The aim of this study was to analyze the clinical data, discharge rate, and fatality rate of COVID‐19 patients for clinical help. The clinical data of COVID‐19 patients from December 2019 to February ...2020 were retrieved from four databases. We statistically analyzed the clinical symptoms and laboratory results of COVID‐19 patients and explained the discharge rate and fatality rate with a single‐arm meta‐analysis. The available data of 1994 patients in 10 literatures were included in our study. The main clinical symptoms of COVID‐19 patients were fever (88.5%), cough (68.6%), myalgia or fatigue (35.8%), expectoration (28.2%), and dyspnea (21.9%). Minor symptoms include headache or dizziness (12.1%), diarrhea (4.8%), nausea and vomiting (3.9%). The results of the laboratory showed that the lymphocytopenia (64.5%), increase of C‐reactive protein (44.3%), increase of lactic dehydrogenase (28.3%), and leukocytopenia (29.4%) were more common. The results of single‐arm meta‐analysis showed that the male took a larger percentage in the gender distribution of COVID‐19 patients 60% (95% CI 0.54, 0.65), the discharge rate of COVID‐19 patients was 52% (95% CI 0.34,0.70), and the fatality rate was 5% (95% CI 0.01,0.11).
Research Highlights
Our study explored the clinical and epidemiological characteristics of COVID‐19 patients, and proposed the need to focus on other systemic symptoms such as the gastrointestinal tract firstly, which could be of use for clinical work.
Metal halide perovskites (MHPs) have been a hot research topic due to their facile synthesis, excellent optical and optoelectronic properties, and record‐breaking efficiency of corresponding ...optoelectronic devices. Nowadays, the development of miniaturized high‐performance photodetectors (PDs) has been fueling the demand for novel photoactive materials, among which low‐dimensional MHPs have attracted burgeoning research interest. In this report, the synthesis, properties, photodetection performance, and stability of low‐dimensional MHPs, including 0D, 1D, 2D layered and nonlayered nanostructures, as well as their heterostructures are reviewed. Recent advances in the synthesis approaches of low‐dimensional MHPs are summarized and the key concepts for understanding the optical and optoelectronic properties related to the PD applications of low‐dimensional MHPs are introduced. More importantly, recent progress in novel PDs based on low‐dimensional MHPs is presented, and strategies for improving the performance and stability of perovskite PDs are highlighted. By discussing recent advances, strategies, and existing challenges, this progress report provides perspectives on low‐dimensional MHP‐based PDs in the future.
Photodetectors (PDs) based on low‐dimensional metal halide perovskites (MHPs) have attracted burgeoning interest for next‐generation optoelectronic applications. Recent advances in low‐dimensional MHP‐based PDs are summarized, including the synthesis, fundamental properties, performance, and stability issues. By discussing practical strategies and existing challenges, perspectives on low‐dimensional MHP PDs for improved future performance and stability are provided.
There is growing evidence that neuroinflammation is closely linked to depression. Honokiol, a biologically active substance extracted from
, which is widely used in traditional Chinese medicine, has ...been shown to exert significant anti-inflammatory effects and improve depression-like behavior caused by inflammation. However, the specific mechanism of action of this activity is still unclear. In this study, the lipopolysaccharide (LPS) mouse model was used to study the effect of honokiol on depression-like behavior induced by LPS in mice and its potential mechanism. A single administration of LPS (1 mg/kg, intraperitoneal injection) increased the immobility time in the forced swimming test (FST) and tail suspension test (TST), without affecting autonomous activity. Pretreatment with honokiol (10 mg/kg, oral administration) for 11 consecutive days significantly improved the immobility time of depressed mice in the FST and TST experiments. Moreover, honokiol ameliorated LPS-induced NF-κB activation in the hippocampus and significantly reduced the levels of the pro-inflammatory cytokines; tumor necrosis factor α (TNF-α), interleukin 1β (IL-1β), and interferon γ (IFN-γ). In addition, honokiol inhibited LPS-induced indoleamine 2,3-dioxygenase (IDO) activation and quinolinic acid (a toxic product) increase and reduced the level of free calcium in brain tissue, thereby inhibiting calcium overload. In summary, our results indicate that the anti-depressant-like effects of honokiol are mediated by its anti-inflammatory effects. Honokiol may inhibit the LPS-induced neuroinflammatory response through the NF-κB signaling pathway, reducing the levels of related pro-inflammatory cytokines, and furthermore, this may affect tryptophan metabolism and increase neuroprotective metabolites.
