The associations of two novel inflammation indexes, systemic immune-inflammation index (SII) and system inflammation response index (SIRI), with periodontitis: evidence from NHANES 2009-2014.

OBJECTIVES: The research's goal is to look for any potential relationships between the systemic immune-inflammation index (SII) and the system inflammation response index (SIRI), along with inflammation indicators and the likelihood of periodontitis. METHODS: Ten thousand two hundred eighty-two individuals in sum were determined to be eligible for this cross-sectional study from the National Health and Nutrition Examination Survey (NHANES) between 2009 and 2014. Multiple logistic regression, generalized additive model, smooth curve fitting, subgroup analysis, and interaction tests were done for analyzing the association between periodontitis and SII, SIRI, and other inflammatory indicators. RESULTS: The analysis, adjusted for population weighting, revealed that individuals with moderate/severe periodontitis had SII levels of 545.46 (95% CI (529.10, 561.82), P = 0.0044) and SIRI levels of 1.33 (95% CI (1.29, 1.37), P < 0.0001). In a fully adjusted multivariate logistic regression model, SII was not sensibly associated with moderate/severe periodontitis among the continuous and quartile Q1-Q4 groups (OR = 0.97, 95% CI (0.91, 1.02)). The continuous variable of SIRI (OR = 1.11, 95% CI (1.06, 1.17)) and the quartile Q4 group (OR = 1.58, 95% CI (1.28, 1.94)) had a deemed significant positive association with moderate to severe periodontitis. In addition, other inflammatory indicators, especially NLR, PPN, PLR, MLR, PC, NC, and MC were observed to be notably involved moderate/severe periodontist in this research. CONCLUSION: We explored the association between periodontitis and two novel comprehensive markers of inflammation (SII and SIRI). CLINICAL RELEVANCE: These inflammatory markers are expected to serve as tools to assist clinicians in diagnosing periodontitis.

EC-YOLO: Improved YOLOv7 Model for PCB Electronic Component Detection.

Electronic components are the main components of PCBs (printed circuit boards), so the detection and classification of ECs (electronic components) is an important aspect of recycling used PCBs. However, due to the variety and quantity of ECs, traditional target detection methods for EC classification still have problems such as slow detection speed and low performance, and the accuracy of the detection needs to be improved. To overcome these limitations, this study proposes an enhanced YOLO (you only look once) network (EC-YOLOv7) for detecting EC targets. The network uses ACmix (a mixed model that enjoys the benefits of both self-attention and convolution) as a substitute for the 3 × 3 convolutional modules in the E-ELAN (Extended ELAN) architecture and implements branch links and 1 × 1 convolutional arrays between the ACmix modules to improve the speed of feature retrieval and network inference. Furthermore, the ResNet-ACmix module is engineered to prevent the leakage of function data and to minimise calculation time. Subsequently, the SPPCSPS (spatial pyramid pooling connected spatial pyramid convolution) block has been improved by replacing the serial channels with concurrent channels, which improves the fusion speed of the image features. To effectively capture spatial information and improve detection accuracy, the DyHead (the dynamic head) is utilised to enhance the model's size, mission, and sense of space, which effectively captures spatial information and improves the detection accuracy. A new bounding-box loss regression method, the WIoU-Soft-NMS method, is finally suggested to facilitate prediction regression and improve the localisation accuracy. The experimental results demonstrate that the enhanced YOLOv7 net surpasses the initial YOLOv7 model and other common EC detection methods. The proposed EC-YOLOv7 network reaches a mean accuracy (mAP@0.5) of 94.4% on the PCB dataset and exhibits higher FPS compared to the original YOLOv7 model. In conclusion, it can significantly enhance high-density EC target recognition.

Mechanism of action of Coptidis Rhizome in treating periodontitis based on network pharmacology and in vitro validation.

OBJECTIVE: Explore the therapeutic mechanism of Coptidis Rhizome (CR) in periodontitis using network pharmacology, and validate it through molecular docking and in vitro experiments. METHODS: Screened potential active components and target genes of CR from TCMSP and Swiss databases. Identified periodontitis-related target genes using GeneCards. Found common target genes using Venny. Conducted GO and KEGG pathway analysis. Performed molecular docking and in vitro experiments using Berberine, the main active component of CR, on lymphocytes from healthy and periodontitis patients. Assessed effects on inflammatory factors using CCK-8, flow cytometry, and ELISA. RESULTS: Fourteen active components and 291 targets of CR were identified. 30 intersecting target genes with periodontitis were found. GO and KEGG analysis revealed oxidative stress response and IL-17 signaling pathway as key mechanisms. Molecular docking showed strong binding of Berberine with ALOX5, AKT1, NOS2, and TNF. In vitro experiments have demonstrated the ability of berberine to inhibit the expression of Th17 + and other immune related cells in LPS stimulated lymphocytes, and reduce the secretion of IL-6, IL-8, and IL-17. CONCLUSION: CR treats periodontitis through a multi-component, multi-target, and multi-pathway approach. Berberine, its key component, acts through the IL-17 signaling pathway to exert anti-inflammatory effects.

