1,187 research outputs found
A rare cause of recurrent spontaneous pneumothorax: Birt-hogg-dube syndrome
Birt-Hogg-Dube (BHD) syndrome is an unusual disorder characterized by the triad of cutaneous lesions, renal tumors and lung cysts. In cases with BHD syndrome, the frequency of recurrent pneumothorax is increased due to presence of multiple lung cysts. It is important to evaluate the BHD syndrome in differential diagnosis of recurrent pneumothorax especially with multiple lung cysts predominating in the lung base. In these patients, the presence of accompanying kidney and other tumors should be investigated. Herein, we report a case of BHD syndrome presenting with recurrent pneumothorax. © 2018 by Turkish Thoracic Society
Analysis of the volatile components of five Turkish Rhododendron species by headspace solid-phase microextraction and GC-MS (HS-SPME-GC-MS)
Volatile constituents of various solvent extracts (n-hexane, CH2Cl2, H2O) of 15 different
organs (leaves, flowers, fruits) of five Rhododendron species (Ericaceae) growing in Turkey
were trapped with headspace solid-phase microextraction (HS-SPME) technique and analyzed
by GC-MS. A total of 200 compounds were detected and identified from organic extracts,
while the water extracts contained only traces of few volatiles. The CH2Cl2 extract of
the R. luteum flowers was found to exhibit the most diverse composition: 34 compounds
were identified, with benzyl alcohol (16.6%), limonene (14.6%) and p-cymene (8.4%) being
the major compounds. The CH2Cl2-solubles of R. x sochadzeae leaves contained only phenyl
ethyl alcohol. This study indicated appreciable intra-specific variations in volatile compositions
within the genus. Different anatomical parts also showed altered volatile profiles. This
is the first application of HS-SPME-GC-MS on the volatiles of Rhododendron species
Bioactive surface design based on functional composite electrospun nanofibers for biomolecule immobilization and biosensor applications
Cataloged from PDF version of article.The combination of nanomaterials and conducting polymers attracted remarkable attention for development of new immobilization matrices for enzymes. Hereby, an efficient surface design was investigated by modifying the graphite rod electrode surfaces with one-step electrospun nylon 6,6 nanofibers or 4% (w/w) multiwalled carbon nanotubes (MWCNTs) incorporating nylon 6,6 nanofibers (nylon 6,6/4MWCNT). High-resolution transmission electron microscopy study confirmed the successful incorporation of the MWCNTs into the nanofiber matrix for nylon 6,6/4MWCNT sample. Then, these nanofibrous surfaces were coated with a conducting polymer, (poly-4-(4,7-di(thiophen-2-yl)-1H-benzo[d]imidazol-2-yl) benzaldehyde) (PBIBA) to obtain a high electroactive surface area as new functional immobilization matrices. Due to the free aldehyde groups of the polymeric structures, a model enzyme, glucose oxidase was efficiently immobilized to the modified surfaces via covalent binding. Scanning electron microscope images confirmed that the nanofibrous structures were protected after the electrodeposition step of PBIBA and a high amount of protein attachment was successfully achieved by the help of high surface to volume ratio of electroactive nanofiber matrices. The biosensors were characterized in terms of their operational and storage stabilities and kinetic parameters (K mapp and Imax). The resulting novel glucose biosensors revealed good stability and promising Imax values (10.03 and 16.67 μA for nylon 6,6/PBIBA and nylon 6,6/4MWCNT/PBIBA modified biosensors, respectively) and long shelf life (32 and 44 days for nylon 6,6/PBIBA and nylon 6,6/4MWCNT/PBIBA modified biosensors, respectively). Finally, the biosensor was tested on beverages for glucose detection. © 2014 American Chemical Society
Advances in plasmonic technologies for point of care applications
Infectious diseases have considerable economic and societal impact on developing settings. For instance, malaria is observed more commonly in sub-Saharan Africa and India. The societal impact of acquired immune deficiency syndrome (AIDS) and tuberculosis is high, through targeting adults in villages and leaving behind declining populations. Highly sensitive and specific lab assays such as cell culture methods, polymerase chain reaction (PCR), and enzyme-linked immunosorbent assay (ELISA) are available for diagnosis of infectious diseases in the developed world. They require sample transportation, manual preparation steps, and skilled and well-trained technicians. These clinical conventional methods provide results in several hours to days, precluding rapid detection and response at the primary care settings. Another diagnostic challenge is identifying multiple pathogens
Simple filter microchip for rapid separation of plasma and viruses from whole blood
Sample preparation is a significant challenge for detection and sensing technologies, since the presence of blood cells can interfere with the accuracy and reliability of virus detection at the nanoscale for point-of-care testing. To the best of our knowledge, there is not an existing on-chip virus isolation technology that does not use complex fluidic pumps. Here, we presented a lab-on-a-chip filter device to isolate plasma and viruses from unprocessed whole blood based on size exclusion without using a micropump. We demonstrated that viruses (eg, HIV) can be separated on a filter-based chip (2-μm pore size) from HIV-spiked whole blood at high recovery efficiencies of 89.9% ± 5.0%, 80.5% ± 4.3%, and 78.2% ± 3.8%, for viral loads of 1000, 10,000 and 100,000 copies/mL, respectively. Meanwhile, 81.7% ± 6.7% of red blood cells and 89.5% ± 2.4% of white blood cells were retained on 2 μm pore–sized filter microchips. We also tested these filter microchips with seven HIV-infected patient samples and observed recovery efficiencies ranging from 73.1% ± 8.3% to 82.5% ± 4.1%. These results are first steps towards developing disposable point-of-care diagnostics and monitoring devices for resource-constrained settings, as well as hospital and primary care settings
Forecasting of Suspended Sediment in Rivers Using Artificial Neural Networks Approach
Suspended sediment estimation is important to the water resources management and water quality problem. In this article, artificial neural networks (ANN), M5tree (M5T) approaches and statistical approaches such as Multiple Linear Regression (MLR), Sediment Rating Curves (SRC) are used for estimation daily suspended sediment concentration from daily temperature of water and streamflow in river. These daily datas were measured at Iowa station in US. These prediction aproaches are compared to each other according to three statistical criteria, namely, mean square errors (MSE), mean absolute relative error (MAE) and correlation coefficient (R). When the results are compared ANN approach have better forecasts suspended sediment than the other estimation methods
Statistical Modeling of Single Target Cell Encapsulation
High throughput drop-on-demand systems for separation and encapsulation of individual target cells from heterogeneous mixtures of multiple cell types is an emerging method in biotechnology that has broad applications in tissue engineering and regenerative medicine, genomics, and cryobiology. However, cell encapsulation in droplets is a random process that is hard to control. Statistical models can provide an understanding of the underlying processes and estimation of the relevant parameters, and enable reliable and repeatable control over the encapsulation of cells in droplets during the isolation process with high confidence level. We have modeled and experimentally verified a microdroplet-based cell encapsulation process for various combinations of cell loading and target cell concentrations. Here, we explain theoretically and validate experimentally a model to isolate and pattern single target cells from heterogeneous mixtures without using complex peripheral systems.Wallace H. Coulter Foundation (Young Investigator in Bioengineering Award)National Institutes of Health (U.S.) (Grant R01AI081534)National Institutes of Health (U.S.) (Grant R21AI087107
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