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Chromosomal rearrangements of the human MLL (mixed lineage leukemia) gene are associated with high-risk infant, pediatric, adult and therapy-induced acute leukemias. We used long-distance inverse-polymerase chain reaction to characterize the chromosomal rearrangement of individual acute leukemia patients. We present data of the molecular characterization of 1590 MLL-rearranged biopsy samples obtained from acute leukemia patients. The precise localization of genomic breakpoints within the MLL gene and the involved translocation partner genes (TPGs) were determined and novel TPGs identified. All patients were classified according to their gender (852 females and 745 males), age at diagnosis (558 infant, 416 pediatric and 616 adult leukemia patients) and other clinical criteria. Combined data of our study and recently published data revealed a total of 121 different MLL rearrangements, of which 79 TPGs are now characterized at the molecular level. However, only seven rearrangements seem to be predominantly associated with illegitimate recombinations of the MLL gene (~ 90%): AFF1/AF4, MLLT3/AF9, MLLT1/ENL, MLLT10/AF10, ELL, partial tandem duplications (MLL PTDs) and MLLT4/AF6, respectively. The MLL breakpoint distributions for all clinical relevant subtypes (gender, disease type, age at diagnosis, reciprocal, complex and therapy-induced translocations) are presented. Finally, we present the extending network of reciprocal MLL fusions deriving from complex rearrangements.
The novel coronavirus (SARS-CoV-2), identified in China at the end of December 2019 and causing the disease COVID-19, has meanwhile led to outbreaks all over the globe with about 2.2 million confirmed cases and more than 150,000 deaths as of April 17, 2020 [37]. In view of most recent information on testing activity [32], we present here an update of our initial work [4]. In this work, mathematical models have been developed to study the spread of COVID-19 among the population in Germany and to asses the impact of non-pharmaceutical interventions. Systems of differential equations of SEIR type are extended here to account for undetected infections, as well as for stages of infections and age groups. The models are calibrated on data until April 5, data from April 6 to 14 are used for model validation. We simulate different possible strategies for the mitigation of the current outbreak, slowing down the spread of the virus and thus reducing the peak in daily diagnosed cases, the demand for hospitalization or intensive care units admissions, and eventually the number of fatalities. Our results suggest that a partial (and gradual) lifting of introduced control measures could soon be possible if accompanied by further increased testing activity, strict isolation of detected cases and reduced contact to risk groups.
The novel coronavirus (SARS-CoV-2), identified in China at the end of December 2019 and causing the disease COVID-19, has meanwhile led to outbreaks all over the globe, with about 571,700 confirmed cases and about 26,500 deaths as of March 28th, 2020. We present here the preliminary results of a mathematical study directed at informing on the possible application or lifting of control measures in Germany. The developed mathematical models allow to study the spread of COVID-19 among the population in Germany and to asses the impact of non-pharmaceutical interventions.