Graph Multi-Convolution and Attention Pooling for Graph Classification.

Many studies have achieved excellent performance in analyzing graph-structured data. However, learning graph-level representations for graph classification is still a challenging task. Existing graph classification methods usually pay less attention to the fusion of node features and ignore the effects of different-hop neighborhoods on nodes in the graph convolution process. Moreover, they discard some nodes directly during the graph pooling process, resulting in the loss of graph information. To tackle these issues, we propose a new Graph Multi-Convolution and Attention Pooling based graph classification method (GMCAP). Specifically, the designed Graph Multi-Convolution (GMConv) layer explicitly fuses node features learned from different perspectives. The proposed weight-based aggregation module combines the outputs of all GMConv layers, for adaptively exploiting the information over different-hop neighborhoods to generate informative node representations. Furthermore, the designed Local information and Global Attention based Pooling (LGAPool) utilizes the local information of a graph to select several important nodes and aggregates the information of unselected nodes to the selected ones by a global attention mechanism when reconstructing a pooled graph, thus effectively reducing the loss of graph information. Extensive experiments show that GMCAP outperforms the state-of-the-art methods on graph classification tasks, demonstrating that GMCAP can learn graph-level representations effectively.

Comparison of Brain Function and Structure in Patients with Major Depression: A Systematic Review and Meta-Analysis of MRI-Based Data.

BACKGROUND: Depression is a common mental illness worldwide. Neuroimaging techniques, such as magnetic resonance imaging and functional magnetic resonance imaging, play an essential role in diagnosing and evaluating depression. This study is based on magnetic resonance imaging (MRI)-related research to explore the comparison of brain function and structure between patients with severe depression and normal individuals, and to conduct meta-analysis. METHODS: We conducted searches in various databases such as PubMed, Web of Science, Embase, and Cochrane Library to obtain research data on comparing brain function and structure between patients with severe depression and healthy individuals. The search keywords included "Major Depressive Disorder", "Brain Function", "Brain Structure", "Depression", "MRI", and "Magnetic Resonance". The quality assessment was conducted using the bias risk assessment tool recommended by the Cochrane Collaborative Network. Literature was screened following the predetermined inclusion and exclusion criteria, and Anisotropic Effect-Size Seed-Based Differential Mapping (AES-SDM) was used for systematic meta-analysis. Regression analysis was performed on age, gender, disease duration, years of education, and treatment status. RESULTS: After a thorough screening process, 10 documents were selected for subsequent analysis. These studies consisted of 477 study subjects, including 231 depression patients and 246 healthy individuals. The proportion of women was 36%-75%, and the disease duration was 3-60 months. The patients in 4 documents had first attacks, and the patients in the other 6 documents had multiple attacks. The baseline conditions of the 10 included documents were consistent and comparable. None of the studies reported blinding methods, and none of the results had incomplete data. The Regional homogeneity (ReHo) levels in the left precuneus (BA7), lentiform nucleus (BA48), and left prefrontal lobe (BA32) were significantly increased in the depression group, with voxel numbers of 358, 116, and 181, respectively. Conversely, the left postcentral gyrus (BA4), left cerebellar area (hemispheric lobule I, IV/V, lingual gyrus, fusiform gyrus), left fusiform gyrus (BA30), and right cingulate gyrus (BA23) were significantly reduced, with voxel numbers of 17, 50, and 124, respectively. Furthermore, regression analysis showed that gender, age, disease duration, years of education, and disease severity were potential influencing factors, and the disease duration demonstrated the most significant impact on the left cingulate gyrus (SDM = 2.777). CONCLUSION: There are significant differences in brain function and structure between patients with major depression and healthy individuals. Furthermore, our findings reveal a substantial correlation between the severity of depressive symptoms and brain function and structure indicators. These findings provide novel research directions and ideas for the diagnosis and treatment of depression.

The relationship between social and psychological factors with cognitive impairment after stroke: a prospective study.

