Double-strand breaks in lymphocyte DNA of humans exposed to [18F]fluorodeoxyglucose and the static magnetic field in PET/MRI.

BACKGROUND: Given the increasing clinical use of PET/MRI, potential risks to patients from simultaneous exposure to ionising radiation and (electro)magnetic fields should be thoroughly investigated as a precaution. With this aim, the genotoxic potential of 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) and a strong static magnetic field (SMF) were evaluated both in isolation and in combination using the γH2AX assay detecting double-strand breaks in lymphocyte DNA. METHODS: Thirty-two healthy young volunteers allocated to three study arms were exposed to [18F]FDG alone, to a 3-T SMF alone or to both combined over 60 min at a PET/CT or a PET/MRI system. Blood samples taken after in vivo exposure were incubated up to 60 min to extend the irradiation of blood by residual [18F]FDG within the samples and the time to monitor the γH2AX response. Absorbed doses to lymphocytes delivered in vivo and in vitro were estimated individually for each volunteer exposed to [18F]FDG. γH2AX foci were scored automatically by immunofluorescence microscopy. RESULTS: Absorbed doses to lymphocytes exposed over 60 to 120 min to [18F]FDG varied between 1.5 and 3.3 mGy. In this time interval, the radiotracer caused a significant median relative increase of 28% in the rate of lymphocytes with at least one γH2AX focus relative to the background rate (p = 0.01), but not the SMF alone (p = 0.47). Simultaneous application of both agents did not result in a significant synergistic or antagonistic outcome (p = 0.91). CONCLUSION: There is no evidence of a synergism between [18F]FDG and the SMF that may be of relevance for risk assessment of PET/MRI.

qPSMA: Semiautomatic Software for Whole-Body Tumor Burden Assessment in Prostate Cancer Using 68Ga-PSMA11 PET/CT.

Our aim was to introduce and validate qPSMA, a semiautomatic software package for whole-body tumor burden assessment in prostate cancer patients using 68Ga-prostate-specific membrane antigen (PSMA) 11 PET/CT. Methods: qPSMA reads hybrid PET/CT images in DICOM format. Its pipeline was written using Python and C++ languages. A bone mask based on CT and a normal-uptake mask including organs with physiologic 68Ga-PSMA11 uptake are automatically computed. An SUV threshold of 3 and a liver-based threshold are used to segment bone and soft-tissue lesions, respectively. Manual corrections can be applied using different tools. Multiple output parameters are computed, that is, PSMA ligand-positive tumor volume (PSMA-TV), PSMA ligand-positive total lesion (PSMA-TL), PSMA SUVmean, and PSMA SUVmax Twenty 68Ga-PSMA11 PET/CT data sets were used to validate and evaluate the performance characteristics of qPSMA. Four analyses were performed: validation of the semiautomatic algorithm for liver background activity determination, assessment of intra- and interobserver variability, validation of data from qPSMA by comparison with Syngo.via, and assessment of computational time and comparison of PSMA PET-derived parameters with serum prostate-specific antigen. Results: Automatic liver background calculation resulted in a mean relative difference of 0.74% (intraclass correlation coefficient [ICC], 0.996; 95%CI, 0.989;0.998) compared with METAVOL. Intra- and interobserver variability analyses showed high agreement (all ICCs > 0.990). Quantitative output parameters were compared for 68 lesions. Paired t testing showed no significant differences between the values obtained with the 2 software packages. The ICC estimates obtained for PSMA-TV, PSMA-TL, SUVmean, and SUVmax were 1.000 (95%CI, 1.000;1.000), 1.000 (95%CI, 1.000;1.000), 0.995 (95%CI, 0.992;0.997), and 0.999 (95%CI, 0.999;1.000), respectively. The first and second reads for intraobserver variability resulted in mean computational times of 13.63 min (range, 8.22-25.45 min) and 9.27 min (range, 8.10-12.15 min), respectively (P = 0.001). Highly significant correlations were found between serum prostate-specific antigen value and both PSMA-TV (r = 0.72, P < 0.001) and PSMA-TL (r = 0.66, P = 0.002). Conclusion: Semiautomatic analyses of whole-body tumor burden in 68Ga-PSMA11 PET/CT is feasible. qPSMA is a robust software package that can help physicians quantify tumor load in heavily metastasized prostate cancer patients.

Superantigen-like gene(s) in human pathogenic Streptococcus dysgalactiae, subsp equisimilis: genomic localisation of the gene encoding streptococcal pyrogenic exotoxin G (speG(dys)).

Streptococcus pyogenes (GAS) causes about 90% of streptococcal human infections while group C (GCS) and G (GGS) streptococci can be pathogenic for different mammalians. Especially the human pathogenic GCS and GGS, Streptococcus dysgalactiae, subsp. equisimilis, account for 5-8% of the human streptococcal diseases like wound infections, otitis media, purulent pharyngitis and also streptococcal toxic shock syndrome. A defined superantigen so far was not identified in GCS and GGS strains. In the present investigation we screened DNA of GCS and GGS human isolates for the presence of genes for streptococcal pyrogenic exotoxins (spe) by hybridisation with probes that stand for the GAS genes speA, speC, speZ (smeZ), speH, speG, speI, speJ and ssa. In many GCS and GGS strains we found positive reactions with the probes speG, speJ and ssa, but not with the probes for the remaining genes under investigation. PCR amplification with subsequent sequence analysis of the PCR fragments revealed only the presence of the gene speG in GCS and GGS strains, while no DNA fragments specific for speJ and ssa could be amplified. Additionally, the upstream and downstream regions flanking speG in GGS strain 39072 were sequenced. Remarkable differences were found in the neighbourhood of speG between GAS and GGS sequences. Downstream of speG we identified in strain GGS 39072 two new open reading frames encoding proteins with no similarity to protein sequences accessible in the databases so far. In the compared GAS strains SF370 and MGAS8232, this segment, apart from some small fragments, had been deleted. Our analysis suggests that a gene transfer from GGS to GAS has preceded following deletion of the two genes orf1 and orf2 in GAS.