Seven swine were experimentally infected with Taenia solium eggs and blood samples from each animal were periodically collected. At the end of the experiment (t140) the animals did not show clinical aspects of cysticercosis or parasites in tongue inspection. All animals were slaughtered and cut into thin slices in searching for cysts. The number of cysts found in each animal varied from 1 to 85. Enzyme-linked immunosorbent assay (ELISA) tests for antibody (Ab) detection and for antigen (Ag) detection were performed, which presented respectively 71 and 57% of positivity. By immunoblot (IB), using 18/14(T. crassiceps Ag) or lentil-lectin-purified glycoproteins from T. solium Ag (LLGP) as Ag, five (71%) and six (86%) animals were positive, respectively. The association between Ag-ELISA with any IB (18/14 or LLGP) allowed the detection of all animals at 140 days post-experimental infection (days p.e.i.). The use of IB 18/14 combined to the Ag-ELISA allowed the detection of all animals since 70 days p.e.i., and the association between IB LLGP and Ag-ELISA allowed the detection of all animals since 112 days p.e.i. While all animals could be considered healthy by conventional screening tests, the use of immunoassays for detecting Ab and Ag showed better accuracy; therefore it would be more useful than usual clinical examination for screening cysticercosis in slightly infected pigs.
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http://dx.doi.org/10.1590/s0074-02762007005000085 | DOI Listing |
J Fluoresc
January 2025
Department of Fine Chemistry, Seoul National University of Science and Technology, Seoul, 01811, Korea.
We report a bithiophene-based fluorescence probe BDT (2,2'-(((1 E, 1'E)-[2,2'-bithiophene]-5,5'-diylbis(methaneylylidene))bis(azaneylylidene))bis(4-(tert-butyl)phenol)) for recognizing ClO. BDT selectively responded to ClO, leading to a blue fluorescence enhancement in a mixture of DMF/HEPES buffer (9:1, v/v). Importantly, BDT showed an ultrafast response (within 1 s) to ClO among the fluorescent turn-on chemosensors based on bithiophene.
View Article and Find Full Text PDFJ Microsc
January 2025
Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, UK.
Electron backscatter diffraction (EBSD) has developed over the last few decades into a valuable crystallographic characterisation method for a wide range of sample types. Despite these advances, issues such as the complexity of sample preparation, relatively slow acquisition, and damage in beam-sensitive samples, still limit the quantity and quality of interpretable data that can be obtained. To mitigate these issues, here we propose a method based on the subsampling of probe positions and subsequent reconstruction of an incomplete data set.
View Article and Find Full Text PDFAdv Mater
January 2025
Italian Institute of Technology, Genoa, 16163, Italy.
Presently, the in vitro recording of intracellular neuronal signals on microelectrode arrays (MEAs) requires complex 3D nanostructures or invasive and approaches such as electroporation. Here, it is shown that laser poration enables intracellular coupling on planar electrodes without damaging neurons or altering their spontaneous electrophysiological activity, allowing the process to be repeated multiple times on the same cells. This capability distinguishes laser-based neuron poration from more invasive methods like electroporation, which typically serve as endpoint measurement for cells.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China.
Bird species detection is critical for applications such as the analysis of bird population dynamics and species diversity. However, this task remains challenging due to local structural similarities and class imbalances among bird species. Currently, most deep learning algorithms focus on designing local feature extraction modules while ignoring the importance of global information.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of AI & Big Data, Honam University, Gwangju 62399, Republic of Korea.
This study proposes an advanced plant disease classification framework leveraging the Attention Score-Based Multi-Vision Transformer (Multi-ViT) model. The framework introduces a novel attention mechanism to dynamically prioritize relevant features from multiple leaf images, overcoming the limitations of single-leaf-based diagnoses. Building on the Vision Transformer (ViT) architecture, the Multi-ViT model aggregates diverse feature representations by combining outputs from multiple ViTs, each capturing unique visual patterns.
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