We explored whether medical health workers had more psychosocial problems than nonmedical health workers during the COVID-19 outbreak.
An online survey was run from February 19 to March 6, 2020; a ...total of 2,182 Chinese subjects participated. Mental health variables were assessed via the Insomnia Severity Index (ISI), the Symptom Check List-revised (SCL-90-R), and the Patient Health Questionnaire-4 (PHQ-4), which included a 2-item anxiety scale and a 2-item depression scale (PHQ-2).
Compared with nonmedical health workers (n = 1,255), medical health workers (n = 927) had a higher prevalence of insomnia (38.4 vs. 30.5%, p < 0.01), anxiety (13.0 vs. 8.5%, p < 0.01), depression (12.2 vs. 9.5%; p< 0.04), somatization (1.6 vs. 0.4%; p < 0.01), and obsessive-compulsive symptoms (5.3 vs. 2.2%; p < 0.01). They also had higher total scores of ISI, GAD-2, PHQ-2, and SCL-90-R obsessive-compulsive symptoms (p ≤ 0.01). Among medical health workers, having organic disease was an independent factor for insomnia, anxiety, depression, somatization, and obsessive-compulsive symptoms (p < 0.05 or 0.01). Living in rural areas, being female, and being at risk of contact with COVID-19 patients were the most common risk factors for insomnia, anxiety, obsessive-compulsive symptoms, and depression (p < 0.01 or 0.05). Among nonmedical health workers, having organic disease was a risk factor for insomnia, depression, and obsessive-compulsive symptoms (p < 0.01 or 0.05).
During the COVID-19 outbreak, medical health workers had psychosocial problems and risk factors for developing them. They were in need of attention and recovery programs.
The use of translanguaging pedagogy rather than favoring English-only teaching put those who hold monolingual ideology in foreign language pedagogy. A study of English as a foreign language (EFL) for ...multilingual students showed that embracing translanguaging appeared compelling that it emerged in Neidi Xinjiang Classes (NXC) in China. After reviewing the concepts of translanguaging, this writing presents the translanguaging theory and translanguaging pedagogy. Guided by translanguaging lenses and stances, a longitudinal case study was carried out to look into the EFL pedagogy in NXC. The focal data was heavily drawn from reflective interviews and classroom observations. The data analyses were particularly paid on the triangulation of data with the interviews and classroom observational data. The findings indicate the students made full use of the affordances to do translanguaging practices while a few EFL teachers with the monolingual ideology still violated English-only constraints against the background of the multilingual turn. Therefore, the conclusion builds on dynamic multilingual stances to identify the significance of enabling the presence of translanguaging pedagogy.
Non-coding RNAs are important in the progression of liver cancer. The present study aimed to investigate the effects of long non-coding RNA HOX transcript antisense RNA (HOTAIR) on the proliferation ...of liver cancer and the association between HOTAIR and microRNA (miR)-217. It was demonstrated that the expression of HOTAIR was upregulated in liver cancer tissues and 3 liver cancer cell lines (MHCC97H, HepG2 and Hep3B). Inhibition of HOTAIR with HOTAIR small interfering (si) RNA lentiviral vectors significantly suppressed the cell proliferation of HepG2 cells, and downregulated the protein expression levels of two proliferation markers, Ki67 and proliferating cell nuclear antigen (PCNA). Furthermore, inhibition of HOTAIR induced G0/G1 cycle arrest by increasing the expression of p27 and decreasing the expression of cyclin D1. It was then predicted and verified that miR-217 was the target of HOTAIR. Expression of miR-217 was downregulated in liver cancer tissues and the 3 liver cancer cell lines. Further results revealed that inhibition of HOTAIR markedly upregulated the expression of miR-217 in HepG2 cells, and miR-217 inhibitor-induced reduction of miR-217 was significantly suppressed by HOTAIR inhibition. Furthermore, the increased cell proliferation and growth, the upregulated expression of Ki67 and PCNA, and the reduced G0/G1 cycle arrest induced by miR-217 inhibitor were partly rescued by inhibition of HOTAIR. Finally, the
experiment indicated that HOTAIR inhibition suppressed tumorigenesis, including the smaller tumor volume and the reduced levels of Ki67. Overall, HOTAIR contributes to the proliferation and growth of liver cancer via downregulation of miR-217.
