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Abstract Background Dauricine (DAU), a benzyl tetrahydroisoquinoline alkaloid isolated from the root of Menispermum dauricum DC, exhibits promising anti-Alzheimer’s disease (AD) effects, but its underlying mechanisms remain inadequately investigated. This paper aims to identify potential targets and molecular mechanisms of DAU in AD treatment. Methods Network pharmacology and molecular docking simulation method were used to screen and focus core targets. Various transgenic Caenorhabditis elegans models were chosen to validate the anti-AD efficacy and mechanism of DAU. Results There are 66 potential DAU-AD target intersections identified from 100 DAU and 3036 AD-related targets. Subsequent protein-protein interaction (PPI) network analysis identified 16 core targets of DAU for anti-AD. PIK3CA, AKT1 and mTOR were predicted to be the central targets with the best connectivity through the analysis of “compound-target-biological process-pathway network”. Molecular docking revealed strong binding affinities between DAU and PIK3CA, AKT1, and mTOR. In vivo experiments demonstrated that DAU effectively reduced paralysis in AD nematodes caused by Aβ aggregation toxicity, downregulated expression of PIK3CA, AKT1, and mTOR homologues (age-1, akt-1, let-363), and upregulated expression of autophagy genes and the marker protein LGG-1. Simultaneously, DAU increased lysosomal content and enhanced degradation of the autophagy-related substrate protein P62. Thioflavin T（Th-T）staining experiment revealed that DAU decreased Aβ accumulation in AD nematodes. Further experiments also confirmed DAU’s protein scavenging activity in polyglutamine (polyQ) aggregation nematodes. Conclusion Collectively, the mechanism of DAU against AD may be related to the activation of the autophagy-lysosomal protein clearance pathway, which contributes to the decrease of Aβ aggregation and the restoration of protein homeostasis.

IntroductionParental absence is greatly associated with school bullying victimization of left-behind children (LBC) in migrant families. With the increasing popularity of the Internet, little is known about the association between parental migration and cyberbullying victimization, and potential mediators.MethodsWe conducted a cross-sectional study in Anhui and Zhejiang Province, China, in 2020. With a sample of 792 currently left-behind children (CLBC), 541 previously left-behind children (PLBC), and 628 never left-behind children (NLBC), path analysis was used to explore the association between parental migration and cyberbullying victimization among children, while considering the independent and sequential mediating roles of parent-child communication, and time spent online.ResultsThe prevalence of cyberbullying victimization was 29.3% among CLBC, 29.2% among PLBC, and 23.4% among NLBC. Path analysis showed that current left-behind status was positively associated with cyberbullying victimization among children (p = 0.024). Furthermore, current left-behind status was associated with worse parent-child communication, which, in turn, predicted a higher prevalence of cyberbullying victimization [95% CI = (0.007, 0.036)]. Similarly, the previous left-behind experience was associated with worse parent-child communication, which, in turn, predicted a higher prevalence of cyberbullying victimization [95% CI = (0.013, 0.043)]. Current left-behind status was associated with increased time spent online, which, in turn, predicted a higher prevalence of cyberbullying victimization [95% CI = (0.013, 0.038)]. Additionally, the current left-behind status positively predicted cyberbullying victimization among children through the serial mediating roles of parent-child communication and time spent online [95% CI = (0.001, 0.006)]. Similarly, previous left-behind experience positively predicted cyberbullying victimization among children through the serial mediating roles of parent-child communication and time spent online [95% CI = (0.002, 0.007)].DiscussionWe propose that to protect CLBC and PLBC from cyberbullying victimization, it is of great importance for migrant parents to regulate children's time spent online and promote daily parent-child communication.

