Artemisinin ameliorates diabetic retinopathy by upregulating CASC2/miR-155/SIRT1 axis

Purpose: To explore the protective effects of artemisinin (Art) against diabetic retinopathy (DR) and the probable mechanism of action.Methods: MIO-M1 cells were treated with high glucose (HG) and Art, and the cells’ proliferative ability was determined using cell counting kit-8 (CCK-8) and 5-ethynyl-2’-deoxyuridine (EdU) assay. The relative levels of inflammatory factors in the culture medium of MIO-M1 cells were determined by enzyme-linked immunosorbent assay (ELISA). while the expression levels of CASC2, miR-155 and Sirtuin1 (SIRT1) in MIO-M1 cells were evaluated by quantitative real-time polymerase chain reaction (qRT-PCR). Interaction of Art with the cell target was assessed using dual-luciferase reporter assay. The role of the CASC2/miR-155/SIRT1 axis in Art-induced protection against the proliferation and inflammation of MIO-M1 cells was evaluated.Results: HG induced elevated proliferation of MIO-M1 cells and production of inflammatory factors, but these effects were countered by Art treatment (p < 0.05). CASC2 and SIRT1 were upregulated, while miR-155 was downregulated in HG-treated MIO-M1 cells; changes in their expressions remained the same following Art treatment. CASC2/miR-155/SIRT1 axis was responsible for the ameliorative effect of Art on HG-treated MIO-M1 cells.Conclusion: Artemisinin treatment inhibits cell activation and production of pro-inflammatory cytokines in HG-induced MIO-M1 cells via CASC2/miR-155/SIRT1 axis. Thus, artemisinin has potentials for development into a therapeutic agent for the management of diabetic retinopathy

Context-dependent pro- and anti-resection roles of ZKSCAN3 in the regulation of fork processing during replication stress

Uncontrolled resection of replication forks under stress can cause genomic instability and influence cancer formation. Extensive fork resection has also been implicated in the chemosensitivity of BReast CAncer gene BRCA-deficient cancers. However, how fork resection is controlled in different genetic contexts and how it affects chromosomal stability and cell survival remains incompletely understood. Here, we report a novel function of the transcription repressor ZKSCAN3 in fork protection and chromosomal stability maintenance under replication stress. We show disruption of ZKSCAN3 function causes excessive resection of replication forks by the exonuclease Exo1 and homologous DNA recombination/repair protein Mre11 following fork reversal. Interestingly, in BRCA1-deficient cells, we found ZKSCAN3 actually promotes fork resection upon replication stress. We demonstrate these anti- and pro-resection roles of ZKSCAN3, consisting of a SCAN box, Kruppel-associated box, and zinc finger domain, are mediated by its SCAN box domain and do not require the Kruppel-associated box or zinc finger domains, suggesting that the transcriptional function of ZKSCAN3 is not involved. Furthermore, despite the severe impact on fork structure and chromosomal stability, depletion of ZKSCAN3 did not affect the short-term survival of BRCA1-proficient or BRCA1-deficient cells after treatment with cancer drugs hydroxyurea, PARPi, or cisplatin. Our findings reveal a unique relationship between ZKSCAN3 and BRCA1 in fork protection and add to our understanding of the relationships between replication fork protection, chromosomal instability, and chemosensitivity

A Simple and Efficient Method for Obtaining the Whole-Range Uniaxial Tensile Properties of Pipeline Steel

To obtain the whole-range true stress-true strain curves of API X65, a method is proposed based on the equal proportion principle and digital images. The tensile elongation was obtained by tracing the gauge points on the specimen surface, and the true strain and true stress of API X65 were calculated according to the formulae. The obtained true stress-true strain curves were validated by a 3-D finite element model. The true stress-true strain curve was set as the input data, while the engineering stress-engineering strain curve was set as the output data. The output data of the finite element model was the same as that of the experiment test. The findings imply that the proposed method could acquire reliable, whole-range true stress-true stain curves. These curves, which depict the material behavior of pipeline steel from initial elongation to fracture, could provide basic data for pipeline defect tolerance limit analysis and fracture assessment

Deep Learning for Mobile Crowdsourcing Techniques, Methods, and Challenges: A Survey

With the ever-increasing popularity of mobile computing technology and the wide adoption of outsourcing strategy in labour-intensive industrial domains, mobile crowdsourcing has recently emerged as a promising resolution for solving complex computational tasks with quick response requirements. However, the complexity of a mobile crowdsourcing task makes it hard to pursue an optimal resolution with limited computing resources, as well as various task constraints. In this situation, deep learning has provided a promising way to pursue such an optimal resolution by training a set of optimal parameters. In the past decades, many researchers have devoted themselves to this hot topic and brought various cutting-edge resolutions. In view of this, we review the current research status of deep learning for mobile crowdsourcing from the perspectives of techniques, methods, and challenges. Finally, we list a group of remaining challenges that call for an intensive study in future research

Deep Learning for Mobile Crowdsourcing Techniques, Methods, and Challenges: A Survey

With the ever-increasing popularity of mobile computing technology and the wide adoption of outsourcing strategy in labour-intensive industrial domains, mobile crowdsourcing has recently emerged as a promising resolution for solving complex computational tasks with quick response requirements. However, the complexity of a mobile crowdsourcing task makes it hard to pursue an optimal resolution with limited computing resources, as well as various task constraints. In this situation, deep learning has provided a promising way to pursue such an optimal resolution by training a set of optimal parameters. In the past decades, many researchers have devoted themselves to this hot topic and brought various cutting-edge resolutions. In view of this, we review the current research status of deep learning for mobile crowdsourcing from the perspectives of techniques, methods, and challenges. Finally, we list a group of remaining challenges that call for an intensive study in future research