Synthesis of Carboxymethyl Starch for increasing drilling mud quality in drilling oil and gas wells

This paper describes the impact of carboxymethyl starch preparation conditions on physicochemical properties of polysaccharide reagent, widely used as fluid loss reducing agent in drilling mud. Variation of the main parameters of carboxymethylation is researched in the experiment. The following conditions such as temperature and reaction time, amount of water, as well as ratio of NaOH to monochloracetic acid define the characteristics of carboxymethyl starch. The degree of substitution is defined for polysaccharides, as well as the characteristics of samples have been studied by infrared spectroscopy. Rheological characteristics and fluid loss indicator have been investigated to study the impact of the reagents on drilling mud quality

Selecting the Best Using Data Envelopment Analysis

One of the most important strengths of Data Envelopment Analysis, (DEA), is that it allows almost complete freedom in the way that each decision making unit, (DMU), evaluates itself relative to its peers. This tends to result in many DMUs receiving a high efficiency score. Particularly when DEA is applied in a decision making context, it may be desirable to select a single option rather than determining the set of efficient alternatives in ranking efficient DMU or to Assist selecting a best DMU. Several extensions to DEA have been proposed and used. This paper examines, compares, and integrates a variety of these methods. A less complicated application area is used to investigate the subtleties of DEA cross-efficiency

Pancreatic ductal adenocarcinoma and chronic pancreatitis may be diagnosed by exhaled-breath profiles:a multicenter pilot study

Background: The diagnosis of pancreatic adenocarcinoma and chronic pancreatitis often rely on expensive and invasive diagnostic approaches, which are not always discriminative since patients with chronic pancreatitis and pancreatic adenocarcinoma may present with similar symptoms. Volatile organic compounds (VOCs) in expired breath, could be used as a non-invasive diagnostic biological marker for detection of pancreatic pathology. Detection and discrimination of pancreatic pathology with an electronic nose has not yet been reported. Purpose: The objective of this pilot study was to determine the diagnostic potential of an electronic nose to identify pancreatic adenocarcinoma and chronic pancreatitis by analyzing volatile organic compoundg (VOC) profiles in exhaled air. Patients and methods: In a multicenter study, the exhaled air of 56 chronic pancreatitis patients, 29 pancreatic adenocarcinoma patients, and 74 disease controls were analyzed using an electronic nose based on 3 metal oxide sensors (MOS). The measurements were evaluated utilizing an artificial neural network. Results: VOC profiles of chronic pancreatitis patients could be discriminated from disease controls with an accuracy of 0.87 (AUC 0.95, sensitivity 80%, specificity 92%). Also, VOC profiles of patients with pancreatic adenocarcinoma differed from disease controls with an accuracy of 0.83 (AUC 0.87, sensitivity 83%, specificity 82%). Discrimination between chronic pancreatitis and pancreatic adenocarcinoma showed an accuracy of 0.75 (AUC 0.83, sensitivity 83%, specificity 71%). Conclusion: An electronic nose may be a valuable diagnostic tool in diagnosis of pancreatic adenocarcinoma and chronic pancreatitis. The current study shows the potential of an electronic nose for discriminating between chronic pancreatitis, pancreatic adenocarcinoma and healthy controls. The results from this proof-of-concept study warrant external validation in larger cohorts

A new approach based on artificial neural networks for high order multivariate fuzzy time series

Fuzzy time series methods have been recently becoming very popular in forecasting. These methods can be categorized into two subclasses that are univariate and multivariate approaches. It is a known fact that real time series data can actually be affected by many factors. In this case, the using multivariate fuzzy time series forecasting model can be more reasonable in order to get more accurate forecasts. To obtain fuzzy forecasts when multivariate fuzzy time series approach is adopted, the most applied method is using tables of fuzzy relations. However, employing this method is a computationally though task. In this study, we introduce a new method that does not require using fuzzy logic relation tables in order to determine fuzzy relationships. Instead, a feed forward artificial neural network is employed to determine fuzzy relationships. The proposed method is applied to the time series data of the total number of annual car road accidents casualties in Belgium from 1974 to 2004 and a comparison is made between our proposed method and the methods proposed by Jilani and Burney [Jilani, T. A., & Burney, S. M. A. (2008). Multivariate stochastic fuzzy forecasting models. Expert Systems with Applications, 35, 691-700] and Lee et al. [Lee, L.-W., Wang, L.-H., Chen, S.-M., & Leu, Y.-H. (2006). Handling forecasting problems based on two factors high order fuzzy time series. IEEE Transactions on Fuzzy Systems, 14, 468-477]. (C) 2009 Elsevier Ltd. All rights reserved