Against the backdrop of global warming, intensifying regional conflicts, deglobalization, and the spread of diseases, global food security is facing severe challenges. Studying the food security situation in China and the United States in depth can provide practical experience for formulating food security policies for countries around the world and promoting global food security governance. On the basis of a meticulous review of the evolving connotations of food security, this study adopts six dimensions—quantity security, quality security, circulation security, economic security, ecological resource security, and policy security—as breakthrough points to construct a framework consisting of food security evaluation indicator system comprising 29 specific indicators. The CRITIC–MEREC–MARCOS model is applied to evaluate the status of food security in China and the United States from 2000 to 2022, while the obstacle degree model (ODM) model is utilized to identify factors impeding food security between the two countries. The results indicate that the level of food security in China has shown slight fluctuations initially, followed by a steady upward trend. The gap with the United States is continuously narrowing. However, significant differences between China and the United States still exist in terms of economic security, ecological resource security, and policy security. Furthermore, due to the limited productivity of agricultural labor, scarcity of water and soil resources, and low efficiency in the use of fertilizers and pesticides, China’s food security is subject to economic and environmental constraints. The restrictions imposed by economic security and ecological resource security on China’s food security are showing an increasing trend year by year. For the United States, with the obstruction of grain exports and the increasing frequency of drought disasters, the impact of circulation security and ecological resource security on food security is becoming increasingly prominent. In the future, China and the United States should join hands to address challenges, actively promote international cooperation in food security, and drive sustainable development for humanity.

ABSTRACTMonoclonal antibodies (mAbs) and the post-exposure prophylaxis (PEP) with mAbs represent a very important public health strategy against coronavirus disease 2019 (COVID-19). This study has assessed a new Anti-SARS-COV-2 mAb (SA58) Nasal Spray for PEP against COVID-19 in healthy adults aged 18 years and older within three days of exposure to a SARS-CoV-2 infected individual. Recruited participants were randomized in a ratio of 3:1 to receive SA58 or placebo. Primary endpoints were laboratory-confirmed symptomatic COVID-19 within the study period. A total of 1222 participants were randomized and dosed (SA58, n = 901; placebo, n = 321). Median of follow-up was 2.25 and 2.79 days for SA58 and placebo, respectively. Adverse events occurred in 221 of 901 (25%) and 72 of 321 (22%) participants with SA58 and placebo, respectively. All adverse events were mild in severity. Laboratory-confirmed symptomatic COVID-19 developed in 7 of 824 participants (0.22 per 100 person-days) in the SA58 group vs. 14 of 299 (1.17 per 100 person-days) in the placebo group, resulting in an estimated efficacy of 80.82% (95%CI 52.41%−92.27%). There were 32 SARS-CoV-2 reverse transcriptase polymerase chain reaction (RT–PCR) positives (1.04 per 100 person-days) in the SA58 group vs. 32 (2.80 per 100 person-days) in the placebo group, resulting in an estimated efficacy of 61.83% (95%CI 37.50%−76.69%). A total of 21 RT–PCR positive samples were sequenced and all were the Omicron variant BF.7. In conclusion, SA58 Nasal Spray showed favourable efficacy and safety in preventing symptomatic COVID-19 or SARS-CoV-2 infection in adults who had exposure to SARS-CoV-2 within 72 h.

Background: This study aims to report the treatment process and results of the first officially published robotic peripheral vascular interventional surgery for lower extremity arterial occlusion in China. After a literature search, no domestic or foreign research reports have been found that share the same research content and innovation points. Methods: A 77-year-old female patient with intermittent claudication received drug balloon dilatation using a universal vascular interventional surgery robotic system on June 17, 2023, at Peking Union Medical College Hospital (PUMCH). Results: With the help of the vascular interventional robot, the operation successfully re-opened the patient's nearly occluded blood vessel, and the patient's discomfort symptoms were relieved. The total operation lasted 60 ​min, which time was similar to surgeries without robots. The postoperative hospital stay was 2 days. Conclusion: This case has preliminarily verified the effectiveness and safety of vascular interventional robotic surgery. The initial study fills in the gaps in related fields and lays a solid foundation for the future development of universal vascular interventional robots. Larger scale trials and more clinical trials are needed to test and support the clinical application of universal vascular interventional robots.

