Publications by authors named "A V Solovev"

52 Publications

Reinvestigation of Carbohydrate Specificity of EBCA-1 Monoclonal Antibody Used for the Detection of Mannan.

J Fungi (Basel) 2021 Jun 24;7(7). Epub 2021 Jun 24.

Laboratory of Glycoconjugate Chemistry, N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciencesa, Leninsky Prospect 47, 119991 Moscow, Russia.

Monoclonal antibody EBCA-1 is used in the sandwich immune assay for the detection of circulating mannan in blood sera samples for the diagnosis of invasive candidiasis. To reinvestigate carbohydrate specificity of EBCA-1, a panel of biotinylated oligosaccharides structurally related to distinct fragments of mannan were loaded onto a streptavidin-coated plate to form a glycoarray. Its use demonstrated that EBCA-1 recognizes the trisaccharide β-Man-(1→2)-α-Man-(1→2)-α-Man and not homo-α-(1→2)-linked pentamannoside, as was reported previously.
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http://dx.doi.org/10.3390/jof7070504DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8303853PMC
June 2021

Angular Analysis of the B^{+}→K^{*+}μ^{+}μ^{-} Decay.

Phys Rev Lett 2021 Apr;126(16):161802

Laboratoire Leprince-ringuet (llr), Palaiseau, France.

We present an angular analysis of the B^{+}→K^{*+}(→K_{S}^{0}π^{+})μ^{+}μ^{-} decay using 9  fb^{-1} of pp collision data collected with the LHCb experiment. For the first time, the full set of CP-averaged angular observables is measured in intervals of the dimuon invariant mass squared. Local deviations from standard model predictions are observed, similar to those in previous LHCb analyses of the isospin-partner B^{0}→K^{*0}μ^{+}μ^{-} decay. The global tension is dependent on which effective couplings are considered and on the choice of theory nuisance parameters.
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http://dx.doi.org/10.1103/PhysRevLett.126.161802DOI Listing
April 2021

CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification.

Med Image Anal 2021 07 1;71:102054. Epub 2021 Apr 1.

Skolkovo Institute of Science and Technology, Moscow, Russia. Electronic address:

The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem. We describe two basic setups: Identification of COVID-19 to prioritize studies of potentially infected patients to isolate them as early as possible; Severity quantification to highlight patients with severe COVID-19, thus direct them to a hospital or provide emergency medical care. We formalize these tasks as binary classification and estimation of affected lung percentage. Though similar problems were well-studied separately, we show that existing methods could provide reasonable quality only for one of these setups. We employ a multitask approach to consolidate both triage approaches and propose a convolutional neural network to leverage all available labels within a single model. In contrast with the related multitask approaches, we show the benefit from applying the classification layers to the most spatially detailed feature map at the upper part of U-Net instead of the less detailed latent representation at the bottom. We train our model on approximately 1500 publicly available CT studies and test it on the holdout dataset that consists of 123 chest CT studies of patients drawn from the same healthcare system, specifically 32 COVID-19 and 30 bacterial pneumonia cases, 30 cases with cancerous nodules, and 31 healthy controls. The proposed multitask model outperforms the other approaches and achieves ROC AUC scores of 0.87±0.01 vs. bacterial pneumonia, 0.93±0.01 vs. cancerous nodules, and 0.97±0.01 vs. healthy controls in Identification of COVID-19, and achieves 0.97±0.01 Spearman Correlation in Severity quantification. We have released our code and shared the annotated lesions masks for 32 CT images of patients with COVID-19 from the test dataset.
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http://dx.doi.org/10.1016/j.media.2021.102054DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8015379PMC
July 2021

Micro-Bio-Chemo-Mechanical-Systems: Micromotors, Microfluidics, and Nanozymes for Biomedical Applications.

Adv Mater 2021 Jun 23;33(22):e2007465. Epub 2021 Apr 23.

Department of Materials Science, Fudan University, Shanghai, 200433, P. R. China.

Wireless nano-/micromotors powered by chemical reactions and/or external fields generate motive forces, perform tasks, and significantly extend short-range dynamic responses of passive biomedical microcarriers. However, before micromotors can be translated into clinical use, several major problems, including the biocompatibility of materials, the toxicity of chemical fuels, and deep tissue imaging methods, must be solved. Nanomaterials with enzyme-like characteristics (e.g., catalase, oxidase, peroxidase, superoxide dismutase), that is, nanozymes, can significantly expand the scope of micromotors' chemical fuels. A convergence of nanozymes, micromotors, and microfluidics can lead to a paradigm shift in the fabrication of multifunctional micromotors in reasonable quantities, encapsulation of desired subsystems, and engineering of FDA-approved core-shell structures with tuneable biological, physical, chemical, and mechanical properties. Microfluidic methods are used to prepare stable bubbles/microbubbles and capsules integrating ultrasound, optoacoustic, fluorescent, and magnetic resonance imaging modalities. The aim here is to discuss an interdisciplinary approach of three independent emerging topics: micromotors, nanozymes, and microfluidics to creatively: 1) embrace new ideas, 2) think across boundaries, and 3) solve problems whose solutions are beyond the scope of a single discipline toward the development of micro-bio-chemo-mechanical-systems for diverse bioapplications.
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http://dx.doi.org/10.1002/adma.202007465DOI Listing
June 2021

Observation of a New Excited D_{s}^{+} Meson in B^{0}→D^{-}D^{+}K^{+}π^{-} Decays.

Phys Rev Lett 2021 Mar;126(12):122002

Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France.

Using pp collision data corresponding to an integrated luminosity of 5.4  fb^{-1} collected with the LHCb detector at a center-of-mass energy of 13 TeV, the B^{0}→D^{-}D^{+}K^{+}π^{-} decay is studied. A new excited D_{s}^{+} meson is observed decaying into the D^{+}K^{+}π^{-} final state with large statistical significance. The pole mass and width, and the spin parity of the new state are measured with an amplitude analysis to be m_{R}=2591±6±7  MeV, Γ_{R}=89±16±12  MeV, and J^{P}=0^{-}, where the first uncertainty is statistical and the second systematic. Fit fractions for all components in the amplitude analysis are also reported. The new resonance, denoted as D_{s0}(2590)^{+}, is a strong candidate to be the D_{s}(2^{1}S_{0})^{+} state, the radial excitation of the pseudoscalar ground-state D_{s}^{+} meson.
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http://dx.doi.org/10.1103/PhysRevLett.126.122002DOI Listing
March 2021
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