Search

Abstract Background Nurses face significant professional challenges arising from global pandemics, inadequate welfare protection, and declining social status. In this context, the decent working conditions for nurses are not adequately safeguarded. This study examines decent work status among Chinese nurses and its links work engagement, and compassion fatigue. Methods This study is a cross-sectional research. Through the method of convenience sampling, five reputable hospitals in Hangzhou, China, were selected, and a total of 838 nurses were surveyed. Data collection was conducted from April to May 2023. We employed a comprehensive set of assessment tools, including an evaluation of demographic characteristics, the Decent Work Perception Scale (DWPS), the Chinese version of the Compassion Fatigue Short Scale (C-CFSS), and the Gallup Q12 survey scale. Results The findings of this study demonstrated that compassion fatigue was significantly negatively correlated with both decent work (r = -0.482, p

Gliomas are prevalent malignant tumors in adults, which can be categorized as either localized or diffuse gliomas. Glioblastoma is the most aggressive and deadliest form of glioma. Currently, there is no complete cure, and the median survival time is less than one year. The main mechanism of regulated cell death involves organisms coordinating the elimination of damaged cells at risk of tumor transformation or cells hijacked by microorganisms for pathogen replication. This process includes apoptosis, necroptosis, autophagy, ferroptosis, pyroptosis, necrosis, parthanayosis, entosis, lysosome-dependent death, NETosis, oxiptosis, alkaliptosis, and disulfidaptosis. The main goal of clinical oncology is to develop therapies that promote the effective elimination of cancer cells by regulating cell death are the main goal of clinical oncology. Recently, scientists have utilized pertinent regulatory factors and natural small-molecule compounds to induce regulated cell death for the treatment of gliomas. By analyzing the PubMed and Web of Science databases, this paper reviews the research progress on the regulation of cell death and the role of natural small-molecule compounds in glioma. The aim is to provide help for the treatment of glioblastoma.

BackgroundThe space environment contains numerous high-energy particles, and a single high-energy particle passing through a spacecraft shell bombards the electronic devices within, triggering single-particle effects such as device logic state upset and function failures, which, in turn, affect spacecraft operation reliability and mission accomplishment.PurposeNotably, ground accelerator irradiation tests provide an important and effective means for simulating space single event effects and for predicting the risks of single event effect rates for electronic devices in space applications. Generally, electronic devices can be used in spacecraft only if their resistance radiation indicators meet astronautical application requirements.MethodsSpacecraft are typically exposed to space radiation particles, primarily heavy ions and protons; therefore, single event effect simulation testing for electronic devices relies predominantly on heavy ion and proton accelerators. To address the requirements of single event effect testing, technologies such as large-area beam expansion and homogenization, high-precision beam current diagnosis, and efficient test terminals have been developed to fulfill the requirements of various test tasks.ResultsParticular focus is placed on the CIAE's (China Institute of Atomic Energy) heavy ion single event and proton single event effect simulation test techniques and the heavy ion microbeam technique for radiation sensitive area identification for electronic devices. Subsequently, the aforementioned techniques are applied to a single event effect risk evaluation for astronautical electronic devices.ConclusionsIn the future, the demand for radiation-resistant devices is expected to continue to increase in the aerospace, nuclear industry, and other radiation application fields. It is, therefore, necessary to further exploit the irradiation potential of existing domestic single event effect simulation equipment and establish new accelerator platforms with improved capacity for single event effect simulation testing.

Glioma is a common primary aggressive tumor with limited clinical treatment. Recently, growing research suggests that gut microbiota is involved in tumor progression, and several probiotics can inhibit tumor growth. However, evidence for the effect of probiotics on glioma is lacking. Here, we found that Bifidobacterium (B.) lactis combined with Lactobacillus (L.) plantarum reduced tumor volume, prolonged survival time and repaired the intestinal barrier damage in an orthotopic mouse model of glioma. Experiments demonstrated that B. lactis combined with L. plantarum suppressed the PI3K/AKT pathway and down-regulated the expression of Ki-67 and N-cadherin. The glioma-inhibitory effect of probiotic combination is also related to the modulation of gut microbiota composition, which is characterized by an increase in relative abundance of Lactobacillus and a decrease in some potential pathogenic bacteria. Additionally, probiotic combination altered fecal metabolites represented by fatty acyls and organic oxygen compounds. Together, our results prove that B. lactis combined with L. plantarum can inhibit glioma growth by suppressing PI3K/AKT pathway and regulating gut microbiota composition and metabolites in mice, thus suggesting the potential benefits of B. lactis and L. plantarum against glioma.

