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In this study, we investigated the acute exacerbation and outcomes of COPD patients during the outbreak of COVID-19 and evaluated the prevalence and mortality of COPD patients with confirmed COVID-19. A prospectively recruited cohort of 489 COPD patients was retrospectively followed-up for their conditions during the COVID-19 pandemic from December 2019 to March 2020 in Hubei, China. In addition, the features of 821 discharged patients with confirmed COVID-19 were retrospectively analyzed. Of the 489 followed-up enrolled COPD patients, 2 cases were diagnosed as confirmed COVID-19, and 97 cases had exacerbations, 32 cases of which were hospitalized, and 14 cases died. Compared with the 6-month follow-up results collected 1 year ago, in 307 cases of this cohort, the rates of exacerbations and hospitalization of the 489 COPD patients during the last 4 months decreased, while the mortality rate increased significantly (2.86% vs 0.65%, p=0.023). Of the 821 patients with COVID-19, 37 cases (4.5%) had pre-existing COPD. Of 180 confirmed deaths, 19 cases (10.6%) were combined with COPD. Compared to COVID-19 deaths without COPD, COVID-19 deaths with COPD had higher rates of coronary artery disease and/or cerebrovascular diseases. Old age, low BMI and low parameters of lung function were risk factors of all-cause mortality for COVID-19 patients with pre-existing COPD. Our findings imply that acute exacerbations and hospitalizations of COPD patients were infrequent during the COVID-19 pandemic. However, COVID-19 patients with pre-existing COPD had a higher risk of all-cause mortality.

Epigallocatechin gallate (EGCG), a well-characterized catechin in green tea, has demonstrated anti-tumor effects in nasopharyngeal carcinoma (NPC) cells and can enhance their sensitivity to radiotherapy. However, the pharmacological targets and mechanisms through which EGCG acts on radiotherapy-resistant NPC remain to be fully elucidated. RNA sequencing, network pharmacology, experimental validation, molecular docking, and molecular dynamics simulations were employed to uncover the molecular mechanisms by which EGCG mitigates radiotherapy resistance in NPC, with a focus on identifying potential therapeutic targets. Mining of online databases revealed 21 common targets between radiotherapy resistance in NPC and EGCG, from which a protein-protein interaction (PPI) network was constructed using STRING. Gene Ontology (GO) analysis identified biological processes relevant to the treatment of radiotherapy-resistant NPC by EGCG, such as response to xenobiotic stimulus, response to hypoxia, and key pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG), including pathogen infection, lipid metabolism, and cancer. Prognostic analysis led to the selection of two core genes (ABCC1, CASP1) for model construction. Notably, an elevated risk score was correlated with increased tumor malignancy and poorer prognosis. Significant differences in the immune microenvironment and immune checkpoints were observed between high- and low-risk groups. Experimental validation in HK1 cells confirmed the regulatory effect of EGCG on the expression of core targets. Molecular docking analysis revealed substantial interactions between EGCG and these targets, with simulation studies further substantiating stable binding. These findings provide a theoretical framework for the molecular mechanisms by which EGCG counteracts radiotherapy resistance in NPC. The identified core targets (ABCC1, CASP1) may serve as critical references for drug development and functional additive research associated with EGCG.

Taurine is an amino acid with several physiological functions and has been shown to be involved in the anti-tumor of human nasopharyngeal carcinoma (NPC) cells. However, the role of taurine metabolism-related genes (TMRGs) in NPC has not been reported. We integrated data from the Genecards, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Expression Omnibus(GEO) databases to identify differentially expressed genes associated with taurine metabolism in NPC patients. Gene Ontology (GO) and KEGG analyses were conducted to investigate the underlying mechanisms. Subsequently, Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were performed to construct a taurine metabolism-related prognostic signature. Survival, medication sensitivity, and immunological microenvironment evaluations were performed to assess the prognostic utility of the model. Finally, immunohistochemistry (IHC) experiments were performed to validate the model’s prognostic reliability. In addition, we further verified the reliability of our research results through molecular docking and single-cell sequencing. Our prognostic model was based on three pivotal TMRGs (ABCB1, GORASP1, and EZH2). Functional analysis revealed a strong association between TMRGs and miRNAs in cancer. Notably, increased risk scores correlated with worsening tumor malignancy and prognosis. Significant disparities in immune microenvironment, immune checkpoints, and drug sensitivity were observed between the high- and low-risk groups. The protein expression patterns of the selected genes in clinical NPC samples were validated using immunohistochemistry. Molecular docking verified the interaction between these three core genes and taurine, which was further supported by single-cell sequencing showing significant expression variation among different cell clusters in NPC. We had elucidated the functions, therapeutic potential, and prognostic significance of three key genes related to taurine metabolism in NPC through multidimensional research and experimental validation. This research provided valuable insights and potential avenues for improved NPC management.

Background Mitochondria are the organelles responsible for energy metabolism and have a direct impact on neuronal function and survival. Mitochondrial abnormalities have been well characterized in Alzheimer Disease (AD). It is believed that mitochondrial fragmentation, due to impaired fission and fusion balance, likely causes mitochondrial dysfunction that underlies many aspects of neurodegenerative changes in AD. Mitochondrial fission and fusion proteins play a major role in maintaining the health and function of these important organelles. Mitofusion 2 (Mfn2) is one such protein that regulates mitochondrial fusion in which mutations lead to the neurological disease. Methods To examine whether and how impaired mitochondrial fission/fusion balance causes neurodegeneration in AD, we developed a transgenic mouse model using the CAMKII promoter to knockout neuronal Mfn2 in the hippocampus and cortex, areas significantly affected in AD. Results Electron micrographs of neurons from these mice show swollen mitochondria with cristae damage and mitochondria membrane abnormalities. Over time the Mfn2 cKO model demonstrates a progression of neurodegeneration via mitochondrial morphological changes, oxidative stress response, inflammatory changes, and loss of MAP2 in dendrites, leading to severe and selective neuronal death. In this model, hippocampal CA1 neurons were affected earlier and resulted in nearly total loss, while in the cortex, progressive neuronal death was associated with decreased cortical size. Conclusions Overall, our findings indicate that impaired mitochondrial fission and fusion balance can cause many of the neurodegenerative changes and eventual neuron loss that characterize AD in the hippocampus and cortex which makes it a potential target for treatment strategies for AD.