Red blood cell folate level is associated with periodontitis in American adults: results from the NHANES 2009-2014.

BACKGROUND: Red blood cell (RBC) folate is an indicator of long-term folate nutrition. Whether there is an association between RBC folate and periodontitis is unclear. This study aimed to use the NHANES database to determine whether RBC folate is associated with moderate/severe periodontitis. METHODS: A cross-sectional analysis of 10,151 participants in the NHANES database from 2009 to 2014 was performed. Multivariate logistic regression was used to analyze the independent relationship between RBC folate and moderate/severe periodontitis. The generalized additive model (GAM), restricted cubic splines (RCS), smooth curve fitting, and threshold effect analysis were used to explore the dose-response relationship and the potential nonlinear relationship between RBC folate and periodontitis. Finally, subgroup analysis and interaction tests were performed to determine the effect of covariates on the relationship between RBC folate and moderate/severe periodontitis. RESULTS: After adjusting for all confounders, there was a negative association between RBC folate concentration and moderate/severe periodontitis. The lowest fraction Q1 (< 360 ng/mL) of RBC folate concentration was used as the reference group, multivariable-adjusted ORs and 95% CIs of the second (360-463 ng/mL), third (464-569 ng/mL), fourth (570-732 ng/mL), and the highest quintile (> 733 ng/mL) categories were 0.88 (0.77, 1.01), 0.83 (0.72, 0.96), 0.77 (0.67, 0.90), 0.65 (0.56, 0.77) respectively. Additionally, a threshold nonlinear association was found between RBC folate (ng/mL) log2 transformation and moderate/severe periodontitis. CONCLUSION: This cross-sectional study revealed a negative relationship between RBC folate and moderate/severe periodontitis within a certain threshold range. Dentists and policymakers should pay closer attention to oral hygiene and health care for people with low or high RBC folate levels. Further causal and longitudinal research mechanisms are needed to validate our findings.

Towards advanced removal of organics in persulfate solution by heterogeneous iron-based catalyst: A review.

Heterogeneous iron-based catalysts have drawn increasing attention in the advanced oxidation of persulfates due to their abundance in nature, the lack of secondary pollution to the environment, and their low cost over the last a few years. In this paper, the latest progress in the research on the activation of persulfate by heterogeneous iron-based catalysts is reviewed from two aspects, in terms of synthesized catalysts (Fe0, Fe2O3, Fe3O4, FeOOH) and natural iron ore catalysts (pyrite, magnetite, hematite, siderite, goethite, ferrohydrite, ilmenite and lepidocrocite) focusing on efforts made to improve the performance of catalysts. The advantages and disadvantages of the synthesized catalysts and natural iron ore were summarized. Particular interests were paid to the activation mechanisms in the catalyst/PS/pollutant system for removal of organic pollutants. Future research challenges in the context of field application were also discussed.

Regulation of heterogeneous electron transfer reactivity by defect engineering through electrochemically induced brominating addition.

Enhancing the electrochemical activity of graphene holds great significance for expanding its applications in various electrochemistry fields. In this study, we have demonstrated a facile and quantitative approach for modulating the defect density of single-layer graphene (SLG) via an electrochemically induced bromination process facilitated by cyclic voltammetry. This controlled defect engineering directly impacts the heterogeneous electron transfer (HET) rate of SLG. By utilizing Raman spectroscopy and scanning electrochemical microscopy (SECM), we have established a correlation between the HET kinetics and both the defect density (nD) and mean distance between defects (LD) of SLG. The variation of the HET rate (k0) with the defect density manifested a distinctive three-stage behavior. Initially, k0 increased slightly with the increasing nD, and then it experienced a rapid increase as nD further increased. However, once the defect density surpassed a critical value of about 1.8 × 1012 cm-2 (LD < 4.2 nm), k0 decreased rapidly. Notably, the results revealed a remarkable 35-fold enhancement of k0 under the optimal defect density conditions compared to pristine SLG. This research paves the way for controllable defect engineering as a powerful strategy to enhance the electrochemical activity of graphene, opening up new possibilities for its utilization in a wide range of electrochemical applications.