Publications by authors named "R V Rubtsov"

Article Synopsis
  • - The study aimed to evaluate tracheal collapsibility using low-dose 4D CT, comparing the results with traditional methods like inspiratory-expiratory CT and bronchoscopy in patients suspected of tracheal collapse.
  • - Researchers analyzed 4D CT scans from 52 patients, finding that 48% exhibited significant tracheal collapsibility (50% or greater) and noted differences between visual 4D CT assessments and other diagnostic methods regarding detection rates of collapsibility.
  • - Results showed that 4D CT was superior in identifying patients with severe collapsibility compared to both paired CT and bronchoscopy, with significant differences in collapsibility measurements between the groups assessed.
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The peculiarities of spin effects in photoinduced electron transfer (ET) in diastereomers of donor-acceptor dyads are considered in order to study the influence of chirality on reactivity. Thus, the spin selectivity-the difference between the enhancement coefficients of chemically induced dynamic nuclear polarization (CIDNP)-of the dyad's diastereomers reflects the difference in the spin density distribution in its paramagnetic precursors that appears upon UV irradiation. In addition, the CIDNP coefficient itself has demonstrated a high sensitivity to the change of chiral centers: when one center is changed, the hyperpolarization of all polarized nuclei of the molecule is affected.

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The "bullseye" sign has been exclusively reported in patients suffering from coronavirus disease 2019 (COVID-19) pneumonia. It is theorized that this newly recognized computed tomography (CT) feature represents a sign of organizing pneumonia. Well established signs of organizing pneumonia also reported in COVID-19 patients include linear opacities, the "reversed halo" sign (or "atoll" sign), and a perilobular distribution of abnormalities.

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Introduction: Deep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which was trained on US screening data, on an independent dataset of indeterminate nodules in an European multicentre trial, to rule out benign nodules maintaining a high lung cancer sensitivity.

Methods: The LCP-CNN has been trained to generate a malignancy score for each nodule using CT data from the U.

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The finger and palmar patterns were studied in miners suffering from peptic ulcer and in healthy subjects. The revealed complex of 26 dermatoglyphic indicators permit the prognostication of peptic ulcer, which fact promotes efficiency of measures designed to prevent the disorder.

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