Investigating the Correlation Between Long-Term Response in Patients with Metastatic HER2+ Breast Cancer and the Activity of Regulatory T Cells: A Retrospective Study

Mustafa Degirmenci,1 Gulden Diniz,2 Dudu Solakoglu Kahraman,3 Mustafa Sahbazlar,4 Lokman Koral,5 Umut Varol,6 Ruchan Uslu7 1Department of Medical Oncology, Health Sciences University, Izmir, Turkey; 2Department of Pathology, Izmir Democracy University, Izmir, Turkey; 3Department of Pathology, University of Health Sciences, Izmir, Turkey; 4Department of Medical Oncology, Celal Bayar University, Manisa, Turkey; 5Department of Medical Oncology, Canakkale Onsekiz Mart University, Canakkale, Turkey; 6Department of Medical Oncology, Izmir Democracy university, Izmir, Turkey; 7Department of Medical Oncology, izmir Medicana Hospital, Izmir, TurkeyCorrespondence: Mustafa Degirmenci, Medical Oncology Department of Health Sciences University, Kazım Dirik Mah. Fatih Sultan Mehmet Cad. No: 29/1 Seyhan Sitesi C blok D:10 35040, Bornova, Izmir, Turkey, Email [email protected]: Trastuzumab is commonly utilized in the management of metastatic HER2-positive breast cancer. Our main goal was to examine the clinical outcomes and immune markers of patients who received trastuzumab and chemotherapy treatment.Methods: Between 1995 and 2012, a total of 98 patients diagnosed with metastatic HER2-positive breast cancer were retrospectively analyzed at Ege University Hospital and Tepecik Training and Research Hospital. The clinicopathological characteristics and clinical outcomes of the patients were assessed, and the associations between response rates, survival and the immune profiles of tumor infiltrating lymphocytes were statistically evaluated.Results: The average age of patients at the time of diagnosis was 50.1± 10.3 (ranging from 30 to 79) years. The mean follow-up period for all patients was 97.9± 53.8 months. Among the patients, complete response was observed in 24.5%, partial response in 61.2%, and stable disease in 8.2% of cases. The average progression-free survival was 50.3± 26.9 months (ranging from 1 to 163 months), and the average overall survival was 88.8± 59.4 months (ranging from 12 to 272 months). After analyzing all cases, it was found that patients who were younger (p=0.006), exhibited higher CD3-positivity (p=0.041), presented with higher FOXP3-positivity (p=0.025), showed complete or at least partial response to treatment (p=0.008), and experienced a long-term response to trastuzumab (and chemotherapy) treatment had longer survival (p=0.001).Conclusion: Patients with HER2-positive breast cancer, who initially respond positively to palliative trastuzumab and chemotherapy treatment, can achieve long-term tumor remission lasting for several years.Keywords: metastatic breast cancer, trastuzumab, forkhead box p3, long-term respons

Use of comparative data for integrated cancer services

Background: Comparative data are an important resource for management of integrated care. In 2001, the English Department of Health created 34 cancer networks, broadly serving populations of half to three million people, to coordinate cancer services across providers. We have investigated how national and regional routine data are used by the cancer network management teams.Methods: Telephone interviews using a standardised semi-structured questionnaire were conducted with 68 participants in 29 cancer network teams. Replies were analysed both quantitatively and qualitatively.Results: While most network teams had a formal information strategy, data were used ad hoc more than regularly, and were not thought to be as influential in network decision making as other sources of information. Data collection was more prominent in information strategies than data use. Perceptions of data usefulness were mixed and there were worries over data quality, relevance, and potential misuse. Participants were receptive to the idea of a new limited dataset collating comparative data from currently available routine data sources. Few network structural factors were associated with data use, perceptions of current data, or receptivity to a new dataset.Conclusion: Comparative data are underused for managing integrated cancer services in England. Managers would welcome more comparative data, but also desired data to be relevant, quality assured and contextualised, and for the teams to be better resourced for data use

Finite-Element Modelling of Biotransistors

Current research efforts in biosensor design attempt to integrate biochemical assays with semiconductor substrates and microfluidic assemblies to realize fully integrated lab-on-chip devices. The DNA biotransistor (BioFET) is an example of such a device. The process of chemical modification of the FET and attachment of linker and probe molecules is a statistical process that can result in variations in the sensed signal between different BioFET cells in an array. In order to quantify these and other variations and assess their importance in the design, complete physical simulation of the device is necessary. Here, we perform a mean-field finite-element modelling of a short channel, two-dimensional BioFET device. We compare the results of this model with one-dimensional calculation results to show important differences, illustrating the importance of the molecular structure, placement and conformation of DNA in determining the output signal