Periodontal Disease and Breast Cancer: A Meta-Analysis of 1,73,162 Participants

Objective: To investigate the correlation between periodontal disease and breast cancer.Materials and Methods: PubMed and China National Knowledge Infrastructure (CNKI) databases were searched up to February 8, 2018 for observational studies examining the association between periodontal disease and breast cancer. Study selection was conducted according to predesigned eligibility criteria, and two authors independently extracted data from included studies. Meta-analysis was performed using the Comprehensive Meta-Analysis v2 software and risk estimates were calculated as relative risks (RRs) with corresponding 95% confidence intervals (CIs).Results: A total of 11 study were included. Meta-analysis indicated that periodontal disease significantly increased the risk of breast cancer by 1.22-fold (RR = 1.22, 95% CI = 1.06–1.40). Amongst participants with periodontal patients and a history of periodontal therapy, the risk of developing breast cancer was not significant (RR = 1.23; 95% CI = 0.95–1.60). The association results between periodontal diseases and breast cancer were found to be robust, as evident in the leave-one-out sensitivity analysis.Conclusions: Periodontal disease may be a potential risk factor for the development of breast cancer among women, and thus effective periodontal therapy may present as a valuable preventive measure against breast cancer

Diaqua­bis(2-methyl-1H-imidazol-3-ium-4,5-dicarboxyl­ato-κ2 O,O′)magnesium

The title compound, [Mg(C6H5N2O4)2(H2O)2], was prepared by reaction of Mg(NO3)2 and 2-methyl-1H-imidazole-4,5-dicarboxylic acid under hydro­thermal conditions. The MgII atom lies on an inversion centre and displays a distorted octa­hedral coordination geometry. An extended three-dimensional network of inter­molecular O—H⋯O and N—H⋯O hydrogen bonds stabilizes the crystal structure

Experimental Test of Tracking the King Problem

In quantum theory, the retrodiction problem is not as clear as its classical counterpart because of the uncertainty principle of quantum mechanics. In classical physics, the measurement outcomes of the present state can be used directly for predicting the future events and inferring the past events which is known as retrodiction. However, as a probabilistic theory, quantum-mechanical retrodiction is a nontrivial problem that has been investigated for a long time, of which the Mean King Problem is one of the most extensively studied issues. Here, we present the first experimental test of a variant of the Mean King Problem, which has a more stringent regulation and is termed "Tracking the King". We demonstrate that Alice, by harnessing the shared entanglement and controlled-not gate, can successfully retrodict the choice of King's measurement without knowing any measurement outcome. Our results also provide a counterintuitive quantum communication to deliver information hidden in the choice of measurement.Comment: 16 pages, 5 figures, 2 table

Kernel-based nonlinear discriminant analysis using minimum squared errors criterion for multiclass and undersampled problems

It is well known that there exist two fundamental limitations in the linear discriminant analysis (LDA). One is that it cannot be applied when the within-class scatter matrix is singular, which is caused by the undersampled problem. The other is that it lacks the capability to capture the nonlinearly clustered structure of the data due to its linear nature. In this paper, a new kernel-based nonlinear discriminant analysis algorithm using minimum squared errors criterion (KDA-MSE) is proposed to solve these two problems. After mapping the original data into a higher-dimensional feature space using kernel function, the MSE criterion is used as the discriminant rule and the corresponding dimension reducing transformation is derived. Since the MSE solution does not require the scatter matrices to be nonsingular, the proposed KDA-MSE algorithm is applicable to the undersampled problem. Moreover, the new KDA-MSE algorithm can be applied to multiclass problem, whereas the existing MSE-based kernel discriminant methods are limited to handle twoclass data only. Extensive experiments, including object recognition and face recognition on three benchmark databases, are performed and the results demonstrate that our algorithm is competitive in comparison with other kernel-based discriminant techniques in terms of recognition accuracy. (C) 2009 Elsevier B.V. All rights reserved.National Natural Science Foundation of China [60672046, 60675002]; Fujian Province Science and Technology Foundation [2008H0036]; Specialized Research Fund for the Doctorol Program of Higher Educatio

RingMo-lite: A Remote Sensing Multi-task Lightweight Network with CNN-Transformer Hybrid Framework

