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The study of molecular mechanism driving osteoarticular diseases like osteoarthritis or osteoporosis is impaired by the low accessibility to mesenchymal stem cells (MSC) from healthy donors (HD) for differential multi-omics analysis. Advances in cell reprogramming have, however, provided both a new source of human cells for laboratory research and a strategy to erase epigenetic marks involved in cell identity and the development of diseases. To unravel the pathological signatures on the MSC at the origin of cellular drifts during the formation of bone and cartilage, we previously developed iPSC from MSC of osteoarthritis donors. Here we present the derivation of three iPSCs from healthy age matched donors to model the disease and further identify (epi)genomic signatures of the pathology.

Far-infrared (FIR) irradiation inhibits adipogenic differentiation of tonsil-derived mesenchymal stem cells (TMSCs) by activating Ca

-dependent protein phosphatase 2B (PP2B), but it stimulates osteogenic differentiation in a PP2B-independent pathway. We investigated the potential involvement of transient receptor potential vanilloid (TRPV) channels, a well-known Ca

-permeable channel, in the effects of FIR irradiation on adipogenic or osteogenic differentiation of TMSCs.

TMSCs, in the absence or presence of activators or inhibitors, were exposed to FIR irradiation followed by adipogenic or osteogenic differentiation, which was assessed using Oil red O or Alizarin red S staining, respectively. RT-PCR, qRT-PCR, and Western blotting were used to determine gene and protein expression of calcium channels and adipocyte-specific markers.

Treatment with the calcium ionophore ionomycin simulated the inhibitory effect of FIR irradiation on adipogenic differentiation but had no effect on osteogenic differentiation, indicating the involvement of intracellular Ca

in adipogenic differentiation. check details Inhibition of pan-TRP channels using ruthenium red reversed the FIR irradiation-induced inhibition of adipogenic differentiation. Among the TRP channels tested, inhibition of the TRPV2 channel by tranilast or siRNA against TRPV2 attenuated the inhibitory effect of FIR irradiation on adipogenic differentiation, accompanied by a decrease in intracellular Ca

levels. By contrast, activation of the TRPV2 channel by probenecid simulated FIR irradiation-induced inhibition of adipogenic differentiation. Expectedly, the stimulatory effect of FIR irradiation on osteogenic differentiation was independent of the TRPV2 channel.

Our data demonstrate that the TRPV2 channel is a sensor/receptor for the inhibited adipogenic differentiation of TMSCs associated with FIR irradiation.

Our data demonstrate that the TRPV2 channel is a sensor/receptor for the inhibited adipogenic differentiation of TMSCs associated with FIR irradiation.Focal Segmental Glomerular Sclerosis (FSGS) is a glomerular disease which can be classified into primary, secondary, genetic, and unknown forms. WT1 mutation has been shown to be associated with this disorder. Recently, we identified a mutation in the Zinc finger C2H2 domain of WT1 gene in a patient with FSGS who also carried a family history of end-stage renal disease (ESRD). The Peripheral Blood Mononuclear Cells (PBMCs) of the patient were obtained and a line of induced pluripotent stem cells (iPSCs) was successfully generated. The iPSC line will be useful for further study of the pathogenesis and drug screening for FSGS.Germline mutations of CHEK2 have been reported in various types of disease including breast cancer, ovarian cancer, colorectal cancer and prostate cancer. We generated two iPSC lines ZNHi001-A and ZNHi001-B from a prostate cancer patient carrying germline mutation in CHEK2 (c.667C＞T, also p.R223C) which may increase the risk of prostate cancer. Pluripotency and multi-lineage differentiation capacity of the two iPSC lines were confirmed by gene expression and teratoma assay. The generated iPSC lines carrying specific CHEK2 mutation might be a useful resource to study the pathogenic mechanism and develop potential therapeutic strategy of prostate cancer.Epilepsy is a neurological brain disorder that affects ∼75 million people worldwide. Predicting epileptic seizures holds great potential for improving the quality of life of people with epilepsy, but seizure prediction solely from the Electroencephalogram (EEG) is challenging. Classical machine learning algorithms and a variety of feature engineering methods have become a mainstay in seizure prediction, yet performance has been variable. In this work, we first propose an efficient data pre-processing method that maps the time-series EEG signals into an image-like format (a “scalogram”) using continuous wavelet transform. We then develop a novel convolution module named “semi-dilated convolution” that better exploits the geometry of wavelet scalograms and nonsquare-shape images. Finally, we propose a neural network architecture named “semi-dilated convolutional network (SDCN)” that uses semi-dilated convolutions to solely expand the receptive field along the long dimension (image width) while maintaining high resolution along the short dimension (image height). Results demonstrate that the proposed SDCN architecture outperforms previous seizure prediction methods, achieving an average seizure prediction sensitivity of 98.90% for scalp EEG and 88.45-89.52% for invasive EEG.Attention-based convolutional neural network (CNN) models are increasingly being adopted for speaker and language recognition (SR/LR) tasks. These include time, frequency, spatial and channel attention, which can focus on useful time frames, frequency bands, regions or channels while extracting features. However, these traditional attention methods lack the exploration of complex information and multi-scale long-range speech feature interactions, which can benefit SR/LR tasks. To address these issues, this paper firstly proposes mixed-order attention (MOA) for low frame-level speech features to gain the finest grain multi-order information at higher resolution. We then combine that with a non-local attention (NLA) mechanism and a dilated residual structure to balance fine grained local detail with convolution from multi-scale long-range time/frequency regions in feature space. The proposed dilated mixed-order non-local attention network (D-MONA) exploits the detail available from the first and the second-order feature attention analysis, but achieves this over a much wider context than purely local attention.