The COVID-19 outbreak devastated business operations and the world economy, especially for small and medium-sized enterprises (SMEs). With limited capital, poorer risk tolerance, and difficulty in withstanding prolonged crises, SMEs are more vulnerable to pandemics and face a higher risk of shutdown. This research sought to establish a model response to shutdown risk by investigating two questions: How do you measure SMEs’ shutdown risk due to pandemics? How do SMEs reduce shutdown risk? To the best of our knowledge, existing studies only analyzed the impact of the pandemic on SMEs through statistical surveys and trivial recommendations. Particularly, there is no case study focusing on an elaboration of SMEs’ shutdown risk. We developed a model to reduce cognitive uncertainty and differences in opinion among experts on COVID-19. The model was built by integrating the improved Dempster’s rule of combination and a Bayesian network, where the former is based on the method of weight assignment and matrix analysis. The model was first applied to a representative SME with basic characteristics for survival analysis during the pandemic. The results show that this SME has a probability of 79% on a lower risk of shutdown, 15% on a medium risk of shutdown, and 6% of high risk of shutdown. SMEs solving the capital chain problem and changing external conditions such as market demand are more difficult during a pandemic. Based on the counterfactual elaboration of the inferred results, the probability of occurrence of each risk factor was obtained by simulating the interventions. The most likely causal chain analysis based on counterfactual elaboration revealed that it is simpler to solve employee health problems. For the SMEs in the study, this approach can reduce the probability of being at high risk of shutdown by 16%. The results of the model are consistent with those identified by the SME respondents, which validates the model.

Background Circular RNAs (circRNAs) have been discovered in colorectal cancer (CRC), but there are few reports on the expression distribution and functional mining analysis of circRNAs. Methods Differentially expressed circRNAs in CRC tissues and adjacent normal tissues were screened and identified by microarray and qRT-PCR. ROC curves of the six circRNAs were analyzed. A series of bioinformatics analyses on differentially expressed circRNAs were performed. Results A total of 207 up-regulated and 357 down-regulated circRNAs in CRC were screened, and three top up-regulated and down-regulated circRNAs were chosen to be verified in 33 pairs of CRCs by qRT-PCR. 6 circRNAs showed high diagnostic values (AUC = 0.6860, AUC = 0.8127, AUC = 0.7502, AUC = 0.9945, AUC = 0.9642, AUC = 0.9486 for hsa_circRNA_100833, hsa_circRNA_103828, hsa_circRNA_103831 and hsa_circRNA_103752, hsa_circRNA_071106, hsa_circRNA_102293). A circRNA-miRNA-mRNA regulatory network (cirReNET) including six candidate circRNAs, 19 miRNAs and 210 mRNA was constructed, and the functions of the cirReNET were predicted and displayed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on these mRNAs and protein-protein interaction (PPI) network of the hub genes acquired by string and CytoHubba. Conclusion A cirReNET containing potential diagnostic and predictive indicators of CRCs and several critical circRNA-miRNA-mRNA regulatory axes (cirReAXEs) in CRC were mined, and may provide a novel route to study the mechanism and clinical targets of CRC.

Abstract Background The objectives of this study were to reveal the anthocyanin biosynthesis metabolic pathway in white and purple flowers of Salvia miltiorrhiza using metabolomics and transcriptomics, to identify different anthocyanin metabolites, and to analyze the differentially expressed genes involved in anthocyanin biosynthesis. Results We analyzed the metabolomics and transcriptomics data of S. miltiorrhiza flowers. A total of 1994 differentially expressed genes and 84 flavonoid metabolites were identified between the white and purple flowers of S. miltiorrhiza. Integrated analysis of transcriptomics and metabolomics showed that cyanidin 3,5-O-diglucoside, malvidin 3,5-diglucoside, and cyanidin 3-O-galactoside were mainly responsible for the purple flower color of S. miltiorrhiza. A total of 100 unigenes encoding 10 enzymes were identified as candidate genes involved in anthocyanin biosynthesis in S. miltiorrhiza flowers. Low expression of the ANS gene decreased the anthocyanin content but enhanced the accumulation of flavonoids in S. miltiorrhiza flowers. Conclusions Our results provide valuable information on the anthocyanin metabolites and the candidate genes involved in the anthocyanin biosynthesis pathways in S. miltiorrhiza.

