Search

Abstract This paper develops a theoretical model of determinants influencing multimodal fake review generation using the theories of signaling, actor-network, motivation, and human–environment interaction hypothesis. Applying survey data from users of China’s three leading E-commerce platforms (Taobao, Jingdong, and Pinduoduo), we adopt structural equation modeling, machine learning technique, and Bayesian complex networks analysis to perform factor identification, path analysis, feature factor importance ranking, regime division, and network centrality analysis of full sample, male sample, and female sample to reach the following conclusions: (1) platforms’ multimodal recognition and governance capabilities exert significant negative moderating effects on merchants’ information behavior, while it shows no apparent moderating effect on users’ information behavior; users’ emotional venting, perceived value, reward mechanisms, and subjective norms positively influence multimodal fake review generation through perceptual behavior control; (2) feature factors of multimodal fake review generation can be divided into four regimes, i.e., regime 1 includes reward mechanisms and perceived social costs, indicating they are key feature factors of multimodal fake review generation; merchant perception impact is positioned in regime 2, signifying its pivotal role in multimodal fake review generation; regime 3 includes multimodal recognition and governance capabilities, supporting/disparaging merchants, and emotional venting; whereas user perception impact is positioned in regime 4, indicating its weaker influence on multimodal fake review generation; (3) both in full sample, male sample, and female sample, reward mechanisms play a crucial role in multimodal fake review generation; perceived value, hiring review control agency, multimodal recognition and governance capabilities exhibit a high degree of correlation; however, results of network centrality analysis also exhibit heterogeneity between male and female samples, i.e., male sample has different trends in closeness centrality values and betweenness centrality values than female sample. This indicates that determinants influencing multimodal fake review generation are complex and interconnected.

AimTo identify optimally therapeutic exercise interventions for improving motor ability among patients with Parkinson’s disease (PD), we conducted a network meta-analysis (NMA) of randomized controlled trials comparing different exercise regimens.MethodsRelevant RCTs were retrieved by searching PubMed, Embase, Cochrane, Web of Science, CINAHL, CBM, China National Knowledge Infrastructure (CNKI), Wan fang, VIP, and other databases from inception to July 9, 2023 is available in English as the primary language. Exercise outcomes as measured by Movement Disorder Society- Unified Parkinson’s Disease Rating Scale Part III (MDS-UPDRS-III) score change were evaluated and ranked using STATA software version 18.0. All included studies were assessed for methodological quality using the Cochrane Risk of Bias tool.ResultsThe final NMA included 71 studies involving 3,732 participants, 87 intervention experiments, and 27distinct interventions. Although most exercise interventions showed some efficacy (reducing MDS-UPDRS-III score), cumulative ranking probability surface (SUCRA) values indicated that the best exercise interventions for motor function improvement were archery (95.6%), riding a bicycle (80.9%), and binary rhythm dance (80.8%).ConclusionAn exercise intervention comprising archery, cycling, and(or) binary rhythm dance may yield superior improvements in motor function among patients with Parkinson’s disease.

Background: Tripterygium glycosides (TG) is extracted from the roots of Tripterygium wilfordii Hook F (Lei gong teng, a traditional Chinese medicine). It is widely used in China to treat immunoglobulin A vasculitis nephritis (IgAVN), which is a common secondary glomerular disease. As there are no guidelines for the rational application of TG, we performed this study to evaluate the efficacy and safety of different doses of TG and to determine the optimal treatment for IgAVN. Methods: Ten databases were searched from their inception to April 2023 for randomised controlled trials (RCTs) using TG, TG combined with glucocorticoids (GC), or TG combined with traditional Chinese medicine (TCM) to treat IgAVN. A network meta-analysis was performed following the protocol (CRD42023401645). Results: Forty-four eligible RCTs involving 3402 patients were included. For effective rate, TG 1.5 mg/kg/d (TG1.5) + TCM was ranked as the best intervention, followed by TG 1.0 mg/kg/d (TG1.0) + TCM, TG1.5, TG1.0+GC, TG1.0, TCM, GC, and routine treatment (RT). TG1.0+TCM ranked best in reducing recurrence, followed by TG1.0+GC, GC, TG1.5, and RT. Compared with TG1.0, TG1.0+TCM and TG1.5+TCM effectively reduced liver injury events. Compared with TG1.5, TG1.5+TCM and TG1.0+TCM effectively reduced leukopenia events. No significant differences in the reduction of gastrointestinal events were observed between the interventions. Subgroup analyses explored the effects of the participants’ age. The intervention rankings of the outcomes generally remained consistent. Only a small difference was observed in gastrointestinal events. TCM was the best treatment for reducing gastrointestinal events in paediatric patients. Conclusions: The results showed a positive correlation between dose and efficacy, whereas no relationship was found between dose and adverse events. TCM can boost the efficacy and reduce adverse events when combined with TG. In conclusion, we consider TG1.5+TCM as the best treatment for IgAVN. However, further research is required to confirm these findings.

