741 research outputs found
Extraction and determination of organosulfur compounds in water samples by using homogeneous liquid-liquid micro-extraction via flotation assistance-gas chromatography-flame ionization detection
A new method was developed for the preconcentration and determination of organosulfur compounds (OSCs) in water samples using homogeneous liquid-liquid microextraction via flotation assistance (HLLME-FA) and gas chromatography (GC) with flame ionization detection (FID). Toluene at microliter volume level and acetone were used as an extraction and a homogeneous solvent, respectively. In this research, a special extraction cell was designed to facilitate collection of the low-density solvent extraction. No centrifugation was required in this procedure. Using air flotation, extraction solvent was collected at the conical part of the designed cell. The effects of the different variables on the efficiency of the extraction such as kind and the volume of extraction and homogeneous solvents, ionic strength and extraction time were studied and optimized. Under the optimum conditions, linearity of the method were in the range from 0.25 to 200 ”g L-1 with limit of detections (LODs) (S/N = 3) were in the range from 0.05 to 0.4 ”g L-1. HLLME-FA is a fast, simple and efficient method for the determination of organic sulfur compounds in aquatic samples. KEY WORDS: Homogeneous liquid-liquid microextraction, Flotation assistance, Organosulfur compounds, Gas chromatography, Water samples Bull. Chem. Soc. Ethiop. 2014, 28(2), 195-204DOI: http://dx.doi.org/10.4314/bcse.v28i2.
Identification of novel genes involved in gastric carcinogenesis by suppression subtractive hybridization
Gastric cancer (GC) is one of the most common and life-threatening types of malignancies. Identification of the differentially expressed genes in GC is one of the best approaches for establishing new diagnostic and therapeutic targets. Furthermore, these investigations could advance our knowledge about molecular biology and the carcinogenesis of this cancer. To screen for the overexpressed genes in gastric adenocarcinoma, we performed suppression subtractive hybridization (SSH) on gastric adenocarcinoma tissue and the corresponding normal gastric tissue, and eight genes were found to be overexpressed in the tumor compared with those of the normal tissue. The genes were ribosomal protein L18A, RNase H2 subunit B, SEC13, eukaryotic translation initiation factor 4A1, tetraspanin 8, cytochrome c oxidase subunit 2, NADH dehydrogenase subunit 4, and mitochondrially encoded ATP synthase 6. The common functions among the identified genes include involvement in protein synthesis, involvement in genomic stability maintenance, metastasis, metabolic improvement, cell signaling pathways, and chemoresistance. Our results provide new insights into the molecular biology of GC and drug discovery: each of the identified genes could be further investigated as targets for prognosis evaluation, diagnosis, treatment, evaluation of the response to new anticancer drugs, and determination of the molecular pathogenesis of GC. © The Author(s) 2014
Genome expression analysis by suppression subtractive hybridization identified overexpression of Humanin, a target gene in gastric cancer chemoresistance
Background: In cancer cells, apoptosis is an important mechanism that influences the outcome of chemotherapy and the development of chemoresistance. To find the genes involved in chemoresistance and the development of gastric cancer, we used the suppression subtractive hybridization method to identify the genes that are overexpressed in gastric cancer tissues compared to normal gastric tissues. Results: In the suppression subtractive hybridization library we constructed, the most highly overexpressed genes were humanin isoforms. Humanin is a recently identified endogenous peptide that has anti-apoptotic activity and has been selected for further study due to its potential role in the chemoresistance of gastric cancer. Upregulation of humanin isoforms was also observed in clinical samples by using quantitative real-time PCR. Among the studied isoforms, humanin isoform 3, with an expression level of 4.166 ± 1.44 fold, was the most overexpressed isoform in GC. Conclusions: The overexpression of humanin in gastric cancer suggests a role for chemoresistance and provides new insight into the biology of gastric cancer. We propose that humanin isoforms are novel targets for combating chemoresistance in gastric cancer. © 2014 Mottaghi-Dastjerdi et al.; licensee BioMed Central Ltd