Objectives: To investigate the association between social and psychological factors and the risk of cognitive impairment following acute ischemic stroke. Materials and methods: A prospective study was conducted at Shanghai Tenth People's Hospital from June 2021 to July 2022. The study focused on social and psychological factors, which were assessed using the Social Support Rating Scale (SSRS), Self-Perceived Burden Scale (SPBS), and Hamilton Depression Scale (HAMD) within 3 days after admission to the hospital. Cognitive function was evaluated using the Montreal Cognitive Assessment at 3 months post-stroke. Logistic hierarchical regression models were used to examine the association between these three indicators and cognitive impairment following a stroke. Results: Among these patients, cognitive function was assessed in 211 cases at the 3-month follow-up after the initial stroke event. At 3 months post-stroke, 118(55.9%) of the participants experienced cognitive impairment, while 93(44.1%) did not. The scores on the SPBS and HAMD showed significant associations with cognitive impairment at 3 months after stroke. The scores of SPBS [scores: 30~39 vs.<20 points, odds ratio (OR)=2.993 (1.135-7.896); scores: ≥40 vs.<20points, OR=7.382 (1.117-48.799); P=0.043] and the HAMD [scores: >7 vs.≤7 points, OR=3.287(1.362~7.936); P=0.008]. There were no significant associations observed between SSRS and PSCI. Conclusion: Early screening for depressive symptoms and focusing on self-perceived burden can be beneficial for decision support for clinicians and improve cognitive function recovery at the 3-month mark post-stroke.

2D petal-like PdAg nanosheets promote efficient electrocatalytic oxidation of ethanol and methanol.

The development of efficient alcohol electrooxidation catalysts is of vital importance for the commercialization of direct liquid fuel cells. As emerging advanced catalysts, two-dimensional (2D) noble metal nanomaterials have attracted much research attention due to their intrinsic structural advantages. Herein, we report the synthesis of petal-like PdAg nanosheets (NSs) with an ultrathin 2D structure and jagged edges via a facile wet-chemical approach, combining doping engineering and morphology tuning. Notably, the highly active sites and Pd-Ag composition endowed PdAg NSs with improved toxicity tolerance and substantially improved the durability toward the ethanol/methanol oxidation reaction (EOR/MOR). Moreover, the electronic effect and synergistic effect significantly enhanced the EOR and MOR activities in comparison with Pd NSs and commercial Pd/C. This work provides efficient catalysts for fuel electrooxidations and deep insight into the rational design and fabrication of novel 2D nanoarchitecture.

A quadrivalent recombinant influenza Hemagglutinin vaccine induced strong protective immune responses in animal models.

Globally, influenza poses a substantial threat to public health, serving as a major contributor to both morbidity and mortality. The current vaccines for seasonal influenza are not optimal. A novel recombinant hemagglutinin (rHA) protein-based quadrivalent seasonal influenza vaccine, SCVC101, has been developed. SCVC101-S contains standard dose protein (15µg of rHA per virus strain) and an oil-in-water adjuvant, CD-A, which enhances the immunogenicity and cross-protection of the vaccine. Preclinical studies in mice, rats, and rhesus macaques demonstrate that SCVC101-S induces robust humoral and cellular immune responses, surpassing those induced by commercially available vaccines. Notably, a single injection with SCVC101-S can induce a strong immune response in macaques, suggesting the potential for a standard-dose vaccination with a recombinant protein influenza vaccine. Furthermore, SCVC101-S induces cross-protection immune responses against heterologous viral strains, indicating broader protection than current vaccines. In conclusion, SCVC101-S has demonstrated safety and efficacy in preclinical settings and warrants further investigation in human clinical trials. Its potential as a valuable addition to the vaccines against seasonal influenza, particularly for the elderly population, is promising.

Inhibition of LPS-Induced Skin Inflammatory Response and Barrier Damage via MAPK/NF-κB Signaling Pathway by Houttuynia cordata Thunb Fermentation Broth.