Background: China has experienced a COVID-19 wave caused by Omicron XBB variant starting in April 2023. Our aim is to conduct a retrospective analysis exploring the dynamics of the outbreak under counterfactual scenarios that combine the use of vaccines, antiviral drugs, and nonpharmaceutical interventions. Methods: We developed a mathematical model of XBB transmission in China, which has been calibrated using SARS-CoV-2 positive rates per week. Intrinsic age-specific infection-hospitalization risk, infection-ICU risk, and infection-fatality risk were used to estimate disease burdens, characterized as number of hospital admissions, ICU admissions, and deaths. Results: We estimated that in absence of behavioral change, the XBB outbreak in spring 2023 would have resulted in 0.86 billion infections (∼61% of the total population). Our counterfactual analysis shows that the synergetic effect of vaccination (70% vaccination coverage), antiviral treatment (20% receiving antiviral treatment), and moderate nonpharmaceutical interventions (20% isolation and L1 PHSMs) could reduce the number of deaths to levels close to seasonal influenza (1.17 vs. 0.65 per 10,000 individuals and 5.85 vs. 3.85 per 10,000 individuals aged 60+, respectively). The maximum peak prevalence of hospital and ICU admissions are estimated to be lower than the corresponding capacities (8.6 vs. 10.4 per 10,000 individuals and 1.2 vs. 2.1 per 10,000 individuals, respectively). Conclusion: Our findings suggest that the capacity of the Chinese healthcare system was adequate to face the Omicron XBB wave in spring 2023 but, at the same time, supports the importance of administering highly effective vaccine with long-lasting immune response, and the use of antiviral treatments.

Abstract. Gliomas tend to have a poor prognosis and are the most common primary malignant tumors of the central nervous system. Compared with patients with other cancers, glioma patients often suffer from increased levels of psychological stress, such as anxiety and fear. Chronic stress (CS) is thought to impact glioma profoundly. However, because of the complex mechanisms underlying CS and variability in individual tolerance, the role of CS in glioma remains unclear. This review suggests a new proposal to redivide the stress system into two parts. Neuronal activity is dominant upstream. Stress-signaling molecules produced by the neuroendocrine system are dominant downstream. We discuss the underlying molecular mechanisms by which CS impacts glioma. Potential pharmacological treatments are also summarized from the therapeutic perspective of CS.

Abstract Background As the largest substantive organ of animals, the liver plays an essential role in the physiological processes of digestive metabolism and immune defense. However, the cellular composition of the pig liver remains poorly understood. This investigation used single-nucleus RNA sequencing technology to identify cell types from liver tissues of pigs, providing a theoretical basis for further investigating liver cell types in pigs. Results The analysis revealed 13 cells clusters which were further identified 7 cell types including endothelial cells, T cells, hepatocytes, Kupffer cells, stellate cells, B cells, and cholangiocytes. The dominant cell types were endothelial cells, T cells and hepatocytes in the liver tissue of Dahe pigs and Dahe black pigs, which accounts for about 85.76% and 82.74%, respectively. The number of endothelial cells was higher in the liver tissue of Dahe pigs compared to Dahe black pigs, while the opposite tendency was observed for T cells. Moreover, functional enrichment analysis demonstrated that the differentially expressed genes in pig hepatic endothelial cells were significantly enriched in the protein processing in endoplasmic reticulum, MAPK signaling pathway, and FoxO signaling pathway. Functional enrichment analysis demonstrated that the differentially expressed genes in pig hepatic T cells were significantly enriched in the thyroid hormone signaling pathway, B cell receptor signaling pathway, and focal adhesion. Functional enrichment analysis demonstrated that the differentially expressed genes in pig hepatic hepatocytes were significantly enriched in the metabolic pathways. Conclusions In summary, this study provides a comprehensive cell atlas of porcine hepatic tissue. The number, gene expression level and functional characteristics of each cell type in pig liver tissue varied between breeds.