Solid-phase extraction followed by dispersive liquid-liquid microextraction for the sensitive determination of ecstasy compounds and amphetamines in biological samples
A novel approach for the determination of ecstasy and amphetamines (3,4-methylenedioxymethylamphetamine (MDMA, Ecstasy), 3,4-methylenedioxyamphetamine (MDA), 3,4-methylenedioxyethylamphetamine (MDEA) and 3,4-methylenedioxypropylamphetamine (MDPA)) in biological samples is presented. The analytes were extracted from the matrix and transferred to a small volume of a high density, water insoluble solvent using solid-phase extraction (SPE) followed by dispersive liquid-liquid microextraction (DLLME). This combination not only resulted in a high enrichment factor, but also it could be used in complex matrices (biological samples). Some important extraction parameters, such as sample solution flow rate, sample pH, type and volume of extraction and disperser solvents as well as the salt addition, were studied and optimized. Under the optimized conditions, the calibration graphs were linear in the range of 0.5-500 ”g L-1 and 1.0-500 ”g L-1 with detection limits in the range of 0.1-0.3 ”g L-1 and 0.2-0.7 ”g L-1 in urine and plasma samples, respectively. The results showed that SPE-DLLME is a suitable method for the determination of ecstasy components and amphetamines in biological and water samples. KEY WORDS: Dispersive liquid-liquid microextraction, Solid-phase extraction, Ecstasy compounds, Amphetamines, Gas chromatography, Biological samples Bull. Chem. Soc. Ethiop. 2014, 28(3), 339-348.DOI: http://dx.doi.org/10.4314/bcse.v28i3.
Application of Ultrasound-assisted Emulsification Microextraction followed by Gas Chromatography for Determination of Oxadiazon in Water and Soil Samples
In this study, a simple and efficient ultrasound-assisted emulsification microextraction (USAEME) method combined with gas chromatography (GC) was developed for the preconcentration and determination of oxadiazon in water and soil samples. In this method, fine droplets of toluene were formedand dispersed in the sample with the help of ultrasonic waves which accelerated the formation of a fine cloudy solution without using disperser solvents. Several factors influencing the extraction efficiency, such as the nature and volume of organic solvent, extraction temperature, ionic strength and centrifugation time, were investigated and optimized. Using optimum extraction conditions a detection limit of 0.1 ÎŒg Lâ1 and a good linearity in a calibration range of 0.25â250 ÎŒg Lâ1 were achieved for the analyte in a river water sample. This proposed method was successfully applied to the analysis of oxadiazon in water and soil samples.KEYWORDS Utrasound-assisted emulsification microextraction, oxadiazon, gas chromatography, water samples, soil samples
Autonomous assessment of spontaneous retinal venous pulsations in fundus videos using a deep learning framework
The presence or absence of spontaneous retinal venous pulsations (SVP) provides clinically significant insight into the hemodynamic status of the optic nerve head. Reduced SVP amplitudes have been linked to increased intracranial pressure and glaucoma progression. Currently, monitoring for the presence or absence of SVPs is performed subjectively and is highly dependent on trained clinicians. In this study, we developed a novel end-to-end deep model, called U3D-Net, to objectively classify SVPs as present or absent based on retinal fundus videos. The U3D-Net architecture consists of two distinct modules: an optic disc localizer and a classifier. First, a fast attention recurrent residual U-Net model is applied as the optic disc localizer. Then, the localized optic discs are passed on to a deep convolutional network for SVP classification. We trained and tested various time-series classifiers including 3D Inception, 3D Dense-ResNet, 3D ResNet, Long-term Recurrent Convolutional Network, and ConvLSTM. The optic disc localizer achieved a dice score of 95% for locating the optic disc in 30 milliseconds. Amongst the different tested models, the 3D Inception model achieved an accuracy, sensitivity, and F1-Score of 84 ± 5%, 90 ± 8%, and 81 ± 6% respectively, outperforming the other tested models in classifying SVPs. To the best of our knowledge, this research is the first study that utilizes a deep neural network for an autonomous and objective classification of SVPs using retinal fundus videos
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