) has been shown to predict tumor HER2 status and reveal its association with tumor progression, recurrence and poor prognosis. In this study, we established a new method, dot blot assay, to measure the serum HER2 level in breast cancer patients and further to evaluate the clinical value for monitoring breast cancer progression. We found that the serum HER2 level measured by dot blot assay was significantly correlated with tissue HER2 status in breast cancer patients (P = 0.001), and also significantly correlated with HER2 level measured by ELISA (P = 1.06×10−11). Compared with ELISA method, the specificity and sensitivity of dot blot assay were 95.3% and 65.0%, respectively. The serum HER2 levels of patients with grade III or ER-negative were higher than those with grade I–II (P = 0.004) and ER-positive (P = 0.033), respectively. Therefore, the novel dot blot method to detect serum HER2 level is a valid and inexpensive assay with potential application in monitoring breast cancer progression in clinical situations

Information integration of diverse laboratory data sources using information supply chains

Information integration in disciplines such as drug discovery poses major challenges, as often only data from unstructured or semi-structured information sources is available and is in many cases neither semantically consistent nor in a form which can be easily extracted and shared. One of the problems with current frameworks is their inability to cope with diversity of sources and changing information types so that they may be seen as constraint rather than support. Therefore there is evidence of growing demand for information integration and information sharing in drug discovery laboratories. In this paper, we present an approach for integration of experimental data in highly diverse, semi-structured scientific domains and promoting collaboration by supplying information at the right time in the right context to the right users. The paper describes how subject-oriented specific Information Supply Chains (ISC) can be established in a flexible manner to integrate required information, thereby balancing information supply and demand. We introduce the concept of ISC using a model based on Research Description Framework (RDF) and demonstrate using a case study approach how ISCs can help the decision process by making required information available at the time needed

Information integration of drug discovery and clinical studies to support complex queries using an information supply chain framework

Query support of frameworks which are capable of linking and integrating trusted information in disciplines such as clinical studies face many challenges, as usually data are neither semantically consistent nor in a form which can be easily extracted and shared.. Drawing conclusions from clinical studies usually requires integrating data from various sources at various time points and inferring facts from an integrated information set. However, relevant information may not be present at all or not in sufficient detail or quality to support a particular query. In this paper, we present an approach for query support in integrating diverse information sources and how trust can be strengthened in using subject oriented specific Information Supply Chains (ISC). We assess feasibility of query support in an ISC framework using an underlying Information Dependency Relation Model for relevant information sources and how such a framework may facilitate collaboration in supplying information at the right time and with required quality

Trusted information supply chain framework in clinical studies

Query support of frameworks which are capable of integrating trusted information in disciplines such as clinical studies face many challenges, as usually data are neither semantically consistent nor in a form which can be easily extracted and shared. Drawing conclusions from clinical studies usually requires integrating data from various sources and inferring facts from an integrated information set. However, relevant information may not be present in sufficient detail to support a particular query. In this paper, we present an approach for query support in integrating diverse information sources and how trust can be strengthened in using subject oriented specific Information Supply Chains (ISC). We assess feasibility of query support in an ISC framework using an underlying Information Dependency Relation Model for relevant information sources

A software based solution to facilitate end to end information supply chain visibility

Even though the benefits of supply chain visibility and the consequences of the lack of it have been thoroughly evaluated, the path towards achieving it appears to be murky due to the factors such as unsupportive IT infrastructure, lack of integration mechanisms and the lack of information sharing strategies. This paper introduces a software solution which is developed to overcome this challenge. It promotes process based internal and external integration and produces graphical visualization of the seamless information flow among the processes which enables real time performance monitoring and measurement. The paper takes a two dimensional approach, by means of a critical review of the relevant literature and a case study, to justify the relevance and importance of addressing the challenge of visibility and subsequently to evaluate the success of the solution

Performance evaluation of Levenberg-Marquardt technique in error reduction for diabetes condition classification

This paper aims to provide a case study to classify diabetes medical condition amongst patients. The study examines the performance of the Levenberg-Marquardt (LM) algorithm on a single dataset, the Pima Indian Diabetes dataset, attempting to minimize error in classifying the patients as diabetes positive or negative. The learning algorithm is applied on dynamically constructed neural network to minimize the error by continuously training the network until the optimum efficiency level is obtained. The performance of the approach is verified by performing a comparison study. The comparison study involves testing of the dynamically constructed network and presents a critical analysis of the classification output. The performance of the network is measured in terms of sensitivity and specificity for different learning algorithms. The study reveals that the LM algorithm outperforms other techniques in these tests and consequently concludes it to be the best ANN learning rule in providing optimum output results when applied to a dynamically constructed neural network