Houttuynia cordata Thunb is rich in active substances and has excellent antioxidant and anti-inflammatory activity. Scanning electron microscopy and gel permeation chromatography were used to analyze the molecular characteristics of the fermentation broth of Houttuynia cordata Thunb obtained through fermentation with Clavispora lusitaniae (HCT-f). The molecular weight of HCT-f was 2.64265 × 105 Da, and the polydispersity coefficient was 183.10, which were higher than that of unfermented broth of Houttuynia cordata Thunb (HCT). By investigating the active substance content and in vitro antioxidant activity of HCT-f and HCT, the results indicated that HCT-f had a higher active substance content and exhibited a superior scavenging effect on 2,2-diphenyl-1-picrylhydrazyl radicals and hydroxyl radicals, with IC50 values of 11.85% and 9.01%, respectively. Our results showed that HCT-f could effectively alleviate the increase in the secretion of inflammatory factors and apoptotic factors caused by lipopolysaccharide (LPS) stimulation, and had a certain effect on repairing skin barrier damage. HCT-f could exert an anti-inflammatory effect by down-regulating signaling in the MAPK/NF-κB pathway. The results of erythrocyte hemolysis and chicken embryo experiments showed that HCT-f had a high safety profile. Therefore, this study provides a theoretical basis for the application of HCT-f as an effective ingredient in food and cosmetics.

Flare: An FPGA-Based Full Precision Low Power CNN Accelerator with Reconfigurable Structure.

Convolutional neural networks (CNNs) have significantly advanced various fields; however, their computational demands and power consumption have escalated, posing challenges for deployment in low-power scenarios. To address this issue and facilitate the application of CNNs in power constrained environments, the development of dedicated CNN accelerators is crucial. Prior research has predominantly concentrated on developing low precision CNN accelerators using code generated from high-level synthesis (HLS) tools. Unfortunately, these approaches often fail to efficiently utilize the computational resources of field-programmable gate arrays (FPGAs) and do not extend well to full precision scenarios. To overcome these limitations, we integrate vector dot products to unify the convolution and fully connected layers. By treating the row vector of input feature maps as the fundamental processing unit, we balance processing latency and resource consumption while eliminating data rearrangement time. Furthermore, an accurate design space exploration (DSE) model is established to identify the optimal design points for each CNN layer, and dynamic partial reconfiguration is employed to maximize each layer's access to computational resources. Our approach is validated through the implementation of AlexNet and VGG16 on 7A100T and ZU15EG platforms, respectively. We achieve an average convolutional layer throughput of 28.985 GOP/s and 246.711 GOP/s for full precision. Notably, the proposed accelerator demonstrates remarkable power efficiency, with a maximum improvement of 23.989 and 15.376 times compared to current state-of-the-art FPGA implementations.

Graph Convolutional Networks With Adaptive Neighborhood Awareness.

Graph convolutional networks (GCNs) can quickly and accurately learn graph representations and have shown powerful performance in many graph learning domains. Despite their effectiveness, neighborhood awareness remains essential and challenging for GCNs. Existing methods usually perform neighborhood-aware steps only from the node or hop level, which leads to a lack of capability to learn the neighborhood information of nodes from both global and local perspectives. Moreover, most methods learn the nodes' neighborhood information from a single view, ignoring the importance of multiple views. To address the above issues, we propose a multi-view adaptive neighborhood-aware approach to learn graph representations efficiently. Specifically, we propose three random feature masking variants to perturb some neighbors' information to promote the robustness of graph convolution operators at node-level neighborhood awareness and exploit the attention mechanism to select important neighbors from the hop level adaptively. We also utilize the multi-channel technique and introduce a proposed multi-view loss to perceive neighborhood information from multiple perspectives. Extensive experiments show that our method can better obtain graph representation and has high accuracy.

Adaptive Fuzzy Tracking Control With Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems.

