Prosthetic heart valve (PHV) dysfunction is rare but potentially life-threatening. Although often challenging, establishing the exact cause of PHV dysfunction is essential to determine the appropriate treatment strategy. In clinical practice, a comprehensive approach that integrates several parameters of valve morphology and function assessed with 2D/3D transthoracic and transoesophageal echocardiography is a key to appropriately detect and quantitate PHV dysfunction. Cinefluoroscopy, multidetector computed tomography, cardiac magnetic resonance imaging, and to a lesser extent, nuclear imaging are complementary tools for the diagnosis and management of PHV complications. The present document provides recommendations for the use of multimodality imaging in the assessment of PHVs.

Epstein-Barr virus (EBV) infection is ubiquitous worldwide and is associated with multiple cancers, including nasopharyngeal carcinoma (NPC). The importance of EBV viral genomic variation in NPC development and its striking epidemic in southern China has been poorly explored. Through large-scale genome sequencing of 270 EBV isolates and two-stage association study of EBV isolates from China, we identified two non-synonymous EBV variants within BALF2 strongly associated with the risk of NPC (odds ratio (OR) = 8.69, P=9.69×10−25 for SNP 162476_C; OR = 6.14, P=2.40×10−32 for SNP 163364_T). The cumulative effects of these variants contributed to 83% of the overall risk of NPC in southern China. Phylogenetic analysis of the risk variants revealed a unique origin in Asia, followed by clonal expansion in NPC-endemic regions. Our results provide novel insights into NPC endemic in southern China and also enable the identification of high-risk individuals for NPC prevention.

Abstract. Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m land-cover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the CCI_LC (Climate Change Initiative Global Land Cover) land-cover and MCD43A4 NBAR products (MODIS Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance). Secondly, a local adaptive random forest model was built for each 5∘×5∘ geographical tile by using the multi-temporal Landsat spectral and texture features and the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile. Lastly, the GLC_FCS30-2015 was validated using three different validation systems (containing different land-cover details) using 44 043 validation samples. The validation results indicated that the GLC_FCS30-2015 achieved an overall accuracy of 82.5 % and a kappa coefficient of 0.784 for the level-0 validation system (9 basic land-cover types), an overall accuracy of 71.4 % and kappa coefficient of 0.686 for the UN-LCCS (United Nations Land Cover Classification System) level-1 system (16 LCCS land-cover types), and an overall accuracy of 68.7 % and kappa coefficient of 0.662 for the UN-LCCS level-2 system (24 fine land-cover types). The comparisons against other land-cover products (CCI_LC, MCD12Q1, FROM_GLC, and GlobeLand30) indicated that GLC_FCS30-2015 provides more spatial details than CCI_LC-2015 and MCD12Q1-2015 and a greater diversity of land-cover types than FROM_GLC-2015 and GlobeLand30-2010. They also showed that GLC_FCS30-2015 achieved the best overall accuracy of 82.5 % against FROM_GLC-2015 of 59.1 % and GlobeLand30-2010 of 75.9 %. Therefore, it is concluded that the GLC_FCS30-2015 product is the first global land-cover dataset that provides a fine classification system (containing 16 global LCCS land-cover types as well as 14 detailed and regional land-cover types) with high classification accuracy at 30 m. The GLC_FCS30-2015 global land-cover products produced in this paper are free access at https://doi.org/10.5281/zenodo.3986872 (Liu et al., 2020).

In this proof-of-concept study, we have demonstrated the feasibility of using 3D printed tissue-mimicking phantoms to quantitatively assess the post-TAVR aortic root strain in vitro. A novel indicator of the post-TAVR annular strain unevenness, the annular bulge index, outperformed the other established variables and achieved a high level of accuracy in predicting post-TAVR PVL, in terms of its occurrence, severity, and location.

BackgroundUric acid (UA) is a complex phenotype influenced by both genetic and environmental factors as well as their interactions. Current genome-wide association studies (GWASs) have identified a variety of genetic determinants of UA in Europeans; however, such studies in Asians, especially in Chinese populations remain limited.MethodsA two-stage GWAS was performed to identify single nucleotide polymorphisms (SNPs) that were associated with serum uric acid (UA) in a Chinese population of 12,281 participants (GWAS discovery stage included 1452 participants from the Dongfeng-Tongji cohort (DFTJ-cohort) and 1999 participants from the Fangchenggang Area Male Health and Examination Survey (FAMHES). The validation stage included another independent 8830 individuals from the DFTJ-cohort). Affymetrix Genome-Wide Human SNP Array 6.0 chips and Illumina Omni-Express platform were used for genotyping for DFTJ-cohort and FAMHES, respectively. Gene-environment interactions on serum UA levels were further explored in 10,282 participants from the DFTJ-cohort.ResultsBriefly, we identified two previously reported UA loci of SLC2A9 (rs11722228, combined P = 8.98 × 10-31) and ABCG2 (rs2231142, combined P = 3.34 × 10-42). The two independent SNPs rs11722228 and rs2231142 explained 1.03% and 1.09% of the total variation of UA levels, respectively. Heterogeneity was observed across different populations. More importantly, both independent SNPs rs11722228 and rs2231142 were nominally significantly interacted with gender on serum UA levels (P for interaction = 4.0 × 10-2 and 2.0 × 10-2, respectively). The minor allele (T) for rs11722228 in SLC2A9 has greater influence in elevating serum UA levels in females compared to males and the minor allele (T) of rs2231142 in ABCG2 had stronger effects on serum UA levels in males than that in females.ConclusionsTwo genetic loci (SLC2A9 and ABCG2) were confirmed to be associated with serum UA concentration. These findings strongly support the evidence that SLC2A9 and ABCG2 function in UA metabolism across human populations. Furthermore, we observed these associations are modified by gender.

OBJECTIVE:The aim of this study was to investigate the serum level of microRNA (miR)-21 in patients with osteosarcoma and its correlation with chemosensitivity and prognosis. METHODS: miR-21 levels in sera from 65 patients with osteosarcoma and 30 healthy controls were measured by real-time reverse transcription-polymerase chain reaction. Correlations between serum miR-21 and clinicopathological features in patients with osteosarcoma were determined. The prognostic significance of serum miR-21 was assessed using a Cox proportional hazards model. RESULTS: The serum level of miR-21 was significantly higher in patients with osteosarcoma than in control subjects. High serum miR-21 was significantly correlated with advanced Enneking stage and chemotherapeutic resistance. Univariate and multivariate analyses for overall survival showed that upregulation of serum miR-21 was an independent, unfavourable prognostic factor for patients with osteosarcoma (hazard ratio, 2.325). CONCLUSIONS: miR-21 might be a good candidate for a therapeutic target, and a potential biomarker for the prediction of chemotherapeutic sensitivity and prognosis in patients with osteosarcoma.

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