Three-way decision (3WD) provides a new perspective for solving practical decision-making problems, which is in line with human's cognitive pattern. A covering information system (CIS) is an information system (IS) that consists of multiple coverings in the universe. A CIS with decision attributes which is seen as a covering decision information system (CDIS). This paper proposes three-way group decisions in a CDIS, as well as gives its application on the problem of position competition. First of all, the neighbourhood of every point in a CDIS is defined, and corresponding similarity class of this point is also obtained. Then, because of the uncertainty of risks, loss functions are acquired through group decision-making by means of interval numbers. Next, a method of three-way group decisions in a CDIS is presented. Eventually, the position competition is presented as an example to support our proposed decision-making method.

Projective non-negative matrix factorization (PNMF) as a variant of NMF has received considerable attention. However, the existing PNMF methods can be further improved from two aspects. On the one hand, the square loss function that is intended to measure the reconstruction error is sensitive to noise. On the other hand, it is non-trivial to estimate the intrinsic manifold of the feature space in a principal manner. So current paper is an attempt that has proposed a new method named as robust ensemble manifold projective non-negative matrix factorization (REPNMF) for image representation. Specifically, REPNMF not only assesses the influence of noise by imposing a spare noise matrix for image reconstruction, but it also assumes that the intrinsic manifold exists in a convex hull of certain pre-given manifold candidates. We aim to remove noise from the data and find the optimized combination of candidate manifolds to approximate the intrinsic manifold simultaneously. We develop iterative multiplicative updating rules for the optimization of REPNMF along with its convergence proof. The experimental results on four image datasets verify that REPNMF is superior as compare to other related state-of-the-art methods.

Forsythia suspensa is a traditional Chinese herb. Its numerous metabolites have important roles, as they possessed a wide range of biological activities. This study explored the accumulations of F. suspensa metabolites by performing widely targeted metabolomic analysis. The metabolites were studied at four stages of fruit development. Metabolites in the fruits and leaves of F. suspensa during fruit development included phenolic acids, flavonoids, lipids, lignans and coumarins, amino acids and their derivatives, terpenes, organic acids, nucleotides and their derivatives, alkaloids, quinones, steroids, and tannins. Fourteen Forsythia related metabolites were detected. Their contents varied among the developmental stages. Statistically significant correlations were found between the levels of forsythoside B and 11-methyl-forsythide, and forsythialan B and phillygenin, in both leaves and fruits. According to the correlation analysis between metabolites, Forsythia related metabolites were divided into two classes and five subclasses. In total, 33 compounds presented significant correlations in both fruits and leaves, which indicated the potential relationship in the synthesis of Forsythia related metabolites. Forsythialan B and phillygenin were both negatively correlated with L-valine, while Z-6,7-epoxyligustilid was positively correlated with both compounds. The quality control compounds forsythiaside A and phillyrin were positively and negatively correlated with uracil, respectively. These metabolomics results may facilitate the biosynthesis of Forsythia related metabolites.

Many diseases are accompanied by changes in certain biochemical indicators called biomarkers in cells or tissues. A variety of biomarkers, including proteins, nucleic acids, antibodies, and peptides, have been identified. Tumor biomarkers have been widely used in cancer risk assessment, early screening, diagnosis, prognosis, treatment, and progression monitoring. For example, the number of circulating tumor cell (CTC) is a prognostic indicator of breast cancer overall survival, and tumor mutation burden (TMB) can be used to predict the efficacy of immune checkpoint inhibitors. Currently, clinical methods such as polymerase chain reaction (PCR) and next generation sequencing (NGS) are mainly adopted to evaluate these biomarkers, which are time-consuming and expansive. Pathological image analysis is an essential tool in medical research, disease diagnosis and treatment, functioning by extracting important physiological and pathological information or knowledge from medical images. Recently, deep learning-based analysis on pathological images and morphology to predict tumor biomarkers has attracted great attention from both medical image and machine learning communities, as this combination not only reduces the burden on pathologists but also saves high costs and time. Therefore, it is necessary to summarize the current process of processing pathological images and key steps and methods used in each process, including: (1) pre-processing of pathological images, (2) image segmentation, (3) feature extraction, and (4) feature model construction. This will help people choose better and more appropriate medical image processing methods when predicting tumor biomarkers.