For strict-feedback systems with mismatched uncertainties, adaptive fuzzy control techniques are developed to provide global prescribed performance with prescribed-time convergence. First, a class of prescribed-time prescribed performance functions are designed to quantify the performance constraints of the tracking error. Additionally, a novel error transformation function is provided to eliminate the initial value limitations and resolve the singularity issue in previous research. To ensure the convergence of the tracking error into a prescribed bounded region within a prescribed time and satisfactory transient performance, controllers with or without approximating structures are established. Notably, the settling time and initial condition of the prescribed performance function are completely independent of the initial tracking error and system parameters, thereby improving upon existing results. Furthermore, the disadvantage of the semi-global boundedness of tracking error induced by dynamic surface control can be eliminated through the use of a novel Lyapunov-like energy function. Finally, the effectiveness of the proposed strategies is validated through numerical simulations performed on practical examples.

Ginsenoside Rh2 enhances immune surveillance of natural killer (NK) cells via inhibition of ERp5 in breast cancer.

BACKGROUND: One critical component of the immune system that prevents breast cancer cells from forming distant metastasis is natural killer (NK) cells participating in immune responses to tumors. Ginsenoside Rh2 (GRh2) as one of the major active ingredients of ginseng has been employed in treatment of cancers, but the function of GRh2 in modulating the development of breast cancer remains elusive. PURPOSE: This study was to dissect the effect of GRh2 against breast cancer and its potential mechanisms associated with NK cells, both in vitro and in vivo. METHODS: MDA-MB-231 and 4T1 cells were used to establish in situ and hematogenous mouse models. MDA-MB-231 and MCF-7 were respectively co-cultured with NK92MI cells or primary NK cells in vitro. Anti-tumor efficacy of GRh2 was verified by immunohistochemistry (IHC), Cell Counting Kit-8 (CCK8), high resolution micro-computed tomography (micro-CT) scanning of lungs and hematoxylin and eosin (H&E) staining. Lactate dehydrogenase (LDH) cytotoxicity assay, flow cytometry, in vivo depletion of NK cells, enzyme-linked immunosorbent assay (ELISA), western blot, quantitative reverse transcription polymerase chain reaction (qRT-PCR), immunofluorescence and cell transfection were performed for investigating the anti-tumor mechanisms of GRh2. Molecular docking, microscale thermophoresis (MST) and cellular thermal shift assay (CETSA) were employed to determine the binding between endoplasmic reticulum protein 5 (ERp5) and GRh2. RESULTS: We demonstrated that GRh2 exerted prominent impacts on retarding the growth and metastasis of breast cancer through boosting the cytotoxic function of NK cells, as validated by the elevated release of perforin, granzyme B and interferon-γ (IFN-γ). Mechanistical studies revealed that GRh2 was capable of diminishing the expression of ERp5 and GRh2 directly bound to ERp5 in MDA-MB-231 cells as well as on a recombinant protein level. GRh2 prevented the formation of soluble MICA (sMICA) and upregulated the expression level of MICA in vivo and in vitro. Importantly, the reduced lung metastasis of breast cancer by GRh2 was almost abolished upon the depletion of NK cells. Moreover, GRh2 was able to insert into the binding pocket of ERp5 directly. CONCLUSION: We firstly demonstrated that GRh2 played a pivotal role in augmenting NK cell activity by virtue of modulating the NKG2D-MICA signaling axis via directly binding to ERp5, and may be further optimized to a therapeutic agent for the treatment of breast cancer.

Fast Determination of Optimal Transmission Rate for Wireless Blockchain Networks: A Graph Convolutional Neural Network Approach.

One of the primary challenges in wireless blockchain networks is to ensure security and high throughput with constrained communication and energy resources. In this paper, with curve fitting on the collected blockchain performance dataset, we explore the impact of the data transmission rate configuration on the wireless blockchain system under different network topologies, and give the blockchain a utility function which balances the throughput, energy efficiency, and stale rate. For efficient blockchain network deployment, we propose a novel Graph Convolutional Neural Network (GCN)-based approach to quickly and accurately determine the optimal data transmission rate. The experimental results demonstrate that the average relative deviation between the blockchain utility obtained by our GCN-based method and the optimal utility is less than 0.21%.

