Liver cT
  , liver T
  , transient elastography (TE) and blood-based biomarkers have independently been shown to predict clinical outcomes but have not been directly compared in a single cohort of patients. Our aim was to compare these tests' prognostic value in a cohort of patients with compensated chronic liver disease.
 
  Patients with unselected compensated liver disease aetiologies had baseline assessments and were followed up for development of clinical outcomes, blinded to the imaging results. The prognostic value of non-invasive liver tests at prespecified thresholds was assessed for a combined clinical endpoint comprising ascites, variceal bleeding, hepatic encephalopathy, hepatocellular carcinoma, liver transplantation and mortality.
 
  One hundred and ninety-seven patients (61% male) with median age of 54years were followed up for 693 patient-years (median (IQR) 43 (26-58) months).  https://www.selleckchem.com/products/dmog.html  The main diagnoses were NAFLD (41%), viral hepatitis (VH, 25%) and alcohol-related liver disease (ArLD; 14%). During follow-up 14 new clinical events, and 11 deaths occurred. Clinical outcomes were predicted by liver cT
  >825ms with HR 9.9 (95% CI 1.29-76.4, P=.007), TE>8kPa with HR 7.8 (95% CI 0.97-62.3, P=.02) and FIB-4>1.45 with HR 4.09 (95% CI 0.90-18.4, P=.05). In analysis taking into account technical failure and unreliability, liver cT
  >825ms could predict clinical outcomes (P=.03), but TE>8kPa could not (P=.4).
 
  We provide further evidence that liver cT
  , TE and serum-based biomarkers can predict clinical outcomes, but when taking into account technical failure/unreliability, TE cut-offs perform worse than those of cT
  and blood biomarkers.
 We provide further evidence that liver cT1 , TE and serum-based biomarkers can predict clinical outcomes, but when taking into account technical failure/unreliability, TE cut-offs perform worse than those of cT1 and blood biomarkers.
  Decitabine-based chemotherapy regimens have shown efficacy in the treatment of elderly patients with acute myeloid leukemia (AML). However, it remains unclear whether any molecular alteration is correlated with the therapeutic effect of such treatment regimens.
 
  Gene mutations were detected using next-generation sequencing, and their impact on survival was investigated in elderly AML patients receiving decitabine-based chemotherapy.
 
  A higher incidence of gene mutations was identified in elderly AML patients than in the younger cohorts. Elderly patients more frequently carried DNMT3A, IDH2, ASXL1, TET2, RUNX1, CEBPA single mutation (CEBPA
  ), and TP53 mutations. Survival analysis showed that DNMT3A, FLT3-ITD, and TP53 mutations were associated with inferior overall survival (OS) and event-free survival (EFS) in younger AML patients receiving standard treatment. However, in elderly patients treated with decitabine-based chemotherapy, FLT3-ITD, and ASXL1 mutations, but not DNMT3A and TP53 mutations, were associated with poor OS and EFS. Moreover, contrary to CEBPA double mutation (CEBPA
  ), CEBPA
  was identified as an unfavorable prognostic factor.
 
  This study comprehensively analyzed the prognostic implications of gene mutations in elderly AML patients under decitabine-based treatment modality. Identification of genetic biomarkers to predict the subgroup of elderly AML patients who can benefit from decitabine-based regimens might have an immediate clinical utility to optimize the treatment of elderly AML patients.
 This study comprehensively analyzed the prognostic implications of gene mutations in elderly AML patients under decitabine-based treatment modality. Identification of genetic biomarkers to predict the subgroup of elderly AML patients who can benefit from decitabine-based regimens might have an immediate clinical utility to optimize the treatment of elderly AML patients.The downregulation of melatonin receptor 1A (MTNR1A) is associated with a range of pathological conditions, including membranous nephropathy. Knowledge of the mechanism underlying MTNR1A expression has been limited to the transcriptional regulation level. Here, RNA interference screening in human kidney cells revealed that heterogeneous nuclear ribonucleoprotein L (hnRNPL) upregulated MTNR1A RNA post-transcriptionally. hnRNPL knockdown or overexpression led to increased or decreased levels of cyclic adenosine monophosphate-responsive element-binding protein phosphorylation, respectively. Molecular studies showed that cytoplasmic hnRNPL exerts a stabilizing effect on the MTNR1A transcript through CA-repeat elements in its coding region. Further studies revealed that the interaction between hnRNPL and MTNR1A serves to protect MNTR1A RNA degradation by the exosome component 10 protein. MTNR1A, but not hnRNPL, displays a diurnal rhythm in mouse kidneys. Enhanced levels of MTNR1A recorded at midnight correlated with robust binding activity between cytoplasmic hnRNPL and the MTNR1A transcript. Both hnRNPL and MTNR1A were decreased in the cytoplasm of tubular epithelial cells from experimental membranous nephropathy kidneys, supporting their clinical relevance. Collectively, our data identified cytoplasmic hnRNPL as a novel player in the upregulation of MTNR1A expression in renal tubular epithelial cells, and as a potential therapeutic target.
  Despite the proven utility of multiparametric magnetic resonance imaging (MRI) in radiation therapy, MRI-guided radiation treatment planning is limited by the fact that MRI does not directly provide the electron density map required for absorbed dose calculation. In this work, a new deep convolutional neural network model with efficient learning capability, suitable for applications where the number of training subjects is limited, is proposed to generate accurate synthetic computed tomography (sCT) images from MRI.
 
  This efficient convolutional neural network (eCNN) is built upon a combination of the SegNet architecture (a 13-layer encoder-decoder structure similar to the U-Net network) without softmax layers and the residual network. Moreover, maxpooling indices and high resolution features from the encoding network were incorporated into the corresponding decoding layers. A dataset containing 15 co-registered MRI-CT pairs of male pelvis (1861 two-dimensional images) were used for training and evaluation of MRI to CT synthesis process using a fivefold cross-validation scheme.