Rapid determination of indapamide in human urine using novel low-density solvent based ultrasound assisted emulsification microextraction coupled with high performance liquid chromatography-variable wavelength detection

Indapamide belongs to the class of thiazide-type diuretic drugs and is widely used in the treatment of hypertension and nephrolithiasis. In this work, a simple, rapid and efficient low density solvent (LDS) based ultrasound assisted emulsification microextraction (USAEME) method combined with high performance liquid chromatography-variable wavelength detection (HPLC-VWD) was investigated for the determination of a popular drug of abuse, indapamide, in human urine samples. The target compound was extracted from acidified sample solution with a few microliter amount of LDS by a USAEME method. The influence of several important experimental variables such as selection of the extraction solvent and its volume, ultrasonication time, pH and ionic strength were thoroughly examined and optimized. Under optimal conditions, the calibration was linear in concentration range from 1–100 ng mL−1 with a correlation coefficient of 0.9977 for the target analyte. The limit of detection based on signal to noise ratio of 3 was 0.3 ng mL−1 and the relative standard deviations varied from 1.2–6.6%. The proposed method provides a rapid, sensitive, low cost, easy to handle, and convenient procedure to determine indapamide in human urine samples

Rapid analysis of triclosan in water samples using an in-tube ultrasonication assisted emulsification microextraction coupled with gas chromatography-electron capture detection

In this study, a new in-tube based ultrasound-assisted emulsification microextraction (IT-USAEME) technique coupled with gas chromatography-micro-electron capture detection (GC-μECD) was developed for the efficient and rapid analysis of triclosan in environmental water samples. In this extraction procedure, the aqueous sample was taken in an indigenously fabricated home-made glass extraction device (an 8 mL glass tube inbuilt with a self-scaled capillary tip) and extraction solvent (low density organic solvent) was added to it followed by ultrasonication. After extraction, the upper extractant layer was narrowed into the self-scaled capillary tip by pushing the plunger plug; thus making the collection and measurement of the upper organic solvent layer simple and convenient. Parameters affecting the extraction efficiency such as selection of extraction solvent, extraction solvent volume, ultrasonication time, pH and ionic strength were thoroughly investigated and optimized. Under optimal conditions, the method showed good linearity in the concentration range from 20–2000 ng L−1 with a correlation coefficient of 0.9982 for the target analyte. The limit of detection was 4 ng L−1 and the enrichment factor obtained was 331. The method was validated with real water samples and the relative recoveries of environmental water samples ranged between 91.2 and 97.3% and relative standard deviations ranged between 2.8 and 5.4%, making the proposed method highly reliable. Moreover, the present approach avoids the usage of chlorinated organic extraction solvents and derivatization processes for triclosan determination. The proposed method provides a simple, rapid, sensitive, low cost, easy to handle (in-tube set-up for USAEME) and eco-friendly procedure to determine triclosan in aqueous samples

A Microwave-Assisted Extraction Method for Determining Hot Water Solubility of Wood

A microwave-assisted extraction method is proposed as an alternative to the conventional method for determining the hot water solubility of wood. In this alternative method, microwave heating substitutes for the boiling water to extract part of the extraneous components as well as starches in wood tissues. Experimental results indicate that 100 mL water can be heated to boiling in only 75 s under the microwave radiation. Hence, only 15-20 min are required to complete the extraction procedure for Liquidambar formosana Hance and Swietenia mahagoni Jacq., 10 min and 5 min for Taiwania cryptomerioides and Cunninghamia lanceolata (Lamb) Hook, respectively. In general, a 15-min microwave heating has the potential to be an alternative to the conventional method, which requires 3 h for the hot water solubility determination

Rapid analysis of triclosan in water samples using an in-tube ultrasonication assisted emulsification microextraction coupled with gas chromatography-electron capture detection

In this study, a new in-tube based ultrasound-assisted emulsification microextraction (IT-USAEME) technique coupled with gas chromatography-micro-electron capture detection (GC-mECD) was developed for the efficient and rapid analysis of triclosan in environmental water samples. In this extraction procedure, the aqueous sample was taken in an indigenously fabricated home-made glass extraction device (an 8 mL glass tube inbuilt with a self-scaled capillary tip) and extraction solvent (low density organic solvent) was added to it followed by ultrasonication. After extraction, the upper extractant layer was narrowed into the self-scaled capillary tip by pushing the plunger plug; thus Moreover, the present approach avoids the usage of chlorinated organic extraction solvents and derivatization processes for triclosan determination. The proposed method provides a simple, rapid, sensitive, low cost, easy to handle (in-tube set-up for USAEME) and eco-friendly procedure to determine triclosan in aqueous samples

Comparison and Identification of Estrogen-Receptor Related Gene Expression Profiles in Breast Cancer of Different Ethnic Origins

The interactions between genetic variants in estrogen receptor (ER) have been identified to be associated with an increased risk of breast cancer. Available evidence indicates that genetic variance within a population plays a crucial role in the occurrence of breast cancer. Thus, the comparison and identification of ER-related gene expression profiles in breast cancer of different ethnic origins could be useful for the development of genetic variant cancer therapy. In this study, we performed microarray experiment to measure the gene expression profiles of 59 Taiwanese breast cancer patients; and through comparative bioinformatics analysis against published U.K. datasets, we revealed estrogen-receptor (ER) related gene expression between Taiwanese and British patients. In addition, SNP databases and statistical analysis were used to elucidate the SNPs associated with ER status. Our microarray results indicate that the expression pattern of the 65 genes in ER+ patients was dissimilar from that of the ER- patients. Seventeen mutually exclusive genes in ER-related breast cancer of the two populations with more than one statistically significant SNP in genotype and allele frequency were identified. These 17 genes and their related SNPs may be important in population-specific ER regulation of breast cancer. This study provides a global and feasible approach to study population-unique SNPs in breast cancer of different ethnic origins