In recent years, remote sensing (RS) vision foundation models such as RingMo have emerged and achieved excellent performance in various downstream tasks. However, the high demand for computing resources limits the application of these models on edge devices. It is necessary to design a more lightweight foundation model to support on-orbit RS image interpretation. Existing methods face challenges in achieving lightweight solutions while retaining generalization in RS image interpretation. This is due to the complex high and low-frequency spectral components in RS images, which make traditional single CNN or Vision Transformer methods unsuitable for the task. Therefore, this paper proposes RingMo-lite, an RS multi-task lightweight network with a CNN-Transformer hybrid framework, which effectively exploits the frequency-domain properties of RS to optimize the interpretation process. It is combined by the Transformer module as a low-pass filter to extract global features of RS images through a dual-branch structure, and the CNN module as a stacked high-pass filter to extract fine-grained details effectively. Furthermore, in the pretraining stage, the designed frequency-domain masked image modeling (FD-MIM) combines each image patch's high-frequency and low-frequency characteristics, effectively capturing the latent feature representation in RS data. As shown in Fig. 1, compared with RingMo, the proposed RingMo-lite reduces the parameters over 60% in various RS image interpretation tasks, the average accuracy drops by less than 2% in most of the scenes and achieves SOTA performance compared to models of the similar size. In addition, our work will be integrated into the MindSpore computing platform in the near future

Evodiamine Augments NLRP3 Inflammasome Activation and Anti-bacterial Responses Through Inducing α-Tubulin Acetylation

Evodiamine is a major ingredient of the plant Evodia rutaecarpa, which has long been used for treating infection-related diseases including diarrhea, beriberi and oral ulcer, but the underlying mechanism is unclear. Here we aimed to explore whether evodiamine influenced NLRP3 (NLR family, pyrin containing domain 3) inflammasome activation in macrophages, which is a critical mechanism for defending the host against pathogenic infections. We uncovered that evodiamine dose-dependently enhanced NLRP3 inflammasome activation in lipopolysaccharide-primed macrophages, as indicated by increased interleukin (IL)-1β production and caspase-1 cleavage, accompanied by increased ASC speck formation and pyroptosis. Mechanistically, evodiamine induced acetylation of α-tubulin around the microtubule organization center (indicated by γ-tubulin) in lipopolysaccharide-primed macrophages. Such evodiamine-mediated increases in NLRP3 activation and pyroptosis were attenuated by activators of α-tubulin deacetylase, resveratrol and NAD+, or dynein-specific inhibitor ciliobrevin A. Small interfering RNA knockdown of αTAT1 (the gene encoding α-tubulin N-acetyltransferase) expression, which reduced α-tubulin acetylation, also diminished evodiamine-mediated augmentation of NLRP3 activation and pyroptosis. Evodiamine also enhanced NLRP3-mediated production of IL-1β and neutrophil recruitment in vivo. Moreover, evodiamine administration evidently improved survival of mice with lethal bacterial infection, accompanied by increased production of IL-1β and interferon-γ, decreased bacterial load, and dampened liver inflammation. Resveratrol treatment reversed evodiamine-induced increases of IL-1β and interferon-γ, and decreased bacterial clearance in mice. Collectively, our results indicated that evodiamine augmented the NLRP3 inflammasome activation through inducing α-tubulin acetylation, thereby conferring intensified innate immunity against bacterial infection

Zigzag magnetic order in a novel tellurate compound Na4−δ_{4-\delta}NiTeO6_{6} with S\mathit{S} = 1 chains

Na$_{4-\delta}$NiTeO$_{6}$ is a rare example in the transition-metal tellurate family of realizing an $S$ = 1 spin-chain structure. By performing neutron powder diffraction measurements, the ground-state magnetic structure of Na$_{4-\delta}$NiTeO$_{6}$ is determined. These measurements reveal that below $T\rm_{N}$ ${\sim}$ 6.8(2) K, the Ni$^{2+}$ moments form a screwed ferromagnetic (FM) spin-chain structure running along the crystallographic $a$ axis but these FM spin chains are coupled antiferromagnetically along the $b$ and $c$ directions, giving rise to a magnetic propagation vector of $k$ = (0, 1/2, 1/2). This zigzag magnetic order is well supported by first-principles calculations. The moment size of Ni$^{2+}$ spins is determined to be 2.1(1) $\mu$$\rm_{B}$ at 3 K, suggesting a significant quenching of the orbital moment due to the crystalline electric field (CEF) effect. The previously reported metamagnetic transition near $H\rm_{C}$ ${\sim}$ 0.1 T can be understood as a field-induced spin-flip transition. The relatively easy tunability of the dimensionality of its magnetism by external parameters makes Na$_{4-\delta}$NiTeO$_{6}$ a promising candidate for further exploring various types of novel spin-chain physics.Comment: 10 pages, 6 figure

