Original Article Neuroprotective effect of functionalized multi-walled carbon nanotubes on spinal cord injury in rats

Abstract: Traumatic injuries to the brain and spinal cord affect a large percentage of the world's population. However, there are currently no effective treatments for these central nervous system (CNS) injuries. In our study, we evaluated the neuroprotective role of functionalized multi-walled carbon nanotubes (MWCNTs) carrying brain derived neurotrophic factor (BNDF), nogo-66 receptor (NgR) and Ras homolog gene family member A (RhoA) in spinal cord injury (SCI). Our results showed that transfection into rat cortical neurons with BDNF-DNA significantly elevated the expression of BDNF both in vitro and in vivo. Meanwhile, transfection with NgR-siRNA and RhoA-siRNA resulted in an obvious down-regulation of NgR and RhoA in neuron cells and in injured spinal cords. In addition, the functionalized MWCNTs carrying BDNF-DNA, NgR-siRNA and RhoA-siRNA exhibited remarkable therapeutic effects on injured spinal cord. Taken together, our study demonstrates that functionalized MWCNTs have a potential therapeutic application on repair and regeneration of the CNS

Clinical characteristics and outcomes in patients with traumatic brain injury in China: a prospective, multicentre, longitudinal, observational study

Background Large-scale studies are required to better characterise traumatic brain injury (TBI) and to identify the most effective treatment approaches for TBI. However, evidence is scarce and mostly originates from high-income countries. We aimed to describe the existing care for patients with TBI and the outcomes in China. Methods The Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) China registry is a prospective, multicentre, longitudinal, observational study done in 56 neurosurgical centres across China. We collected data of patients who were admitted to hospital with a clinical diagnosis of TBI and an indication for CT. Patients who were discharged directly from the emergency room were excluded. The primary endpoint was survival on discharge. Prognostic analyses were applied to identify predictors of mortality. Variations in mortality were compared between centres and provinces within China. Mortality was compared with expected mortality, estimated using the CRASH basic model. This study was registered with ClinicalTrials.gov, NCT02210221. Findings From Dec 22, 2014, to Aug 1, 2017, 13 627 patients with TBI from 56 centres were enrolled in the registry. Data from 13 138 patients from 52 hospitals in 22 provinces of China were analysed. Most patients were male (9782 [74%]), with a median age of 48 years (IQR 33–61). The median Glasgow Coma Scale (GCS) score was 13 (IQR 9–15), and the leading cause of injury was road-traffic incident (6548 [50%]). Overall, 637 (5%) patients died, including 552 (20%) patients with severe TBI. Age, GCS score, injury severity score, pupillary light reflex, CT findings (compressed basal cistern and midline shift ≥5 mm), presence of hypoxia, systemic hypotension, altitude higher than >500 m, and GDP per capita were significantly associated with survival in all patients with TBI. Variation in mortality existed between centres and regions. The expected 14-day mortality was 1116 (13%), but 544 (7%) deaths within 14 days were observed (observed to expected ratio 0·49 [95% CI 0·45–0·53]). Interpretation The results show differences in mortality between centres and regions across China, which indicates potential for identifying best practices through comparative effectiveness research. The risk factors identified in prognostic analyses might contribute to developing benchmarks for assessing quality of care. Funding None

Seizing the window of opportunity to mitigate the impact of climate change on the health of Chinese residents

The health threats posed by climate change in China are increasing rapidly. Each province faces different health risks. Without a timely and adequate response, climate change will impact lives and livelihoods at an accelerated rate and even prevent the achievement of the Healthy and Beautiful China initiatives. The 2021 China Report of the Lancet Countdown on Health and Climate Change is the first annual update of China’s Report of the Lancet Countdown. It comprehensively assesses the impact of climate change on the health of Chinese households and the measures China has taken. Invited by the Lancet committee, Tsinghua University led the writing of the report and cooperated with 25 relevant institutions in and outside of China. The report includes 25 indicators within five major areas (climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement) and a policy brief. This 2021 China policy brief contains the most urgent and relevant indicators focusing on provincial data: The increasing health risks of climate change in China; mixed progress in responding to climate change. In 2020, the heatwave exposures per person in China increased by 4.51 d compared with the 1986–2005 average, resulting in an estimated 92% increase in heatwave-related deaths. The resulting economic cost of the estimated 14500 heatwave-related deaths in 2020 is US$176 million. Increased temperatures also caused a potential 31.5 billion h in lost work time in 2020, which is equivalent to 1.3% of the work hours of the total national workforce, with resulting economic losses estimated at 1.4% of China’s annual gross domestic product. For adaptation efforts, there has been steady progress in local adaptation planning and assessment in 2020, urban green space growth in 2020, and health emergency management in 2019. 12 of 30 provinces reported that they have completed, or were developing, provincial health adaptation plans. Urban green space, which is an important heat adaptation measure, has increased in 18 of 31 provinces in the past decade, and the capacity of China’s health emergency management increased in almost all provinces from 2018 to 2019. As a result of China’s persistent efforts to clean its energy structure and control air pollution, the premature deaths due to exposure to ambient particulate matter of 2.5 μm or less (PM2.5) and the resulting costs continue to decline. However, 98% of China’s cities still have annual average PM2.5 concentrations that are more than the WHO guideline standard of 10 μg/m3. It provides policymakers and the public with up-to-date information on China’s response to climate change and improvements in health outcomes and makes the following policy recommendations. (1) Promote systematic thinking in the related departments and strengthen multi-departmental cooperation. Sectors related to climate and development in China should incorporate health perspectives into their policymaking and actions, demonstrating WHO’s and President Xi Jinping’s so-called health-in-all-policies principle. (2) Include clear goals and timelines for climate-related health impact assessments and health adaptation plans at both the national and the regional levels in the National Climate Change Adaptation Strategy for 2035. (3) Strengthen China’s climate mitigation actions and ensure that health is included in China’s pathway to carbon neutrality. By promoting investments in zero-carbon technologies and reducing fossil fuel subsidies, the current rebounding trend in carbon emissions will be reversed and lead to a healthy, low-carbon future. (4) Increase awareness of the linkages between climate change and health at all levels. Health professionals, the academic community, and traditional and new media should raise the awareness of the public and policymakers on the important linkages between climate change and health.</p

SAR Target Recognition Using cGAN-Based SAR-to-Optical Image Translation

Target recognition in synthetic aperture radar (SAR) imagery suffers from speckle noise and geometric distortion brought by the range-based coherent imaging mechanism. A new SAR target recognition system is proposed, using a SAR-to-optical translation network as pre-processing to enhance both automatic and manual target recognition. In the system, SAR images of targets are translated into optical by a modified conditional generative adversarial network (cGAN) whose generator with a symmetric architecture and inhomogeneous convolution kernels is designed to reduce the background clutter and edge blur of the output. After the translation, a typical convolutional neural network (CNN) classifier is exploited to recognize the target types in translated optical images automatically. For training and testing the system, a new multi-view SAR-optical dataset of aircraft targets is created. Evaluations of the translation results based on human vision and image quality assessment (IQA) methods verify the improvement of image interpretability and quality, and translated images obtain higher average accuracy than original SAR data in manual and CNN classification experiments. The good expansibility and robustness of the system shown in extending experiments indicate the promising potential for practical applications of SAR target recognition