A Supervised Network Analysis on Gene Expression Profiles of Breast Tumors Predicts a 41-Gene Prognostic Signature of the Transcription Factor MYB

Background. MYB is predicted to be a favorable prognostic predictor in a breast cancer population. We proposed to find the inferred mechanism(s) relevant to the prognostic features of MYB via a supervised network analysis. Methods. Both coefficient of intrinsic dependence (CID) and Galton Pierson’s correlation coefficient (GPCC) were combined and designated as CIDUGPCC. It is for the univariate network analysis. Multivariate CID is for the multivariate network analysis. Other analyses using bioinformatic tools and statistical methods are included. Results. ARNT2 is predicted to be the essential gene partner of MYB. We classified four prognostic relevant gene subpools in three breast cancer cohorts as feature types I–IV. Only the probes in feature type II are the potential prognostic feature of MYB. Moreover, we further validated 41 prognosis relevant probes to be the favorable prognostic signature. Surprisingly, two additional family members of MYB are elevated to promote poor prognosis when both levels of MYB and ARNT2 decline. Both MYBL1 and MYBL2 may partially decrease the tumor suppressive activities that are predicted to be up-regulated by MYB and ARNT2. Conclusions. The major prognostic feature of MYB is predicted to be determined by the MYB subnetwork (41 probes) that is relevant across subtypes

Major Functional Transcriptome of an Inferred Center Regulator of an ER(−) Breast Cancer Model System

We aimed to find clinically relevant gene activities ruled by the signal transducer and activator of transcription 3 (STAT3) proteins in an ER(−) breast cancer population via network approach. STAT3 is negatively associated with both lymph nodal category and stage. MYC is a component of STAT3 network. MYC and STAT3 may co-regulate gene expressions for Warburg effect, stem cell like phenotype, cell proliferation and angiogenesis. We identified a STAT3 network in silico showing its ability in predicting its target gene expressions primarily for specific tumor subtype, tumor progression, treatment options and prognostic features. The aberrant expressions of MYC and STAT3 are enriched in triple negatives (TN). They promote histological grade, vascularity, metastasis and tumor anti-apoptotic activities. VEGFA, STAT3, FOXM1 and METAP2 are druggable targets. High levels of METAP2, MMP7, IGF2 and IGF2R are unfavorable prognostic factors. STAT3 is an inferred center regulator at early cancer development predominantly in TN

Statistical identification of gene association by CID in application of constructing ER regulatory network

<p>Abstract</p> <p>Background</p> <p>A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating <it>in silico </it>inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID), is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs) (X) and their downstream genes (Y) based on clinical data. More specifically, we use estrogen receptor α (ERα) as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A).</p> <p>Results</p> <p>The analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearson's correlation coefficient (GPCC), Student's <it>t</it>-test (STT), coefficient of determination (CoD), and mutual information (MI). When being compared to GPCC, CoD, and MI, CID reveals its preferential ability to discover the regulatory association where distribution of the mRNA expression levels on X and Y does not fit linear models. On the other hand, when CID is used to measure the association of a continuous variable (Y) against a discrete variable (X), it shows similar performance as compared to STT, and appears to outperform CoD and MI. In addition, this study established a two-layer transcriptional regulatory network to exemplify the usage of CID, in combination with GPCC, in deciphering gene networks based on gene expression profiles from patient arrays.</p> <p>Conclusion</p> <p>CID is shown to provide useful information for identifying associations between genes and transcription factors of interest in patient arrays. When coupled with the relationships detected by GPCC, the association predicted by CID are applicable to the construction of transcriptional regulatory networks. This study shows how information from different data sources and learning algorithms can be integrated to investigate whether relevant regulatory mechanisms identified in cell models can also be partially re-identified in clinical samples of breast cancers.</p> <p>Availability</p> <p>the implementation of CID in R codes can be freely downloaded from <url>http://homepage.ntu.edu.tw/~lyliu/BC/</url>.</p

Identification of Prognostic Genes for Recurrent Risk Prediction in Triple Negative Breast Cancer Patients in Taiwan

Discrepancies in the prognosis of triple negative breast cancer exist between Caucasian and Asian populations. Yet, the gene signature of triple negative breast cancer specifically for Asians has not become available. Therefore, the purpose of this study is to construct a prediction model for recurrence of triple negative breast cancer in Taiwanese patients. Whole genome expression profiling of breast cancers from 185 patients in Taiwan from 1995 to 2008 was performed, and the results were compared to the previously published literature to detect differences between Asian and Western patients. Pathway analysis and Cox proportional hazard models were applied to construct a prediction model for the recurrence of triple negative breast cancer. Hierarchical cluster analysis showed that triple negative breast cancers from different races were in separate sub-clusters but grouped in a bigger cluster. Two pathways, cAMP-mediated signaling and ephrin receptor signaling, were significantly associated with the recurrence of triple negative breast cancer. After using stepwise model selection from the combination of the initial filtered genes, we developed a prediction model based on the genes SLC22A23, PRKAG3, DPEP3, MORC2, GRB7, and FAM43A. The model had 91.7% accuracy, 81.8% sensitivity, and 94.6% specificity under leave-one-out support vector regression. In this study, we identified pathways related to triple negative breast cancer and developed a model to predict its recurrence. These results could be used for assisting with clinical prognosis and warrant further investigation into the possibility of targeted therapy of triple negative breast cancer in Taiwanese patients