Construction and evaluation of endometriosis diagnostic prediction model and immune infiltration based on efferocytosis-related genes

Background: Endometriosis (EM) is a long-lasting inflammatory disease that is difficult to treat and prevent. Existing research indicates the significance of immune infiltration in the progression of EM. Efferocytosis has an important immunomodulatory function. However, research on the identification and clinical significance of efferocytosis-related genes (EFRGs) in EM is sparse.Methods: The EFRDEGs (differentially expressed efferocytosis-related genes) linked to datasets associated with endometriosis were thoroughly examined utilizing the Gene Expression Omnibus (GEO) and GeneCards databases. The construction of the protein-protein interaction (PPI) and transcription factor (TF) regulatory network of EFRDEGs ensued. Subsequently, machine learning techniques including Univariate logistic regression, LASSO, and SVM classification were applied to filter and pinpoint diagnostic biomarkers. To establish and assess the diagnostic model, ROC analysis, multivariate regression analysis, nomogram, and calibration curve were employed. The CIBERSORT algorithm and single-cell RNA sequencing (scRNA-seq) were employed to explore immune cell infiltration, while the Comparative Toxicogenomics Database (CTD) was utilized for the identification of potential therapeutic drugs for endometriosis. Finally, immunohistochemistry (IHC) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were utilized to quantify the expression levels of biomarkers in clinical samples of endometriosis.Results: Our findings revealed 13 EFRDEGs associated with EM, and the LASSO and SVM regression model identified six hub genes (ARG2, GAS6, C3, PROS1, CLU, and FGL2). Among these, ARG2, GAS6, and C3 were confirmed as diagnostic biomarkers through multivariate logistic regression analysis. The ROC curve analysis of GSE37837 (AUC = 0.627) and GSE6374 (AUC = 0.635), along with calibration and DCA curve assessments, demonstrated that the nomogram built on these three biomarkers exhibited a commendable predictive capacity for the disease. Notably, the ratio of nine immune cell types exhibited significant differences between eutopic and ectopic endometrial samples, with scRNA-seq highlighting M0 Macrophages, Fibroblasts, and CD8 Tex cells as the cell populations undergoing the most substantial changes in the three biomarkers. Additionally, our study predicted seven potential medications for EM. Finally, the expression levels of the three biomarkers in clinical samples were validated through RT-qPCR and IHC, consistently aligning with the results obtained from the public database.Conclusion: we identified three biomarkers and constructed a diagnostic model for EM in this study, these findings provide valuable insights for subsequent mechanistic research and clinical applications in the field of endometriosis

Involvement and repair of epithelial barrier dysfunction in allergic diseases

The epithelial barrier serves as a critical defense mechanism separating the human body from the external environment, fulfilling both physical and immune functions. This barrier plays a pivotal role in shielding the body from environmental risk factors such as allergens, pathogens, and pollutants. However, since the 19th century, the escalating threats posed by environmental pollution, global warming, heightened usage of industrial chemical products, and alterations in biodiversity have contributed to a noteworthy surge in allergic disease incidences. Notably, allergic diseases frequently exhibit dysfunction in the epithelial barrier. The proposed epithelial barrier hypothesis introduces a novel avenue for the prevention and treatment of allergic diseases. Despite increased attention to the role of barrier dysfunction in allergic disease development, numerous questions persist regarding the mechanisms underlying the disruption of normal barrier function. Consequently, this review aims to provide a comprehensive overview of the epithelial barrier’s role in allergic diseases, encompassing influencing factors, assessment techniques, and repair methodologies. By doing so, it seeks to present innovative strategies for the prevention and treatment of allergic diseases