Pathogenetic Characteristics of Anemia in Patients with Solid Tumors

Aim. To study the impact iron metabolism disturbances and cytokine levels on the development of anemia in patients with solid tumors. Materials & Methods. The research included 42 patients with malignant neoplasms, including 24 patients with anemia (19 men and 5 women, median age 67.7 ± 10 years) and 18 patients without anemia (15 men, 3 women, median age 65.7 ± 14 years). Anemia was diagnosed according to the WHO criteria (in men: erythrocytes < 4.0 × 1012/L, hemoglobin < 130 g/L, hematocrit < 39 %; in women: erythrocytes < 3.8 × 1012/L, hemoglobin < 120 g/L, hematocrit < 36 %). Results. A comparative analysis of iron metabolism in patients with and without anemia was performed. The lower values of serum iron and transferrin saturation in patients with anemia were shown (p 0,05). Higher levels of interleukins 6 and 10 (IL-6 and IL-10) were observed in patients with anemia (p < 0.05). For IL-6, correlations were observed with levels of erythrocytes (r = –0,58), hemoglobin (r = –0,57), hematocrit (r = –0,52), and leukocytes (r = 0,42). The levels of IL-10 slightly correlated with the levels of erythrocytes, leukocytes, platelets, MCV, and MCH (r < 0.3). For IL-10, correlations were established with levels of MCHC (r = –0,71), hemoglobin (r = –0,64) and hematocrit (r = –0,32). Correlations between the levels IL-6, IL-6 and hemoglobin, erythrocytes and several color indices may indicate their influence on the development of anemia in patients with malignant neoplasms. Conclusion. A functional iron deficiency in patients with anemia was found. Several causes of anemia development and significant role of interleukins in anemia pathogenesis were also discovered

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Design, construction and operation of the ProtoDUNE-SP Liquid Argon TPC

The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber (LArTPC) that was constructed and operated in the CERN North Area at the end of the H4 beamline. This detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment (DUNE), which will be constructed at the Sandford Underground Research Facility (SURF) in Lead, South Dakota, U.S.A. The ProtoDUNE-SP detector incorporates full-size components as designed for DUNE and has an active volume of 7 × 6 × 7.2 m3. The H4 beam delivers incident particles with well-measured momenta and high-purity particle identification. ProtoDUNE-SP's successful operation between 2018 and 2020 demonstrates the effectiveness of the single-phase far detector design. This paper describes the design, construction, assembly and operation of the detector components

Low exposure long-baseline neutrino oscillation sensitivity of the DUNE experiment

The Deep Underground Neutrino Experiment (DUNE) will produce world-leading neutrino oscillation measurements over the lifetime of the experiment. In this work, we explore DUNE's sensitivity to observe charge-parity violation (CPV) in the neutrino sector, and to resolve the mass ordering, for exposures of up to 100 kiloton-megawatt-years (kt-MW-yr). The analysis includes detailed uncertainties on the flux prediction, the neutrino interaction model, and detector effects. We demonstrate that DUNE will be able to unambiguously resolve the neutrino mass ordering at a 3σ (5σ) level, with a 66 (100) kt-MW-yr far detector exposure, and has the ability to make strong statements at significantly shorter exposures depending on the true value of other oscillation parameters. We also show that DUNE has the potential to make a robust measurement of CPV at a 3σ level with a 100 kt-MW-yr exposure for the maximally CP-violating values \delta_{\rm CP}} = \pm\pi/2. Additionally, the dependence of DUNE's sensitivity on the exposure taken in neutrino-enhanced and antineutrino-enhanced running is discussed. An equal fraction of exposure taken in each beam mode is found to be close to optimal when considered over the entire space of interest