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Gravitational radiation from ultra high energy cosmic rays in models with large extra dimensions
(2006)
The effects of classical gravitational radiation in models with large extra dimensions are investigated for ultra high energy cosmic rays (CRs). The cross sections are implemented into a simulation package (SENECA) for high energy hadron induced CR air showers. We predict that gravitational radiation from quasi-elastic scattering could be observed at incident CR energies above 10^9 GeV for a setting with more than two extra dimensions. It is further shown that this gravitational energy loss can alter the energy reconstruction for CR energies E_CR > 5 10^9 GeV.
The thrombopoietin receptor agonist eltrombopag was successfully used against human cytomegalovirus (HCMV)-associated thrombocytopenia refractory to immunomodulatory and antiviral drugs. These effects were ascribed to effects of eltrombopag on megakaryocytes. Here, we tested whether eltrombopag may also exert direct antiviral effects. Therapeutic eltrombopag concentrations inhibited HCMV replication in human fibroblasts and adult mesenchymal stem cells infected with six different virus strains and drug-resistant clinical isolates. Eltrombopag also synergistically increased the anti-HCMV activity of the mainstay drug ganciclovir. Time-of-addition experiments suggested that eltrombopag interferes with HCMV replication after virus entry. Eltrombopag was effective in thrombopoietin receptor-negative cells, and addition of Fe3+ prevented the anti-HCMV effects, indicating that it inhibits HCMV replication via iron chelation. This may be of particular interest for the treatment of cytopenias after haematopoietic stem cell transplantation, as HCMV reactivation is a major reason for transplantation failure. Since therapeutic eltrombopag concentrations are effective against drug-resistant viruses and synergistically increase the effects of ganciclovir, eltrombopag is also a drug repurposing candidate for the treatment of therapy-refractory HCMV disease.
Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features. We constructed a multi-object fruit phantom to perform MRI acquisition as scan-rescan using a 3 Tesla MRI scanner. We applied T2-weighted (T2w) half-Fourier acquisition single-shot turbo spin-echo (HASTE), T2w turbo spin-echo (TSE), T2w fluid-attenuated inversion recovery (FLAIR), T2 map and T1-weighted (T1w) TSE. Images were resampled to isotropic voxels. Fruits were segmented. The workflow was repeated by a second reader and the first reader after a pause of one month. We applied PyRadiomics to extract 107 radiomic features per fruit and sequence from seven feature classes. We calculated concordance correlation coefficients (CCC) and dynamic range (DR) to obtain measurements of feature robustness. Intraclass correlation coefficient (ICC) was calculated to assess intra- and inter-observer reproducibility. We calculated Gini scores to test the pairwise discriminative power specific for the features and MRI sequences. We depict Bland Altmann plots of features with top discriminative power (Mann–Whitney U test). Shape features were the most robust feature class. T2 map was the most robust imaging technique (robust features (rf), n = 84). HASTE sequence led to the least amount of rf (n = 20). Intra-observer ICC was excellent (≥ 0.75) for nearly all features (max–min; 99.1–97.2%). Deterioration of ICC values was seen in the inter-observer analyses (max–min; 88.7–81.1%). Complete robustness across all sequences was found for 8 features. Shape features and T2 map yielded the highest pairwise discriminative performance. Radiomics validity depends on the MRI sequence and feature class. T2 map seems to be the most promising imaging technique with the highest feature robustness, high intra-/inter-observer reproducibility and most promising discriminative power.
The long-term effect of protection by two doses of SARS-CoV-2 vaccination in patients receiving chronic intermittent hemodialysis (CIHD) is an urging question. We investigated the humoral and cellular immune response of 42 CIHD patients who had received two doses of SARS-CoV-2 vaccine, and again after a booster vaccine with mRNA-1273 six months later. We measured antibody levels and SARS-CoV-2-specific surrogate neutralizing antibodies (SNA). Functional T cell immune response to vaccination was assessed by quantifying interferon-γ (IFN-γ) and IL-2 secreting T cells specific for SARS-CoV-2 using an ELISpot assay. Our data reveal a moderate immune response after the second dose of vaccination, with significantly decreasing SARS-CoV-2-specific antibody levels and less than half of the study group showed neutralizing antibodies six months afterwards. Booster vaccines increased the humoral response dramatically and led to a response rate of 89.2% for antibody levels and a response rate of 94.6% for SNA. Measurement in a no response/low response (NR/LR) subgroup of our cohort, which differed from the whole group in age and rate of immunosuppressive drugs, indicated failure of a corresponding T cell response after the booster vaccine. We strongly argue in favor of a regular testing of surrogate neutralizing antibodies and consecutive booster vaccinations for CIHD patients to provide a stronger and persistent immunity.
The ALICE Zero Degree Calorimeter system (ZDC) is composed of two identical sets of calorimeters, placed at opposite sides with respect to the interaction point, 114 meters away from it, complemented by two small forward electromagnetic calorimeters (ZEM). Each set of detectors consists of a neutron (ZN) and a proton (ZP) ZDC. They are placed at zero degrees with respect to the LHC axis and allow to detect particles emitted close to beam direction, in particular neutrons and protons emerging from hadronic heavy-ion collisions (spectator nucleons) and those emitted from electromagnetic processes. For neutrons emitted by these two processes, the ZN calorimeters have nearly 100% acceptance.
During the √sNN = 2.76 TeV Pb-Pb data-taking, the ALICE Collaboration studied forward neutron emission with a dedicated trigger, requiring a minimum energy deposition in at least one of the two ZN. By exploiting also the information of the two ZEM calorimeters it has been possible to separate the contributions of electromagnetic and hadronic processes and to study single neutron vs. multiple neutron emission.
The measured cross sections of single and mutual electromagnetic dissociation of Pb nuclei at √sNN = 2.76 TeV, with neutron emission, are σsingle EMD = 187:4 ± 0.2 (stat.)−11.2+13.2 (syst.) b and σmutual EMD = 5.7 ± 0.1 (stat.) ±0.4 (syst.) b, respectively [1]. This is the first measurement of electromagnetic dissociation of 208Pb nuclei at the LHC energies, allowing a test of electromagnetic dissociation theory in a new energy regime. The experimental results are compared to the predictions from a relativistic electromagnetic dissociation model.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.