A Multichannel Convolutional Decoding Network for Graph Classification.

Graph convolutional networks (GCNs) have shown superior performance on graph classification tasks, and their structure can be considered as an encoder-decoder pair. However, most existing methods lack the comprehensive consideration of global and local in decoding, resulting in the loss of global information or ignoring some local information of large graphs. And the commonly used cross-entropy loss is essentially an encoder-decoder global loss, which cannot supervise the training states of the two local components (encoder and decoder). We propose a multichannel convolutional decoding network (MCCD) to solve the above-mentioned problems. MCCD first adopts a multichannel GCN encoder, which has better generalization than a single-channel GCN encoder since different channels can extract graph information from different perspectives. Then, we propose a novel decoder with a global-to-local learning pattern to decode graph information, and this decoder can better extract global and local information. We also introduce a balanced regularization loss to supervise the training states of the encoder and decoder so that they are sufficiently trained. Experiments on standard datasets demonstrate the effectiveness of our MCCD in terms of accuracy, runtime, and computational complexity.

Risk Correlation Analysis between Polycystic Ovary Syndrome (PCOS) and Serum Visfatin Levels in Middle-Aged Women: Systematic Review and Meta-Analysis.

BACKGROUND: Polycystic ovary syndrome (PCOS) is an endocrine disorder that occurs frequently in women of childbearing age and is associated with insulin resistance. Serum visfatin can affect insulin resistance by binding to insulin receptors and further affect the occurrence and development of PCOS. In this study, we investigated the current status of serum visfatin levels in patients with PCOS through a literature search and meta-analysis. METHODS: We searched online Pubmed, Embase, Web of Science, the Cochrane Library, CNKI (China National Knowledge Infrastructure), CBMdisc (China Biology Medicine disc) databases and registered websites such as the ICTRP (International Clinical Trial Registration Platform) and clinicaltrials.gov (https://clinicaltrials.gov/) for case-control studies on PCOS and visfatin levels, assessed the quality of the included articles with the Newcastle-Ottawa Scale (NOS scale), and combined the comparison of serum visfatin levels between patients with PCOS and healthy individuals from high-quality studies. RESULTS: 20 research papers were included in the quantitative analysis of this study. The combined analysis showed that obese patients with PCOS had statistically significantly higher visfatin levels than healthy people [MD (mean difference) = 12.94, 95% CI (confidence interval) (6.52-19.37), Z = 3.95, p < 0.0001]. Visfatin levels were higher in non-obese patients with PCOS than in healthy people and are statistically significant [MD = 14.98, 95% CI (5.80-24.16), Z = 3.20, p = 0.001]. Heterogeneity in the combined analysis was not related to study location, the publication year of the literature, source of serum samples, but was influenced by the quality of the literature. After excluding the most influential papers, the combined analysis was conducted again, and the conclusion was consistent with that before the exclusion. The results of Egger's test showed no significant publication bias. CONCLUSIONS: High serum visfatin levels are a natural feature of PCOS and are not associated with obesity; Serum visfatin levels may be a potential marker for the diagnosis of PCOS, but their relationship with PCOS and insulin resistance remains worthy of in-depth investigation.

Analysis of the relationship between supply-demand matching of selected ecosystem services and urban spatial governance: a case study of Suzhou, China.

