On the basis of the lanthanide immunofluorescent assay (LIFA) test systems for the determination of herpes simplex virus (HSV-LIFA) and cytomegalovirus (CMV-LIFA) antigens have been developed. The test system HSV-LIFA includes the sandwich of monoclonal antibodies (McAb) HSV A.3.3. or B-11 to type 2 HSV, strain BH; the test system CMV-LIFA includes the sandwich of rabbit McAb to CMV, strain AD 169. The approbation of the test systems has revealed that they insure the specific detection of HSV and CMV antigens in clinical specimens (urine, blood, liquor, saliva), the LIFA results well correlating with the data on the isolation of viruses in cell cultures, with the results obtained by other diagnostic methods and with clinical manifestations of diseases. LIFA has been shown to be more sensitive than the enzyme immunoassay.
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Phys Rev Lett
December 2024
Center for Computational Astrophysics, Flatiron Institute, 162 5th Avenue, New York, New York 10010, USA.
High-energy extensions to general relativity modify the Einstein-Hilbert action with higher-order curvature corrections and theory-specific coupling constants. The order of these corrections imprints a universal curvature dependence on observations while the coupling constant controls the deviation strength. In this Letter, we leverage the theory-independent expectation that modifications to the action of a given order in spacetime curvature (Riemann tensor and contractions) lead to observational deviations that scale with the system length scale to a corresponding power.
View Article and Find Full Text PDFDiagnosis (Berl)
January 2025
Department of Laboratory Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.
Objectives: To examine factors impacting diagnostic evaluation of suspected deep vein thrombosis (DVT) by analyzing the test ordering patterns and provider decision-making within a universal health coverage system in Hungary.
Methods: We analyzed test orders for suspected DVT between 2007 and 2020, and the financial framework influencing diagnostic practices. An anonymous survey was also conducted among Emergency Department physicians to explore factors influencing diagnostic decision-making.
Anal Chem
January 2025
Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium.
Addressing the global challenge of ensuring access to safe drinking water, especially in developing countries, demands cost-effective, eco-friendly, and readily available technologies. The persistence, toxicity, and bioaccumulation potential of organic pollutants arising from various human activities pose substantial hurdles. While high-performance liquid chromatography coupled with high-resolution mass spectrometry (HPLC-HRMS) is a widely utilized technique for identifying pollutants in water, the multitude of structures for a single elemental composition complicates structural identification.
View Article and Find Full Text PDFThis study intends to optimize the carbon footprint management model of power enterprises through artificial intelligence (AI) technology to help the scientific formulation of carbon emission reduction strategies. Firstly, a carbon footprint calculation model based on big data and AI is established, and then machine learning algorithm is used to deeply mine the carbon emission data of power enterprises to identify the main influencing factors and emission reduction opportunities. Finally, the driver-state-response (DSR) model is used to evaluate the carbon audit of the power industry and comprehensively analyze the effect of carbon emission reduction.
View Article and Find Full Text PDFPLoS One
January 2025
School of Economics & Management, Beijing Information Science & Technology University, Beijing, China.
E-commerce faces challenges such as content homogenization and high perceived risk among users. This paper aims to predict perceived risk in different contexts by analyzing review content and website information. Based on a dataset containing 262,752 online reviews, we employ the KeyBERT-TextCNN model to extract thematic features from the review content.
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