Hawthorn leaves, which is a traditional Chinese medicine (TCM), has been used for treating coronary heart disease (CHD) for a long time in China. But the limited understanding of the main active components and molecular mechanisms of this traditional medicine has restricted its application and further research. The active compounds of hawthorn leaves were obtained from TCMSP database and SymMap database. The targets of it were predicted based on TCMSP, PubChem, Swiss Target Prediction, and SymMap database. The putative targets of CHD were gathered from multi-sources databases including the Online Mendelian Inheritance in Man (OMIM) database, the DrugBank database, the GeneCards database and the DisGeNet database. Network topology analysis, GO and KEGG pathway enrichment analyses were performed to select the key targets and pathways. Molecular docking was performed to demonstrate the binding capacity of the key compounds to the predicted targets. Furthermore, RAW264.7 cells stimulated by lipopolysaccharides (LPS) were treated with three effective compounds of hawthorn leaves to assess reliability of prediction. Quercetin, isorhamnetin and kaempferol were main active compounds in hawthorn leaves. Forty four candidate therapeutic targets were identified to be involved in protection of hawthorn leaves against CHD. Additionally, the effective compounds of it had good binding affinities to PTGS2, EGFR, and MMP2. Enrichment analyses suggested that immune inflammation related biological processes and pathways were possibly the potential mechanism. Besides, we found that three predicted effective compounds of hawthorn leaves decreased protein expression of PTGS2, MMP2, MMP9, IL6, IL1B, TNFα and inhibited activation of macrophage. In summary, the present study demonstrates that quercetin, kaempferol and isorhamnetin are proved to be the main effective compounds of hawthorn leaves in treatment of CHD, possibly by suppressing expression of PTGS2, MMP2, MMP9, inflammatory cytokines and macrophages viability. This study provides a new understanding of the active components and mechanisms of hawthorn leaves treating CHD from the perspective of network pharmacology.

Abstract Background Strong epidemiologic evidence indicates that green tea intake is protective against hyperlipidemia; however, randomized controlled studies have presented varying results. In the present study, we aimed to conduct a literature review and meta-analysis to assess the effect of green tea on blood lipids. Methods PubMed, Embase, and the Cochrane Library were electronically explored from inception to September 2019 for all relevant studies. Random effect models were used to estimate blood lipid changes between green tea supplementation and control groups by evaluating the weighted mean differences (WMD) with 95% confidence intervals (CIs). The risk of bias for study was assessed using the Cochrane tool. Publication bias was evaluated using funnel plots and Egger’s tests. Results Thirty-one trials with a total of 3321 subjects were included in the meta-analysis. In general, green tea intake significantly lowered the total cholesterol (TC); WMD: − 4.66 mg/dL; 95% CI: − 6.36, − 2.96 mg/dL; P

Purine nucleoside phosphorylases (PNPs) are promising biocatalysts for the synthesis of purine nucleoside analogs. Although a number of PNPs have been reported, the development of highly efficient enzymes for industrial applications is still in high demand. Herein, a new trimeric purine nucleoside phosphorylase (AmPNP) from Aneurinibacillus migulanus AM007 was cloned and heterologously expressed in Escherichia coli BL21(DE3). The AmPNP showed good thermostability and a broad range of pH stability. The enzyme was thermostable below 55 °C for 12 h (retaining nearly 100% of its initial activity), and retained nearly 100% of the initial activity in alkaline buffer systems (pH 7.0−9.0) at 60 °C for 2 h. Then, a one-pot, two-enzyme mode of transglycosylation reaction was successfully constructed by combining pyrimidine nucleoside phosphorylase (BbPyNP) derived from Brevibacillus borstelensis LK01 and AmPNP for the production of purine nucleoside analogs. Conversions of 2,6-diaminopurine ribonucleoside (1), 2-amino-6-chloropurine ribonucleoside (2), and 6-thioguanine ribonucleoside (3) synthesized still reached >90% on the higher concentrations of substrates (pentofuranosyl donor: purine base; 20:10 mM) with a low enzyme ratio of BbPyNP: AmPNP (2:20 μg/mL). Thus, the new trimeric AmPNP is a promising biocatalyst for industrial production of purine nucleoside analogs.

More than 100,000 people were killed and around 2.6 million injured in road accidents in the People’s Republic of China (PRC), that is four to eight times that of developed countries, equivalent to 6.2 mortality per 10 thousand vehicles—the highest rate in the world. There are more than 1,700 fatalities and 840,000 injuries yearly due to vehicle crashes off public highways. In this paper, we proposed a interactive safety situation and threat analysis framework based on driver behaviour and vehicle dynamics risk analysis based on ISO26262…

