LncRNA Malat1 regulates iPSC-derived ß-cell differentiation by targeting the miR-15b-5p/Ihh axis.

BACKGROUND: Differentiation of induced pluripotent stem cells (iPSCs)-derived ß-like cells is a novel strategy for treatment of type 1 diabetes. Elucidation of the regulatory mechanisms of long noncoding RNAs (lncRNAs) in ß-like cells derived from iPSCs is important for understanding the development of the pancreas and pancreatic ß-cells and may improve the quality of ß-like cells for stem cell therapy. METHODS: ß-like cells were derived from iPSCs in a three-step protocol. RNA sequencing and bioinformatics analysis were carried out to screen the differentially expressed lncRNAs and identify the putative target genes separately. LncRNA Malat1 was chosen for further research. Series of loss and gain of functions experiments were performed to study the biological function of LncRNA Malat1. Quantitative real-time PCR (qRT-PCR), Western blot (WB) analysis and immunofluorescence (IF) staining were carried out to separately detect the functions of pancreatic ß-cells at the mRNA and protein levels. Cytoplasmic and nuclear RNA fractionation and fluorescence in situ hybridization (FISH) were used to determine the subcellar location of lncRNA Malat1 in ß-like cells. Enzyme-linked immunosorbent assays (ELISAs) were performed to examine the differentiation and insulin secretion of ß-like cells after stimulation with different glucose concentrations. Structural interactions between lncRNA Malat1 and miR-15b-5p and between miR-15b-5p/Ihh were detected by dual luciferase reporter assays (LRAs). RESULTS: We found that the expression of lncRNA Malat1 declined during differentiation, and overexpression (OE) of lncRNA Malat1 notably impaired the differentiation and maturation of ß-like cells derived from iPSCs in vitro and in vivo. Most importantly, lncRNA Malat1 could function as a competing endogenous RNA (ceRNA) of miR-15b-5p to regulate the expression of Ihh according to bioinformatics prediction, mechanistic analysis and downstream experiments. CONCLUSION: This study established an unreported regulatory network of lncRNA Malat1 and the miR-15b-5p/Ihh axis during the differentiation of iPSCs into ß-like cells. In addition to acting as an oncogene promoting tumorigenesis, lncRNA Malat1 may be an effective and novel target for treatment of diabetes in the future.

Focused low-intensity pulsed ultrasound alleviates osteoarthritis via restoring impaired FUNDC1-mediated mitophagy.

Mitophagy is critical for maintaining proper cellular functions, and it contributes to the onset and progression of osteoarthritis (OA). A recent study showed that focused low-intensity pulsed ultrasound (FLIPUS) could activate mitophagy, but the molecular mechanism remains unclear. This study aimed to elucidate the chondroprotective effects of FLIPUS in OA and the regulatory effects on FUN14-domain containing 1 (FUNDC1-mediated mitophagy. In vitro, FLIPUS improved inflammatory response, anabolism, and catabolism in interleukin (IL)-1ß-induced OA chondrocytes. The chondroprotective effects of FLIPUS were attributed to promoting the expression of phosphoglycerate mutase 5 (PGAM5) and the dephosphorylation of FUNDC1 at serine 13 (Ser13), as well as promoting the mitophagy process. In vivo, FLIPUS reduced the cartilage degeneration and apoptosis and reversed the change of anabolic- and catabolic-related proteins in destabilized medial meniscus (DMM)-induced mouse model. Thus, the study indicates that FLIPUS exhibits a chondroprotective effect via activating impaired FUNDC1-mediated mitophagy.

Efficacy and safety of focused low-intensity pulsed ultrasound versus pulsed shortwave diathermy on knee osteoarthritis: a randomized comparative trial.