Modern data centers provide the foundational infrastructure of cloud computing. Workload generation, which involves simulating or constructing tasks and transactions to replicate the actual resource usage patterns of real-world systems or applications, plays essential role for efficient resource management in these centers. Data center traces, rich in information about workload execution and resource utilization, are thus ideal data for workload generation. Traditional traces provide detailed temporal resource usage data to enable fine-grained workload generation. However, modern data centers tend to favor tracing statistical metrics to reduce overhead. Therefore the accurate reconstruction of temporal resource consumption without detailed, temporized trace information become a major challenge for trace-based workload generation. To address this challenge, we propose STWGEN, a novel method that leverages statistical trace data for workload generation. STWGEN is specifically designed to generate the batch task workloads based on Alibaba trace. STWGEN contains two key components: a suite of C program-based flexible workload building blocks and a heuristic strategy to assemble building blocks for workload generation. Both components are carefully designed to reproduce synthetic batch tasks that closely replicate the observed resource usage patterns in a representative data center. Experimental results demonstrate that STWGEN outperforms state-of-the-art workload generation methods as it emulates workload-level and machine-level resource usage in much higher accuracy.

Background: Low-dose ionizing radiation-induced protection and damage are of great significance among radiation workers. We aimed to study the role of glutathione S-transferase Pi (GSTP1) in low-dose ionizing radiation damage and clarify the impact of ionizing radiation on the biological activities of cells. Results: In this study, we collected peripheral blood samples from healthy adults and workers engaged in radiation and radiotherapy and detected the expression of GSTP1 by qPCR. We utilized γ-rays emitted from uranium tailings as a radiation source, with a dose rate of 14 μGy/h. GM12878 cells subjected to this radiation for 7, 14, 21, and 28 days received total doses of 2.4, 4.7, 7.1, and 9.4 mGy, respectively. Subsequent analyses, including flow cytometry, MTS, and other assays, were performed to assess the ionizing radiation’s effects on cellular biological functions. In peripheral blood samples collected from healthy adults and radiologic technologist working in a hospital, we observed a decreased expression of GSTP1 mRNA in radiation personnel compared to the healthy controls. In cultured GM12878 cells exposed to low-dose ionizing radiation from uranium tailings, we noted significant changes in cell morphology, suppression of proliferation, delay in cell cycle progression, and increased apoptosis. These effects were partially reversed by overexpression of GSTP1. Moreover, low-dose ionizing radiation increased GSTP1 gene methylation and downregulated GSTP1 expression. Furthermore, low-dose ionizing radiation affected the expression of GSTP1-related signaling molecules. Conclusions: This study shows that low-dose ionizing radiation damages GM12878 cells and affects their proliferation, cell cycle progression, and apoptosis. In addition, GSTP1 plays a modulating role under low-dose ionizing radiation damage conditions. Low-dose ionizing radiation affects the expression of Nrf2, JNK, and other signaling molecules through GSTP1.