An information system is a database that represents relationships between objects and attributes. A set-valued information system is the generalized model of a single-valued information system. A set-value information system that contains probability distributions and missing values is called an incomplete probability set-value information system (IPSIS). Uncertainty measure is an effective tool for evaluation. This paper explores information structures and uncertainty measures in an IPSIS. According to the Bhattacharyya distance, the distance between two objects in a given subsystem of an IPSIS is first proposed. Then, the tolerance relation on an object set, induced by a probability set-valued information system by using this distance, is obtained. Next, the information structure of this subsystem is introduced by a set vector. Moreover, the dependence between two information structures is studied by using the inclusion degree. Finally, as an application for information structures, measures of uncertainty for an IPSIS are investigated, and to evaluate the performance of the proposed measures, effectiveness analysis is given from the angle of statistics. These results will be helpful for establishing a framework of granular computing and understanding the essence of uncertainty in an IPSIS.

Colorectal cancer (CRC) is one of the most malignant cancers with high morbidity and mortality. MicroRNAs (miRNAs) are small non-coding RNAs that affect biological processes by binding to mRNAs and regulating their expression, and epigenetic alterations including miRNA dysregulation are significantly involved in CRC development. Determining the effect of the miRNA-mRNA network on CRC could be helpful for developing novel therapeutic targets and prognostic biomarkers, and even improving survival. In this study, microarray assays were used to screen differentially expressed miRNAs (DE miRNAs) and mRNAs (DE mRNAs) in CRC and the adjacent normal tissues. Among the detected genes, 42 miRNAs and 142 mRNAs were significantly upregulated in CRC, while 23 miRNAs and 279 mRNAs were significantly downregulated. Through overlapping of predicted targets of DE miRNAs and anti-expressed DE mRNAs, networks of DE miRNAs and DE mRNAs in CRC were established. Additionally, the formation of a protein-protein interaction network of DE mRNAs possibly targeted by DE miRNAs, functional annotation and pathway analysis, stable subnetwork mining, and determination of hub genes provided the probable mechanism used by DE miRNAs and DE mRNAs to regulate CRC growth. Finally, validation of expression and prognostic potential of hub genes provided further support for the results above and indicated that CCL-28, GPR15, PNOC, NUSAP1, and their interacted miRNAs may be a potential signature for prognosis of CRC patients. In sum, we successfully established miRNA-mRNA regulatory networks based on microarray results targeting CRC, and these findings may elucidate the mechanisms used for CRC growth and identify miRNA-related signatures for prognosis and treatment of CRC.

Background: β-catenin plays a crucial role in the progression of osteosarcoma. However, the clinical significance of β-catenin over-expression in osteosarcoma still remains unclear. Thus, we performed a meta-analysis of studies that evaluated the impact of β-catenin on metastasis and overall survival (OS) in osteosarcoma.Methods: We searched PubMed, The Cochrane Library, Embase, Springer, Science Direct, OVID, Weipu, Wanfang and China National Knowledge Internet (CNKI) databases from their start year up to Aug.2019. Individual hazard ratios (HRs) and 95% confidence intervals (CIs) were extracted and pooled HRs with 95% CIs or odd ratio (OR) were used to evaluate the relationships between β-catenin over-expression and metastasis and overall survival in osteosarcoma.Results: Eight related studies involving 521 patients were qualified for this meta-analysis. Results showed that over-expression of β-catenin was significantly correlated with metastasis (OR = 3.31, 95% CI = 2.08–5.24, P < 0.001) and overall survival (HR = 2.32, 95% CI = 1.48–363, P = 0.02).Conclusion: The meta-analysis revealed that over-expression of β-catenin might be associated with distant metastasis and overall survival in osteosarcoma, which reminds that β-catenin acts as a prognostic biomarker and it can guide the clinical therapy in osteosarcoma patients.