The mismatch between the supply and demand of ecosystem services has become a critical cause of the decline of urban ecological security. Studying the supply-demand matching of ecosystem services and exploring its association with urban spatial governance are imperative for ensuring sustainable urbanization. Taking Suzhou City as a case, the supply and demand values and matching degrees of five selected ecosystem services were assessed. Additionally, we explored the relationship between ecosystem services and urban spatial governance, with a focus on urban functional zoning. The findings indicate that first, the supply value of water production, food production, carbon sequestration, and tourism and leisure fall short of the demand value, while the supply value of air purification exceeds the demand value. The spatial matching of supply and demand shows a typical circular structure, with areas in short supply predominantly located in the downtown area and its vicinity. Second, the degree of coupling coordination between the supply-demand ratio of selected ecosystem services and the intensity of ecological control is low. Urban functional zoning can affect the supply-demand relationship of selected ecosystem services, and intensified development efforts can exacerbate the mismatch between supply and demand. Third, research on the supply-demand matching of selected ecosystem services can facilitate the assessment and regulation of urban functional zoning. Urban spatial governance can be regulated based on land use, industry, and population, with a focus on achieving a better supply-demand matching of ecosystem services. Through the analysis, this paper is aimed to provide reference for mitigating urban environmental problems and formulating sustainable urban development strategies.

Pseudomonas aeruginosa isolation is an important predictor for recurrent hemoptysis after bronchial artery embolization in patients with idiopathic bronchiectasis: a multicenter cohort study.

BACKGROUND: Nearly half of bronchiectasis patients receiving bronchial artery embolization (BAE) still have recurrent hemoptysis, which may be life-threatening. Worse still, the underlying risk factors of recurrence remain unknown. METHODS: A retrospective cohort was conducted of patients with idiopathic bronchiectasis who received BAE from 2015 to 2019 at eight centers. Patients were followed up for at least 24 months post BAE. Based on the outcomes of recurrent hemoptysis and recurrent severe hemoptysis, a Cox regression model was used to identify risk factors for recurrence. RESULTS: A total of 588 individuals were included. The median follow-up period was 34.0 months (interquartile range: 24.3-53.3 months). The 1-month, 1-year, 2-year, and 5-year cumulative recurrent hemoptysis-free rates were 87.2%, 67.5%, 57.6%, and 49.4%, respectively. The following factors were relative to recurrent hemoptysis: 24-h sputum volume (hazard ratio [HR] = 1.99 [95% confidence interval [95% CI]: 1.25-3.15, p = 0.015]), isolation of Pseudomonas aeruginosa (HR = 1.50 [95% CI: 1.13-2.00, p = 0.003]), extensive bronchiectasis (HR = 2.00 [95% CI: 1.29-3.09, p = 0.002]), and aberrant bronchial arteries (AbBAs) (HR = 1.45 [95% CI: 1.09-1.93, p = 0.014]). The area under the receiver operating characteristic curve of the nomogram was 0.728 [95% CI: 0.688-0.769]. CONCLUSIONS: Isolation of Pseudomonas aeruginosa is an important independent predictor of recurrent hemoptysis. The clearance of Pseudomonas aeruginosa might effectively reduce the hemoptysis recurrence rate.

Polydopamine-coated thalidomide nanocrystals promote DSS-induced murine colitis recovery through Macrophage M2 polarization together with the synergistic anti-inflammatory and anti-angiogenic effects.

High levels of proinflammatory cytokines, macrophage polarization status and immune-mediated angiogenesis play pivotal roles in the pathogenesis of inflammatory bowel disease (IBD). Thalidomide, an anti-inflammatory, immunomodulatory and antiangiogenic agent, is used off-label for treatment of IBD. The therapeutic potential of thalidomide is limited by its poor solubility and side effects associated with its systemic exposure. To address these issues and promote its therapeutic effects on IBD, thalidomide nanocrystals (Thali NCs) were prepared and coated with polydopamine (PDA), a potential macrophage polarization modulator, to form PDA coated Thali NCs (Thali@PDA). Thali@PDA possessed a high drug loading and displayed average particle size of 764.7 ± 50.30 nm. It showed a better anti-colitis effect than bare thalidomide nanocrystals at the same dose of thalidomide. Synergistic effects of polydopamine on anti-inflammatory and anti-angiogenic activities of thalidomide were observed. Furthermore, PDA coating could direct polarization of macrophages towards M2 phenotype, which boosted therapeutic effects of Thali@PDA on IBD. Upon repeated dosing of Thali@PDA for one week, symptoms of IBD in mice were significantly relieved, and histomorphology of the colitis colons were normalized. Key proinflammatory cytokine levels in the inflamed intestines were significantly decreased. Toxicity study also revealed that Thali@PDA is a safe formulation.