Global change ecology nowadays embraces ever‐growing large observational datasets (big‐data) and complex mathematical models that track hundreds of ecological processes (big‐model). The rapid ...advancement of the big‐data‐big‐model has reached its bottleneck: high computational requirements prevent further development of models that need to be integrated over long time‐scales to simulate the distribution of ecosystems carbon and nutrient pools and fluxes. Here, we introduce a machine‐learning acceleration (MLA) tool to tackle this grand challenge. We focus on the most resource‐consuming step in terrestrial biosphere models (TBMs): the equilibration of biogeochemical cycles (spin‐up), a prerequisite that can take up to 98% of the computational time. Through three members of the ORCHIDEE TBM family part of the IPSL Earth System Model, including versions that describe the complex interactions between nitrogen, phosphorus and carbon that do not have any analytical solution for the spin‐up, we show that an unoptimized MLA reduced the computation demand by 77%–80% for global studies via interpolating the equilibrated state of biogeochemical variables for a subset of model pixels. Despite small biases in the MLA‐derived equilibrium, the resulting impact on the predicted regional carbon balance over recent decades is minor. We expect a one‐order of magnitude lower computation demand by optimizing the choices of machine learning algorithms, their settings, and balancing the trade‐off between quality of MLA predictions and need for TBM simulations for training data generation and bias reduction. Our tool is agnostic to gridded models (beyond TBMs), compatible with existing spin‐up acceleration procedures, and opens the door to a wide variety of future applications, with complex non‐linear models benefit most from the computational efficiency.
The rapid advancement of the big‐data‐big‐model has reached its bottleneck: high computational requirements prevent further development of Terrestrial Biosphere Models (TBM) that need to be integrated over long time scales to simulate the distribution of ecosystems carbon and nutrient pools and fluxes. To tackle this grand challenge, we developed a machine‐learning acceleration (MLA) tool for the most resource‐consuming step in TBMs: the equilibration of biogeochemical cycles (spin‐up). We show that an unoptimized MLA reduced the computation demand by 77%–80% for global studies via interpolating the equilibrated state of biogeochemical variables for a subset of model pixels.
Gut Microbiota-brain Axis Wang, Hong-Xing; Wang, Yu-Ping
Chinese medical journal,
10/2016, Letnik:
129, Številka:
19
Journal Article
Recenzirano
Odprti dostop
Objective: To systematically review the updated information about the gut microbiota-brain axis.
Data Sources: All articles about gut microbiota-brain axis published up to July 18, 2016, were ...identified through a literature search on PubMed, ScienceDirect, and Web of Science, with the keywords of "gut microbiota", "gut-brain axis", and "neuroscience".
Study Selection: All relevant articles on gut microbiota and gut-brain axis were included and carefully reviewed, with no limitation of study design.
Results: It is well-recognized that gut microbiota affects the brain's physiological, behavioral, and cognitive functions although its precise mechanism has not yet been fully understood. Gut microbiota-brain axis may include gut microbiota and their metabolic products, enteric nervous system, sympathetic and parasympathetic branches within the autonomic nervous system, neural-immune system, neuroendocrine system, and central nervous system. Moreover, there may be five communication routes between gut microbiota and brain, including the gut-brain's neural network, neuroendocrine-hypothalamic-pituitary-adrenal axis, gut immune system, some neurotransmitters and neural regulators synthesized by gut bacteria, and barrier paths including intestinal mucosal barrier and blood-brain barrier. The microbiome is used to define the composition and functional characteristics of gut microbiota, and metagenomics is an appropriate technique to characterize gut microbiota.
Conclusions: Gut microbiota-brain axis refers to a bidirectional information network between the gut microbiota and the brain, which may provide a new way to protect the brain in the near future.
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