This article concerns the Hamiltonian elliptic system $$\displaylines{ -\Delta u +V(x)u=H_{v}(x, u, v),\quad x\in \mathbb{R}^N, \cr -\Delta v +V(x)v=H_{u}(x, u, v),\quad x\in \mathbb{R}^N, \cr u(x)\to 0,\quad v(x)\to 0, \quad \text{as } |x|\to \infty, }$$ where $z=(u,v): \mathbb{R}^{N}\to\mathbb{R}\times\mathbb{R}$, $N\geq 3$ and the potential V(x) is allowed to be sign-changing. Under weak superquadratic assumptions for the nonlinearities, by applying the variant generalized weak linking theorem for strongly indefinite problem developed by Schechter and Zou, we obtain the existence of nontrivial and ground state solutions.

Brain inflammation is one of the main causes of epileptogenesis, a chronic process triggered by various insults, including genetic or acquired factors that enhance susceptibility to seizures. Amentoflavone, a naturally occurring biflavonoid compound that has anti-inflammatory effects, exerts neuroprotective effects against nervous system diseases. In the present study, we aimed to investigate the effects of amentoflavone on epilepsy in vivo and in vitro and elucidate the underlying mechanism. The chronic epilepsy model and BV2 microglial cellular inflammation model were established by pentylenetetrazole (PTZ) kindling or lipopolysaccharide (LPS) stimulation. Cognitive dysfunction was tested by Morris water maze while hippocampal neuronal apoptosis was evaluated by immunofluorescence staining. The levels of nucleotide oligomerization domain-like receptor protein 3 (NLRP3) inflammasome complexes and inflammatory cytokines were determined using quantitative real-time polymerase chain reaction, Western blotting, immunofluorescence staining, and enzyme-linked immunosorbent assay. Amentoflavone reduced seizure susceptibility, minimized PTZ-induced cognitive dysfunction, and blocked the apoptosis of hippocampal neurons in PTZ-induced kindling mice. Amentoflavone also inhibited the activation of the NLRP3 inflammasome and decreased the levels of inflammatory cytokines in the hippocampus of PTZ-induced kindling mice. Additionally, amentoflavone could alleviate the LPS-induced inflammatory response by inhibiting the NLRP3 inflammasome in LPS-induced BV2 microglial cells. Our results indicated that amentoflavone affects epileptogenesis and exerts neuroprotective effects by inhibiting the NLRP3 inflammasome and, thus, mediating the inflammatory process in PTZ-induced kindling mice and LPS-induced BV2 microglial cells. Therefore, amentoflavone may be a potential treatment option for epilepsy.

The ruin probability is used to determine the overall operating risk of an insurance company. Modeling risks through the characteristics of the historical data of an insurance business, such as premium income, dividends and reinvestments, can usually produce an integral differential equation that is satisfied by the ruin probability. However, the distribution function of the claim inter-arrival times is more complicated, which makes it difficult to find an analytical solution of the ruin probability. Therefore, based on the principles of artificial intelligence and machine learning, we propose a novel numerical method for solving the ruin probability equation. The initial asset u is used as the input vector and the ruin probability as the only output. A trigonometric exponential function is proposed as the projection mapping in the hidden layer, then a block trigonometric exponential neural network (BTENN) model with a symmetrical structure is established. Trial solution is set to meet the initial value condition, simultaneously, connection weights are optimized by solving a linear system using the extreme learning machine (ELM) algorithm. Three numerical experiments were carried out by Python. The results show that the BTENN model can obtain the approximate solution of the ruin probability under the classical risk model and the Erlang(2) risk model at any time point. Comparing with existing methods such as Legendre neural networks (LNN) and trigonometric neural networks (TNN), the proposed BTENN model has a higher stability and lower deviation, which proves that it is feasible and superior to use a BTENN model to estimate the ruin probability.

Soft set theory is a mathematical tool for handling uncertainty. This paper investigates the limits of the interval type of soft sets ( i t -soft sets). The notion of i t -soft sets is first introduced. Then, the limits of i t -soft sets are proposed and their properties obtained. Next, point-wise continuity of i t -soft sets and continuous i t -soft sets is discussed. Finally, an application for rough sets is given.