Prognostic Factors and Clinical Outcomes of High-Dose Chemotherapy followed by Autologous Stem Cell Transplantation in Patients with Peripheral T Cell Lymphoma, Unspecified: Complete Remission at Transplantation and the Prognostic Index of Peripheral T Cell Lymphoma Are the Major Factors Predictive of Outcome

AbstractHigh-dose chemotherapy followed by autologous stem cell transplantation (HDT/ASCT) offers a rescue option for T cell lymphoma patients with poor prognosis. However, the effectiveness of HDT/ASCT in patients with various peripheral T cell subtypes, optimal transplant timing, and the prognostic factors that predict better outcomes, have not been identified. We retrospectively investigated the clinical outcomes and prognostic factors for HDT/ASCT in 64 Korean patients with peripheral T cell lymphoma, unspecified (PTCL-U) between March 1995 and February 2007. The median age at transplantation was 44 years (range: 15-63 years). According to the age-adjusted International Prognostic Index (a-IPI) and the prognostic index of PTCL (PIT), 8 patients (12.5%) were in the high-risk group and 16 (26.6%) had the 2-3 PIT factors, respectively. After a median follow-up of 29.7 months, the 3-year overall survival (OS) and progression-free survival (PFS) rates were 53.0% ± 7.5% and 44.3% ± 7.0%, respectively. Univariate analysis showed that poor performance status, high lactate dehydrogenase (LDH) levels, high a-IPI score, high PIT classes, failure to achieve complete response (CR) at transplantation, and nonfrontline transplantation were associated with poor OS. Multivariate analysis showed that failure to achieve CR at transplantation (hazard ratio [HR] 2.23; 95% confidence interval [CI] 1.78-7.93) and 2-3 PIT factors (HR 3.76; 95% CI 1.02-5.42) were independent prognostic factors for OS. Failure to achieve CR at transplantation and high PIT are negative predictable factors for survival following HDT/ASCT in patients with PTCL-U

Long non-coding RNA ChRO1 facilitates ATRX/DAXX-dependent H3.3 deposition for transcription-associated heterochromatin reorganization.

Constitutive heterochromatin undergoes a dynamic clustering and spatial reorganization during myogenic differentiation. However the detailed mechanisms and its role in cell differentiation remain largely elusive. Here, we report the identification of a muscle-specific long non-coding RNA, ChRO1, involved in constitutive heterochromatin reorganization. ChRO1 is induced during terminal differentiation of myoblasts, and is specifically localized to the chromocenters in myotubes. ChRO1 is required for efficient cell differentiation, with global impacts on gene expression. It influences DNA methylation and chromatin compaction at peri/centromeric regions. Inhibition of ChRO1 leads to defects in the spatial fusion of chromocenters, and mislocalization of H4K20 trimethylation, Suv420H2, HP1, MeCP2 and cohesin. In particular, ChRO1 specifically associates with ATRX/DAXX/H3.3 complex at chromocenters to promote H3.3 incorporation and transcriptional induction of satellite repeats, which is essential for chromocenter clustering. Thus, our results unveil a mechanism involving a lncRNA that plays a role in large-scale heterochromatin reorganization and cell differentiation.Individual Basic Researcher Program [2018R1D1A1B070 48056 to E.-J.C., 2017R1D1A1B03035883 to J.P.]; Advanced Research Center Program [NRF-2010-0029359 to E.-J.C.]; National Creative Research Laboratory Program [2012R1A3A2048767 to H.-D.Y.]; NRF-2012-Fostering Core Leaders of the Future Basic Science Program through the National Research Foundation of Korea [2012H1A8003093 to J.P.]

Fault Detection of a Roller-Bearing System through the EMD of a Wavelet Denoised Signal

This paper investigates fault detection of a roller bearing system using a wavelet denoising scheme and proper orthogonal value (POV) of an intrinsic mode function (IMF) covariance matrix. The IMF of the bearing vibration signal is obtained through empirical mode decomposition (EMD). The signal screening process in the wavelet domain eliminates noise-corrupted portions that may lead to inaccurate prognosis of bearing conditions. We segmented the denoised bearing signal into several intervals, and decomposed each of them into IMFs. The first IMF of each segment is collected to become a covariance matrix for calculating the POV. We show that covariance matrices from healthy and damaged bearings exhibit different POV profiles, which can be a damage-sensitive feature. We also illustrate the conventional approach of feature extraction, of observing the kurtosis value of the measured signal, to compare the functionality of the proposed technique. The study demonstrates the feasibility of wavelet-based de-noising, and shows through laboratory experiments that tracking the proper orthogonal values of the covariance matrix of the IMF can be an effective and reliable measure for monitoring bearing fault