Specimen mammography for intraoperative margin assessment in breast conserving surgery: a meta-analysis.

In breast conserving surgery (BCS), specimen mammography is one of the most widely used intraoperative methods of assessing margin status. We performed a meta-analysis to evaluate the diagnostic accuracy of specimen mammography. Literature databases including PubMed, Cochrane Library, Web of Science, and EMBASE were searched prior to Jun 2022. A total of 1967 patients were included from 20 studies. A pooled analysis, heterogeneity testing, threshold effect testing, publication bias analysis, and subgroup analyses were performed from extracted data. The pooled weighted values were a sensitivity of 0.55 (95% confidence interval [CI], 0.47-0.63), a specificity of 0.85 (95% CI, 0.78-0.90), a diagnostic odds ratio of 7 (95% CI, 4-12), and a pooled positive likelihood ratio of 3.7 (95% CI 2.6-5.5). The area under the receiver operator characteristic curve was 0.75 (95% CI 0.71-0.78). In the subgroup analysis, the pooled specificity in the positive margin defined as tumor at margin subgroup was lower than the other positive margin definition subgroup (0.82 [95% CI: 0.71, 0.92] vs. 0.87 [95% CI: 0.80, 0.94], p = 0.01). Our findings indicated that specimen mammography was an accurate intraoperative imaging technique for margin assessment in BCS.

The association of healthy eating index with periodontitis in NHANES 2013-2014.

Background: Periodontal disease is very common worldwide and is one of the main causes of tooth loss in adults. Periodontal disease is characterized by chronic inflammation that can destroy adjacent alveolar bone and lead to a loss of periodontal ligaments. Although previous studies have found that a daily diet can influence the development of periodontal disease (e.g., a diet low in carbohydrates and rich in vitamins C and D and fiber can have a protective effect). Periodontal disease may present as gingivitis or periodontitis. However, studies on the role of healthy eating index in periodontitis are lacking. The purpose of this study was to assess the association between healthy eating index and periodontitis. Methods: We analyzed data collected from participants in the National Health and Nutrition Examination Survey (NHANES), a nationally representative survey conducted in 2-year cycles from 2013 to 2014. As part of our analysis, we developed multivariate logistic regression models to examine the independent association between the healthy eating index and periodontitis. We evaluated the significance of association using odds ratios (OR) with 95% confidence intervals (95%CI). Results: Individuals with a lower total healthy eating index had a higher prevalence of periodontitis. Adjusted multivariate regression models showed that a higher healthy diet index was associated with a lower prevalence of periodontitis (OR = 0.69, 95% CI: 0.55-0.86, P < 0.05). Conclusion: The results of the study showed that dietary structure was associated with the prevalence of periodontitis. Patients with a higher healthy eating index had a lower prevalence of periodontitis. These findings will need to be confirmed by longitudinal, prospective studies in the future.

Research on Risk Contagion among Financial Submarkets in China Based on Complex Networks.

As the COVID-19 outbreak has an impact on the global economy, there will be interest in how China's financial markets function during the outbreak. To investigate the path of risk contagion in China's financial sub-markets before and after the COVID-19 outbreak, we divided the 2016-2021 period into two phases. Based on the time of the COVID-19 outbreak, we divided the new stage of economic development into pre-epidemic and post-epidemic stages and employed the DCC-GARCH model to investigate the dynamic correlation coefficients among the financial sub-markets in China. Furthermore, we employed complex network theory and the minimum tree model to describe the risk contagion path between two-stage Chinese financial submarkets. Finally, we provided pertinent recommendations for investors and policymakers and conducted a brief discussion based on the findings of the research.