Backgrounds: Virtual reality (VR) has become a transformative technology with applications in gaming, education, healthcare, and psychotherapy. The subjective experiences in VR vary based on the virtual environment's characteristics, and electroencephalography (EEG) is instrumental in assessing these differences. By analyzing EEG signals, researchers can explore the neural mechanisms underlying cognitive and emotional responses to VR stimuli. However, distinguishing EEG signals recorded by two-dimensional (2D) versus three-dimensional (3D) VR environments remains underexplored. Current research primarily utilizes power spectral density (PSD) features to differentiate between 2D and 3D VR conditions, but the potential of other feature parameters for enhanced discrimination is unclear. Additionally, the use of machine learning techniques to classify EEG signals from 2D and 3D VR using alternative features has not been thoroughly investigated, highlighting the need for further research to identify robust EEG features and effective classification methods. Methods: This study recorded EEG signals from participants exposed to 2D and 3D VR video stimuli to investigate the neural differences between these conditions. Key features extracted from the EEG data included PSD and common spatial patterns (CSPs), which capture frequency-domain and spatial-domain information, respectively. To evaluate classification performance, several classical machine learning algorithms were employed: ssupport vector machine (SVM), k-nearest neighbors (KNN), random forest (RF), naive Bayes, decision Tree, AdaBoost, and a voting classifier. The study systematically compared the classification performance of PSD and CSP features across these algorithms, providing a comprehensive analysis of their effectiveness in distinguishing EEG signals in response to 2D and 3D VR stimuli. Results: The study demonstrated that machine learning algorithms can effectively classify EEG signals recorded during watching 2D and 3D VR videos. CSP features outperformed PSD in classification accuracy, indicating their superior ability to capture EEG signals differences between the VR conditions. Among the machine learning algorithms, the Random Forest classifier achieved the highest accuracy at 95.02%, followed by KNN with 93.16% and SVM with 91.39%. The combination of CSP features with RF, KNN, and SVM consistently showed superior performance compared to other feature-algorithm combinations, underscoring the effectiveness of CSP and these algorithms in distinguishing EEG responses to different VR experiences. Conclusions: This study demonstrates that EEG signals recorded during watching 2D and 3D VR videos can be effectively classified using machine learning algorithms with extracted feature parameters. The findings highlight the superiority of CSP features over PSD in distinguishing EEG signals under different VR conditions, emphasizing CSP's value in VR-induced EEG analysis. These results expand the application of feature-based machine learning methods in EEG studies and provide a foundation for future research into the brain cortical activity of VR experiences, supporting the broader use of machine learning in EEG-based analyses. [ABSTRACT FROM AUTHOR]

To improve the real-time performance and the target adaptability of penetration fuze detonation control systems, and to enhance the system fusion processing capability for multi-sensor information, this paper uses a modular design concept to construct a miniaturized (ø38 mm × 4 mm) fuze detonation control system that is capable of real-time processing of data from multiple information sources. The core component of this system is the GD32E230 microcontroller, which features a high dominant frequency and low power consumption. This device is integrated with a ferroelectric memory and signal processing circuits that match the sensors. To address the issue of unclear traditional acceleration signal penetration and the difficulties associated with the identification of these signals, the approach in this paper improves feature recognition accuracy through rapid acquisition and fusion of multiple types of sensor output signal, and self-adaptive identification of multilayered targets and single-layer thick targets is achieved. During the programming of the embedded system, the hardware register is operated directly, the instruction execution sequence is optimized, and the program execution efficiency is improved by using the function characteristic that some microcontroller unit peripherals do not occupy the central processing unit when working, thus allowing the intended purpose of improving the system's real-time performance to be achieved. A semi-physical simulation method is then used to verify the performance of the penetration fuze detonation control system. The results obtained show that the system has 100%-layer counting accuracy for multilayered targets and a relative error of less than 1% for the calculated residual velocities of single-layer thick targets, thus validating the effectiveness of the system. [ABSTRACT FROM AUTHOR]

Drought stress is one of the most important climate-related factors affecting crop production. Tomatoes (Solanum lycopersicum L.) are economically important crops which are highly sensitive to drought. The entomopathogenic fungus Beauveria bassiana, a widely used biological insecticide, can form symbiotic relationships with plants via endophytic colonization, increasing plant biomass and the ability to resist biotic stress. Under simulated drought stress conditions, the biomass of tomato seedlings such as plant height, root length, stem diameter, fresh weight, and relative water content, as well as the density and size of stomata in tomato leaves were significantly increased after B. bassiana colonization via root irrigation (p < 0.05). Meanwhile, the physicochemical properties associated with drought resistance such as peroxidase activity and proline content increased significantly (p < 0.05), while malondialdehyde reduced significantly (p < 0.05), and the expression levels of key genes related to stomatal development and drought tolerance pathways increased significantly (p < 0.05). These results indicate that the colonization of B. bassiana enhances the water absorption capacity of tomato seedlings and the rate of transpiration significantly and increases drought tolerance in tomato via the "water spender" pathway, which provides a new strategy for improving crop resistance to drought stress. [ABSTRACT FROM AUTHOR]