Multiple myeloma (MM) is still an incurable malignancy of plasma cells. Proteasome inhibitors (PIs) work as the backbone agent and have greatly improved the outcome in majority of newly diagnosed patients with myeloma. However, drug resistance remains the major obstacle causing treatment failure in clinical practice. Here, we investigated the effects of Indirubin-3’-monoxime (I3MO), one of the derivatives of Indirubin, in the treatment of MM. MM patient primary samples and human cell lines were examined. I3MO effects on myeloma treatment and the underling molecular mechanisms were investigated via in vivo and in vitro study. Our results demonstrated the anti-MM activity of I3MO in both drug- sensitive and -resistance MM cells. I3MO sensitizes MM cells to bortezomib-induced apoptosis. Mechanistically, I3MO acts as a multifaceted regulator of cell death, which induced DNA damage, cell cycle arrest, and abrogates NF-κB activation. I3MO efficiently down-regulated USP7 expression, promoted NEK2 degradation, and suppressed NF-κB signaling in MM. Our study reported that I3MO directly bound with and caused the down-regulation of PA28γ (PSME3), and PA200 (PSME4), the proteasome activators. Knockdown of PSME3 or PSME4 caused the inhibition of proteasome capacity and the overload of paraprotein, which sensitizes MM cells to bortezomib-mediated growth arrest. Clinical data demonstrated that PSME3 and PSME4 are over-expressed in relapsed/refractory MM (RRMM) and associated with inferior outcome. Altogether, our study indicates that I3MO is agent triggering proteasome inhibition and represents a promising therapeutic strategy to improve patient outcome in MM. A full list of funding can be found in the acknowledgements.

Background: The recently released Myeloma Response Assessment and Diagnosis System (MY-RADS) for multiple myeloma (MM) evaluation by whole-body MRI (WB-MRI) describes the total burden score. Howeve...

China is experiencing severe environmental degradation, particularly air pollution. To explore whether air pollutants are spatially correlated (i.e., trans-boundary effects) and to analyse the main contributing factors, this research investigates the annual concentration of the Air Quality Index (AQI) and 13 polluting sectors in 30 provinces and autonomous regions across China. Factor analysis, the linear regression model and the spatial auto-regression (SAR) model are employed to analyse the latest data in 2014. Several important findings are derived. Firstly, the global Moran’s I test reveals that the AQI of China shows a distinct positive spatial correlation. The local Moran’s I test shows that significant high–high AQI agglomeration regions are found around the Beijing–Tianjin–Hebei area and the regions of low–low AQI agglomeration all locate in south China, including Yunnan, Guangxi and Fujian. Secondly, the effectiveness of the SAR model is much better than that of the linear regression model, with a significantly improved R-squared value from 0.287 to 0.705. A given region’s AQI will rise by 0.793% if the AQI of its ambient region increases by 1%. Thirdly, car ownership, steel output, coke output, coal consumption, built-up area, diesel consumption and electric power output contribute most to air pollution according to AQI, whereas fuel oil consumption, caustic soda output and crude oil consumption are inconsiderably accountable in raising AQI. Fourthly, the air quality in Beijing and Tianjin is under great exogenous influence from nearby regions, such as Hebei’s air pollution, and cross-boundary and joint efforts must be committed by the Beijing–Tianjin–Hebei region in order to control air pollution.

As the most widely-used data form in the field of finance, the open-high-low-close (OHLC) data is being collected by all kinds of financial trading systems all the time. This paper puts forward a pseudo-principal component analysis (PCA) for multi-dimensional OHLC data, which can extract their useful information in a comprehensible way for visualization and easy interpretation. Firstly, a novel feature-based representation for OHLC data is proposed, which contains fruitful and explicit economic implications. Next, we define a full set of numerical characteristics and variance-covariance structures for the feature-based OHLC data. Then, the pseudo-PCA procedure for OHLC data is deduced based on the proposed algebraic operators. Finally, the effectiveness and interpretability of the proposed pseudo-PCA method are verified through finite simulations and three typical empirical experiments. This paper enriches the application scenarios of classical PCA and contributes to the multivariate statistical modeling of symbolic data. The proposed applications can serve as models for related studies.

This study explores the factors influencing the migration intentions of the new generation of migrant workers from the perspective of employment quality. Utilizing differential analysis, correlation analysis, and the Partial Least Squares Structural Equation Modeling (PLS-SEM) model, this study analyzes data on hukou transfer intention obtained through an exploratory questionnaire survey. The results indicate significant differences in migration intentions among new-generation migrant workers differentiated by job industry, job position, gender, household registration type, and housing type. Additionally, age, the number of local family members, and housing satisfaction are strongly correlated with migration intentions. Path analysis reveals that employment stability, income–expenditure match, and social integration significantly positively affect migration intentions. This research provides a unique perspective on employment quality and offers theoretical foundations for policies related to migrant workers’ hukou transfer intention.