The aim of this study was to compare the efficacy and safety of focused low-intensity pulsed ultrasound (FLIPUS) with pulsed shortwave diathermy (PSWD) in subjects with painful knee osteoarthritis (OA). In a prospective randomized trial, 114 knee OA patients were randomly allocated to receive FLIPUS or PSWD therapy. The primary outcome was the change from baseline in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total scores. Secondary outcomes included the numerical rating scale (NRS) for pain assessment, time up and go (TUG) test, active joint range of motion (ROM) test, and Global Rating of Change (GRC) scale. Data were collected at baseline, 12 days, 12 weeks and 24 weeks. Patients receiving FLIPUS therapy experienced significantly greater improvements in the WOMAC total scores than patients receiving PSWD therapy at 12 days (mean difference, - 10.50; 95% CI - 13.54 to - 7.45; P = 0.000). The results of the NRS, TUG test, ROM test and GRC scale showed that participants treated with FLIPUS reported less pain and better physical function and health status than those treated with PSWD at 12 days (P = 0.011, P = 0.005, P = 0.025, P = 0.011, respectively). Furthermore, patients in the FLIPUS group showed significant improvements in the WOMAC total scores and NRS scores at 12 weeks (mean difference, - 7.57; 95% CI - 10.87 to - 4.26; P = 0.000 and - 1.79; 95% CI - 2.11 to - 1.47, respectively) and 24 weeks (mean difference, - 6.96; 95% CI - 10.22 to - 3.71; P = 0.000 and - 1.37; 95% CI - 1.64 to - 0.96; P = 0.000, respectively) of follow-up. There were no adverse events during or after the interventions in either group. This study concluded that both FLIPUS and pulsed SWD are safe modalities, and FLIPUS was more effective than PSWD in alleviating pain and in improving dysfunction and health status among subjects with knee OA in the short term.Trial registration: Chinese Clinical Trial Registry, ChiCTR2000032735. Registered 08/05/2020, http://www.chictr.org.cn/showproj.aspx?proj=53413 .

Inter-Level Feature Balanced Fusion Network for Street Scene Segmentation.

Semantic segmentation, as a pixel-level recognition task, has been widely used in a variety of practical scenes. Most of the existing methods try to improve the performance of the network by fusing the information of high and low layers. This kind of simple concatenation or element-wise addition will lead to the problem of unbalanced fusion and low utilization of inter-level features. To solve this problem, we propose the Inter-Level Feature Balanced Fusion Network (IFBFNet) to guide the inter-level feature fusion towards a more balanced and effective direction. Our overall network architecture is based on the encoder-decoder architecture. In the encoder, we use a relatively deep convolution network to extract rich semantic information. In the decoder, skip-connections are added to connect and fuse low-level spatial features to restore a clearer boundary expression gradually. We add an inter-level feature balanced fusion module to each skip connection. Additionally, to better capture the boundary information, we added a shallower spatial information stream to supplement more spatial information details. Experiments have proved the effectiveness of our module. Our IFBFNet achieved a competitive performance on the Cityscapes dataset with only finely annotated data used for training and has been greatly improved on the baseline network.

Children's fear in traffic and its association with pedestrian decisions.

INTRODUCTION: Research on risk for child pedestrian injury risk focuses primarily on cognitive risk factors, but emotional states such as fear may also be relevant to injury risk. The current study examined children's perception of fear in various traffic situations and the relationship between fear perception and pedestrian decisions. METHOD: 150 children aged 6-12-years old made pedestrian decisions using a table-top road model. Their perceived fear in the pedestrian context was assessed. RESULTS: Children reported greater emotional fear when they faced quicker traffic, shorter distances from approaching traffic, and red rather than green traffic signals. Children who were more fearful made safer pedestrian decisions in more challenging traffic situations. However, when the least risky traffic situation was presented, fear was associated with more errors in children's pedestrian decisions: fearful children failed to cross the street when they could have done so safely. Perception of fear did not vary by child age, although safe pedestrian decisions were more common among the older children. CONCLUSIONS: Children's emotional fear may predict risk-taking in traffic. When traffic situations are challenging to cross within, fear may appropriately create safer decisions. However, when the traffic situation is less risky, feelings of fear could lead to excessive caution and inefficiency. Practical applications: Child pedestrian safety interventions may benefit by incorporating activities that introduce realistic fear of traffic risks into broader safety lessons.

Whole and Part Adaptive Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition.

Spatiotemporal graph convolution has made significant progress in skeleton-based action recognition in recent years. Most of the existing graph convolution methods take all the joints of the human skeleton as the overall modeling graph, ignoring the differences in the movement patterns of various parts of the human, and cannot well connect the relationship between the different parts of the human skeleton. To capture the unique features of different parts of human skeleton data and the correlation of different parts, we propose two new graph convolution methods: the whole graph convolution network (WGCN) and the part graph convolution network (PGCN). WGCN learns the whole scale skeleton spatiotemporal features according to the movement patterns and physical structure of the human skeleton. PGCN divides the human skeleton graph into several subgraphs to learn the part scale spatiotemporal features. Moreover, we propose an adaptive fusion module that combines the two features for multiple complementary adaptive fusion to obtain more effective skeleton features. By coupling these proposals, we build a whole and part adaptive fusion graph convolution neural network (WPGCN) that outperforms previous state-of-the-art methods on three large-scale datasets: NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400.