Worldwide productivity and research trend of publications concerning electroactive materials and spinal cord injury: A bibliometric study

Purpose: We investigated the current state and trends in the area during the previous 10 years using bibliometric approaches to evaluate the global scientific output of research on electroactive materials and spinal cord injury.Methods: Studies on spinal cord injury in electroactive materials that were published between 2012 and 2022 were located using the Web of science (WOS) datebase. The software programs bibliometrix R-package and CiteSpace were used to do quantitative analyses of annual publications, nation, author, institution, journal source, co-cited references, and keywords. The studies were categorized by the research’s main points using a qualitative analysis, and publications having more than 10 citations each year.Results: In the final analysis, 1,330 relevant papers or reviews were included. There is an increased tendency in both the average annual citation rate and the number of publications in the discipline. The United States and the University of Toronto are the countries and institutions that have contributed the most to this discipline, respectively. The majority of authors are from the China and United States. Zhang Y is the author with the most published articles and holds the top position in the cited author h-index species. The journal with the highest number of published articles is “Disability and rehabilitation”; the journal is divided into four main areas including physics, materials, chemistry, molecular, and biology. The keyword analysis revealed a shift in research hotspots from schwann cell, fracture, and urinary disorders to carbon-based materials, functional recovery, and surgery. Analysis of qualitative data revealed that the role and mechanism of injectable conductive hydrogels in spinal cord healing after damage is a hot topic of current study, with the mechanism primarily focusing on the inhibition of oxidative stress (Nrf2) and apoptosis (Casepase 3).Conclusion: Our bibliometric analysis indicates that research on electroactive materials for spinal cord injury remains an active field of study. Moreover, contemporary research is concentrated on carbon-based materials, functional rehabilitation, and surgery

Real-Time Neural Signals of Disorder and Order Perception

Order and disorder are prevalent in everyday life, yet little is known about the neural real-time processing that occurs during the perception of disorder relative to order. In the present study, from a cognitive perspective, by adopting the ERP method, we aimed to examine the elicited real-time neural signals of disorder and order perception when participants processed physical environmental and basic visual disorder and order pictures in an irrelevant red or green rectangle detection task, and we attempted to test the hypothesis of cognitive disfluency in disorder perception. Generally, we observed that at each measured time interval, the ERPs elicited by order stimuli were more positive (less negative) in amplitude than those elicited by disorder stimuli at the frontal electrodes (represented by F7/F8, FT7/FT8, Fz, and FCz), whereas at the posterior electrodes (represented by P7/P8, PO7/PO8, Pz, and POz), the opposite was true. These data reveal for the first time the neural underpinnings of disorder and order perception, extending our understanding of the nature of disorder and order. This study also contributes to the cognitive fluency literature and indirectly expands the research on disorder and order stimuli in cognitive fluency

Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications

As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization

Zambia Signal Functions study 2016 dataset

This dataset contains information related to health facilities’ infrastructure, staffing, equipment, supplies, and capacity to perform various clinical functions related to reproductive and maternal health service provision. The study was conducted in Central Province, Zambia and its primary aim was to assess facilities’ capacity to provide termination of pregnancy services. EMBARGOED UNTIL 31st DEC 201

ACE2-Mediated Reduction of Oxidative Stress in the Central Nervous System Is Associated with Improvement of Autonomic Function

Oxidative stress in the central nervous system mediates the increase in sympathetic tone that precedes the development of hypertension. We hypothesized that by transforming Angiotensin-II (AngII) into Ang-(1–7), ACE2 might reduce AngII-mediated oxidative stress in the brain and prevent autonomic dysfunction. To test this hypothesis, a relationship between ACE2 and oxidative stress was first confirmed in a mouse neuroblastoma cell line (Neuro2A cells) treated with AngII and infected with Ad-hACE2. ACE2 overexpression resulted in a reduction of reactive oxygen species (ROS) formation. In vivo, ACE2 knockout (ACE2−/y) mice and non-transgenic (NT) littermates were infused with AngII (10 days) and infected with Ad-hACE2 in the paraventricular nucleus (PVN). Baseline blood pressure (BP), AngII and brain ROS levels were not different between young mice (12 weeks). However, cardiac sympathetic tone, brain NADPH oxidase and SOD activities were significantly increased in ACE2−/y. Post infusion, plasma and brain AngII levels were also significantly higher in ACE2−/y, although BP was similarly increased in both genotypes. ROS formation in the PVN and RVLM was significantly higher in ACE2−/y mice following AngII infusion. Similar phenotypes, i.e. increased oxidative stress, exacerbated dysautonomia and hypertension, were also observed on baseline in mature ACE2−/y mice (48 weeks). ACE2 gene therapy to the PVN reduced AngII-mediated increase in NADPH oxidase activity and normalized cardiac dysautonomia in ACE2−/y mice. Altogether, these data indicate that ACE2 gene deletion promotes age-dependent oxidative stress, autonomic dysfunction and hypertension, while PVN-targeted ACE2 gene therapy decreases ROS formation via NADPH oxidase inhibition and improves autonomic function. Accordingly, ACE2 could represent a new target for the treatment of hypertension-associated dysautonomia and oxidative stress

Hospice Care Preferences and Its Associated Factors among Community-Dwelling Residents in China

Hospice care is a comprehensive approach addressing patients’ physical, psychosocial, and spiritual needs at the end of life (EoL). Despite the recognition of its effectiveness in improving the quality of EoL care, little is known about hospice care in mainland China. In this study, we aimed to examine the preferences for hospice care and its related factors among community-dwelling residents in mainland China. Participants were recruited using a convenience sampling method, and 992 community-dwelling residents responded to an online survey from June 2018 to August 2019. The majority (66.7%) of the participants were female, and the mean age was 48.4 years. Approximately 28% of the participants had heard of hospice care, and 91.2% preferred to receive hospice care if diagnosed with a terminal illness. Participants who had heard of hospice care, and with higher levels of education (bachelor’s degree or above) and health insurance coverage were more likely to accept hospice care than their counterparts. Community-based education on hospice care is imperative to improve public knowledge and the acceptance of hospice care. Meanwhile, there is a need to develop policies to integrate and expand hospice care into clinical settings

Automatic lane change data extraction from car data sequence

An automatic real driving data extraction method for lane change behavior is proposed in this paper which can efficiently detect the accurate start and end timestamp of lane change behaviors from long time driving data sequence. The objective of this work is to efficiently collect lane change data samples for behavior model building or intelligent ADAS system training. The proposed machine leaning based approach shows robustness against confusion from similar driving behaviors and results in highly accurate performance in extracting lane change behavior data segments in a fully automatic way.EICPCI-S(ISTP)[email protected]