Objective. To examine the efficacy of popular Chinese herbs used in a traditional Chinese medicine (TCM) combination of Ganoderma lucidum and San Miao San (SMS), with purported diverse health benefits including antioxidant properties in rheumatoid arthritis (RA). Methods. We randomly assigned 32 patients with active RA, despite disease-modifying antirheumatic drugs, to TCM and 33 to placebo in addition to their current medications for 24 weeks. The TCM group received G lucidum (4 gm) and SMS (2.4 gm) daily. The primary outcome was the number of patients achieving American College of Rheumatology (ACR) 20% response and secondary outcomes included changes in the ACR components, plasma levels, and ex vivo-induced cytokines and chemokines and oxidative stress markers. Results. Eighty-nine percent completed the 24-week study. Fifteen percent in the TCM group compared with 9.1% in the placebo group achieved ACR20 (P > 0.05). Pain score and patient's global score improved significantly only in the TCM group. The percentage, absolute counts, and CD4؉/CD8؉/natural killer/B lymphocytes ratio were unchanged between groups. CD3, CD4, and CD8 lymphocyte counts and markers of inflammation including plasma interleukin-18 (IL-18), interferon-␥ (IFN␥)-inducible protein 10, monocyte chemoattractant protein 1, monokine induced by IFN␥, and RANTES were unchanged. However, in an ex vivo experiment, the percentage change of IL-18 was significantly lower in the TCM group. Thirteen patients reported 22 episodes (14 in placebo group and 8 in TCM group) of mild adverse effects. Conclusion. G lucidum and San Miao San may have analgesic effects for patients with active RA, and were generally safe and well tolerated. However, no significant antioxidant, antiinflammatory, or immunomodulating effects could be demonstrated.

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Improving our comprehension of the weight and spatial distribution of urban built environment stocks is essential for informing urban resource, waste, and environmental management, but this is often hampered by inaccuracy and inconsistency of the typology and material composition data of buildings and infrastructure. Here, we have integrated big data mining and analytics techniques and compiled a local material composition database to address these gaps, for a detailed characterization of the quantity, quality, and spatial distribution (in 500 m × 500 m grids) of the urban built environment stocks in Beijing in 2018. We found that 3621 megatons (140 ton/cap) of construction materials were accumulated in Beijing's buildings and infrastructure, equaling to 1141 Mt of embodied greenhouse gas emissions. Buildings contribute the most (63% of total, roughly half in residential and half in nonresidential) to the total stock and the subsurface stocks account for almost half. Spatially, the belts between 3 and 7 km from city center (approximately 5 t/m 2 ) and commercial grids (approximately 8 t/m 2 ) became the densest. Correlation analyses between material stocks and socioeconomic factors at a high resolution reveal an inverse relationship between building and road stock densities and suggest that Beijing is sacrificing skylines for space in urban expansion. Our results demonstrate that harnessing emerging big data and analytics (e.g., point of interest data and web crawling) could help realize more spatially refined characterization of built environment stocks and highlight the role of such information and urban planning in urban resource, waste, and environmental strategies.

These results suggest that Yunzhi-Danshen can exert an immunomodulating effect in alleviating lymphopenia during radiotherapy in NPC patients.

Background: Currently, some advanced treatments such as Levodopa-Carbidopa intestinal gel infusion (LCIG), deep-brain stimulation (DBS), and subcutaneous apomorphine infusion have become alternative strategies for advanced Parkinson's disease (PD). However, which treatment is better for individual patients remains unclear. This review aims to compare therapeutic effects of motor and/or non-motor symptoms of advanced PD patients between LCIG and DBS. Methods: We manually searched electronic databases (PubMed, Embase, Cochrane Library) and reference lists of included articles published until April 04, 2019 using related terms, without language restriction. We included case-controlled cohort studies and randomized-controlled trials, which directly compared differences between LCIG and DBS. The Newcastle-Ottawa scale (NOS), proposed by the Cochrane Collaboration, was utilized to assess the quality of the included studies. Two investigators independently extracted data from each trial. Pooled standard-mean differences (SMDs) and relative risks (RRs) with 95% confidence intervals (CIs) were calculated by meta-analysis. Outcomes were grouped according to the part III and part IV of the Unified Parkinson Disease Rating Scale (UPDRS) and adverse events. We also descriptively reviewed some data, which were unavailable for statistical analysis. Results: This review included five cohort trials of 257 patients for meta-analysis. There were no significant differences between LCIG and subthalamic nucleus deep-brain stimulation (STN-DBS) on UPDRS-III and adverse events comparisons: UPDRS-III (pooled SMDs = 0.200, 95% CI: −0.126–0.527, P = 0.230), total adverse events (pooled RRs = 1.279, 95% CI: 0.983–1.664, P = 0.067), serious adverse events (pooled RRs = 1.539, 95% CI: 0.664–3.566, P = 0.315). Notably, the improvement of UPDRS-IV was more significant in STN-DBS groups: pooled SMDs = 0.857, 95% CI: 0.130–1.584, P = 0.021. However, the heterogeneity was moderate for UPDRS-IV ( I 2 = 73.8%). Conclusion: LCIG has comparable effects to STN-DBS on motor function for advanced PD, with acceptable tolerability. More large, well-designed trials are needed to assess the comparability of LCIG and STN-DBS in the future.

Urban subway system, as an important type of urban transportation infrastructure, can provide mass mobility service and help address urban sustainability challenges such as traffic congestion and air pollution. The continuous construction of subways, however, causes large amounts of construction materials and embodied greenhouse gas (GHG) emissions. In this study, we characterized the patterns of subway development, construction material stocks, and embodied emissions covering all 219 cities in the world in which subways are found by July 2020. The global subway length reached 16,419 km in 2020, and the construction material stocks amounted to 2.5 gigatons, equaling to an embodied emission of 560 megatons. In particular, China’s subway system contributes to ~40% of the total global stocks, with a pattern of moderate and steady stocks growth before 2010 and a rapid expansion afterwards, implying the late-development advantages and infrastructure-based urbanization mode. Our results demonstrated that identifying the spatiotemporal characteristics of subway materials stocks development is imperative for benchmarking future resource demand, informing sustainable subway planning, prospecting urban mining and waste management opportunities and challenges, and mitigating the associated environmental impacts for global GHG emission reduction.

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City image in general refers to the perception, the feeling, and the opinion of a city,which contributes great importance to urban management, urban planning, urban cultural perceptions, and tourism resource development. Traditionally, city image is often inferred by the 'five-element' model of physical factors while lacking the consideration of subjective perception. With the rising penetration of smart mobile devices and social media, massive data of location-related texts has been generated for a variety of urban areas. The accessibility to the big data leads to a new approach of understanding the subjective perception of city image, which is important since the new approach takes the subjective heterogeneity into account. Based on the Beijing's Weibo (microblog) data in the year of 2016, we use a random forest model to categorize user backgrounds into locals and non-locals. Meanwhile, spatial clustering is applied to identify hotspots. Then two text analysis methods-term frequency-inverse document frequency (TF-IDF) and latent Dirichlet allocation (LDA)-are adopted to abstract topics regarding the different geographical hotspots in the city across the different groups of individuals. Our research shows text mining on geotagged big data for city image makes it possible to accommodate the heterogeneity of the activities of different groups of people and to understand their preferences for different points of interests in the city, and thereby reveals the socio-cultural and functional features for the city. INDEX TERMS City image, geotagged data, hotspots, social media, text analysis.