Purpose: Prolonged exposure to low dose-rate radiation (LDRR) is of growing concern to public health. Recent evidences indicates that LDRR causes deleterious health effects and is closely related to miRNAs. The aim of our study is to investigate the relationship between miRNAs and DNA damage caused by LDRR. Materials and methods: In this study, we irradiated C57BL/6J mice with 12.5μGy/h dose of γ ray emitted from uranium ore for 8 h a day for 120 days at a total dose of 12 mGy, and identified differentially expressed miRNAs from the mice long-term exposed to LDRR through isolating serum RNAs, constructing small RNA library, Illumina sequencing. To further investigate the role of differential miRNA under LDRR，we first built DNA damage model in Immortal B cells irradiated with 12.5μGy/h dose of γ ray for 28 days at a total dose of 9.4 mGy. Then, we chose the highly conserved miR-181c-3p among 12 miRNA and its mechanism in alleviating DNA damage induced by LDRR was studied by transfection, quantitative PCR, luciferase assay, and Western blot. Results and conclusions: We have found that 12 differentially expressed miRNAs including miR-181c-3p in serum isolated from irradiated mice. Analysis of GO and KEGG indicated that target genes of theses 12 miRNA enriched in pathways related to membrane, protein binding and cancer. Long-term exposure to LDRR induced upregulation of gamma-H2A histone family member X (γ-H2AX) expression, a classical biomarker for DNA damage in B cells. miR-181c-3p inhibited Leukemia inhibitory factor (LIF) expression via combining its 3‘UTR. LIF, MDM2, p53, and p-p53-s6 were upregulated after exposure to LDRR. In irradiated B cells, Transfection of miR-181c-3p reduced γ-H2AX expression and suppressed LIF and MDM2 protein levels, whereas p-p53-s6 expression was increased. As expected, the effect of LIF inhibition on irradiated B cells was similar to miR-181c-3p overexpression. Our results suggest that LDRR alters miRNA expression and induces DNA damage. Furthermore, miR-181c-3p can alleviate LDRR-induced DNA damage via the LIF/MDM2/p-p53-s6 pathway in human B lymphocytes. This could provide the basis for prevention and treatment of LDRR injury.

Increasing evidence indicates that the addictive use of social media can have a detrimental effect on marital satisfaction, due mainly to the decrease in time and focus given to one’s spouse. However, the impact of social media use among older couples remains under-investigated, and the research that does exist relies on individual-level data that do not allow the exploration of the dynamics between the dyadic partners. Therefore, the present study focused on older adults’ use of short-video platforms, as these have been shown to be particularly addictive for older adults. A sample of 264 older couples was gathered (meanage = 68.02, SD = 8.68), and both spouses completed surveys reporting addictive use of short-video platforms, negative emotions, and marital satisfaction. Using an actor–partner interdependence model, we found an asymmetrical dyadic process in that the addictive use of short-video platforms by the wives was not only related to their own negative emotions, but also those of their spouse, as well as to decreased marital satisfaction. Meanwhile, addictive use by the husbands seemed to relate only to their own increased negative emotions, as well as to decreased marital satisfaction. Together, the findings from this study reveal dyadic dynamics with delineated pathways through which the addictive use of short-video platforms can damage older couples’ interactive processes and marital satisfaction.

The carbon market is regarded as one of the important means to achieve China's dual carbon target. It has ancillary effect for reducing air pollution while regulating carbon emissions since climate change and air pollution share the same origin and homology. Research on how to design the carbon market mechanism in order to maximize the synergistic effect of reducing greenhouse gas and air pollution will have a very important practical impact for China. This study conducts a theoretical analysis of the collaborative emission reduction path of China's carbon market, and constructs an Energy-Economy-Environment (3E) model of the collaborative emission reduction effect of carbon trading system based on System Dynamics. After analyzing the feedback path of the core cycle of the model and verifying its performance, three main policy factors in the carbon market are explored, and their effects under the dual objectives of emission control and economic development are comprehensively evaluated. This study suggests that the exploration of the potential of carbon market for collaborative governance should be accelerated, and ensure the orderly expansion of coverage and precise setting of limits, so as to ensure the smooth achievement of carbon reduction targets while guaranteeing the social and economic development.

The extraction of indole from wash oil is of economic value and practical significance. In this study, deep eutectic solvents (DESs) are investigated to extract indole from wash oil. Initially, density functional theory calculations were used to explore the association of indole with aromatic compounds in wash oil, and the feasibility of indole extraction with a tetraethylammonium chloride/p-toluenesulfonic acid (TEAC/2TsOH) DES. Then, the influence of the temperature, stirring time, DESs dose, initial indole concentration, and amount of water added were investigated, followed by discussions on the comparison with other extractants and the performance of extracting of indole from real wash oil. The results show that indole associates with aromatic compounds in wash oil through π−π interactions, but that a large electrostatic interaction energy of −52.8 kJ/mol between indole and the DESs is the main driving force for the extraction process. Under optimal conditions, TEAC/2TsOH extracts more than 63.5 ± 3% of indole from wash oil, and its extraction capacity is superior to other extractants.