With the speedy advancement and accelerated popularization of 5G networks, the provision and request of services through mobile smart terminals have become a hot topic in the development of mobile service computing. In this scenario, an efficient and reasonable edge server deployment solution can effectively reduce the deployment cost and communication latency of mobile smart terminals, while significantly improving investment efficiency and resource utilization. Focusing on the issue of edge server placement in mobile service computing environment, this paper proposes an edge server deployment method based on optimal benefit quantity and genetic algorithm. This method is firstly, based on a channel selection strategy for optimal communication impact benefits, it calculates the quantity of edge servers which can achieve optimal benefit. Then, the issue of edge server deployment is converted to a dual-objective optimization problem under three constraints to find the best locations to deploy edge servers, according to balancing the workload of edge servers and minimizing the communication delay among clients and edge servers. Finally, the genetic algorithm is utilized to iteratively optimize for finding the optimal resolution of edge server deployment. A series of experiments are performed on the Mobile Communication Base Station Data Set of Shanghai Telecom, and the experimental results verify that beneath the limit of the optimal benefit quantity of edge servers, the proposed method outperforms MIP, K-means, ESPHA, Top-K, and Random in terms of effectively reducing communication delays and balancing workloads.

Introduction: Bacillus species are known for their ability to produce nanoparticles with various potential applications. Methods: In this study, we present a facile approach for the green synthesis of selenium nanoparticles (Se NPs) using the biogenic selenate-reducing bacterium Bacillus paramycoides 24522. We optimized the growth conditions and sodium selenite reduction efficiency (SSRE) of B. paramycoides 24522 using a response surface approach. Results: Se NPs were synthesized by reducing selenite ions with B. paramycoides 24522 at 37 °C, pH 6, and 140 r/min, resulting in stable red-colored Se NPs and maximal SSRE (99.12%). The synthesized Se NPs demonstrated lethality against Staphylococcus aureus and Escherichia coli with MICs of 400 and 600 μg/mL, and MBCs of 600 and 800 μg/mL, respectively, indicating the potential of Se NPs as antibacterial agents. Furthermore, the Se NPs showed promising antioxidant capabilities through scavenging DPPH radicals and reducing power. Discussion: This study highlights the environmentally friendly production of Se NPs using B. paramycoides 24522 and their possible applications in addressing selenium pollution, as well as in the fields of environment and biotechnology.

Purpose With the exponential growth of mobile applications, recommending suitable mobile applications to users becomes a critical challenge. Although existing methods have made achievements in mobile application recommendation by leveraging graph convolutional networks (GCNs), they suffer from two limitations: the reliance on a singular acquisition path leads to signal sparsity, and the neighborhood aggregation method exacerbates the adverse impact of noisy interactions. This paper aims to propose SMAR, a self-supervised mobile application recommendation approach based on GCN, which is designed to overcome existing challenges by using self-supervised learning to create an auxiliary task. Design/methodology/approach In detail, this method uses three distinct data augmentation techniques node dropout, edge dropout and random walk, which create varied perspectives of each node. Then compares these perspectives, aiming to ensure uniformity across different views of the same node while maintaining the differences between separate nodes. Ultimately, auxiliary task is combined with the primary supervised task using a multi-task learning framework, thereby refining the overall mobile application recommendation process. Findings Extensive experiments on two real datasets demonstrate that SMAR achieves better Recall and NDCG performances than other strong baselines, validating the effectiveness of the proposed method. Originality/value In this paper, the authors introduce self-supervised learning into mobile application recommendation approach based on GCNs. This method enhances traditional supervised tasks by using auxiliary task to provide additional information, thereby improving signal accuracy and reducing the influence of noisy interactions in mobile application recommendations.

Purpose With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed. Design/methodology/approach In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked. Findings Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR. Originality/value In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.

Oxidative stress contributes to the formation of cataracts. The leucine rich repeat containing G protein-coupled receptor 4 (LGR4, also known as GPR48), is important in many developmental processes. Since deletion of Lgr4 has previously been shown to lead to cataract formation in mice, we sought to determine the specific role that Lgr4 plays in the formation of cataracts. Initially, the lens opacities of Lgr4(-/-) mice at different ages without ocular anterior segment dysgenesis (ASD) were evaluated with slit-lamp biomicroscopy. Lenses from both Lgr4(-/-) and wild-type mice were subjected to oxidation induced protein denaturation to assess the ability of the lens to withstand oxidation. The expression of antioxidant enzymes was evaluated with real-time quantitative PCR. Phenotypically, Lgr4(-/-) mice showed earlier onset of lens opacification and higher incidence of cataract formation compared with wild-type mice of similar age. In addition, Lgr4(-/-) mice demonstrated increased sensitivity to environmental oxidative damage, as evidenced by altered protein expression. Real-time quantitative PCR showed that two prominent antioxidant defense enzymes, catalase (CAT) and superoxidase dismutase-1 (SOD1), were significantly decreased in the lens epithelial cells of Lgr4(-/-) mice. Our results suggest that the deletion of Lgr4 can lead to premature cataract formation, as well as progressive deterioration with aging. Oxidative stress and altered expression of several antioxidant defense enzymes contribute to the formation of cataracts.