11 research outputs found
P438: ARYL HYDROCARBON RECEPTOR SIGNALLING IN NORMAL HAEMATOPOIETIC (HSC) AND LEUKEMIA STEM CELLS (LSC): DIFFERENTIAL EFFECT OF AHR ANTAGONIST ON LSC COMPARED TO HSC
Multiomics, virtual reality and artificial intelligence in heart failure
Aim: Multiomics delivers more biological insight than targeted investigations. We applied multiomics to patients with heart failure (HF) and reduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography–mass spectrometry and solid-phase microextraction volatilomics in plasma and urine. HFrEF was defined using left ventricular (LV) global longitudinal strain, EF and N-terminal pro hormone BNP. AECG and Echo AI were performed over 5 min, with a subset of patients undergoing a virtual reality mental stress test. Results: A-ECG had similar diagnostic accuracy as N-terminal pro hormone BNP for HFrEF (area under the curve = 0.95, 95% CI: 0.85–0.99), and correlated with global longitudinal strain (r = -0.77, p < 0.0001), while Echo AI-generated measurements correlated well with manually measured LV end diastolic volume r = 0.77, LV end systolic volume r = 0.8, LVEF r = 0.71, indexed left atrium volume r = 0.71 and indexed LV mass r = 0.6, p < 0.005. AI-LVEF and other HFrEF biomarkers had a similar discrimination for HFrEF (area under the curve AI-LVEF = 0.88; 95% CI: -0.03 to 0.15; p = 0.19). Virtual reality mental stress test elicited arrhythmic biomarkers on AECG and indicated blunted autonomic responsiveness (alpha 2 of RR interval variability, p = 1 × 10-4) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF. </jats:p
The MLL recombinome of acute leukemias in 2013
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