Higher FOXP3-TSDR demethylation rates in adjacent normal tissues in patients with colon cancer were associated with worse survival

BACKGROUND: The influence of natural regulatory T cells (nTregs) on the patients with colon cancer is unclear. Demethylated status of the Treg-specific demethylated region (TSDR) of the FOXP3 gene was reported to be a potential biomarker for the identification of nTregs. METHODS: The demethylation rate of the TSDR (TSDR-DMR) was calculated by using methylation-specific quantitative polymerase chain reaction (MS-qPCR) assay. The expression of TSDR-DMR and FOXP3 mRNA was investigated in various colorectal cancer cell lines. A total of 130 colon carcinoma samples were utilized to study the DMR at tumor sites (DMR(T)) and adjacent normal tissue (DMR(N)). The correlations between DMRs and clinicopathological variables of patients with colon cancer were studied. RESULTS: The TSDR-DMRs varied dramatically among nTregs (97.920 ± 0.466%) and iTregs (3.917 ± 0.750%). Significantly, DMR(T) (3.296 ± 0.213%) was higher than DMR(N) (1.605 ± 0.146%) (n = 130, p = 0.000). Higher DMR(N) levels were found in female patients (p = 0.001) and those with distant metastases (p = 0.017), and were also associated with worse recurrence-free survival in non-stage IV patients (low vs. high, p = 0.022). However, further Cox multivariate analysis revealed that the FOXP3-TSDR status does not have prognostic value. CONCLUSION: MS-qPCR assays of FOXP3-TSDR can efficiently distinguish nTregs from non-nTregs. Abnormal recruitment of nTregs occurs in the local tumor microenvironment. Infiltration of tissue-resident nTregs may have a negative role in anti-tumor effects in patients with colon cancer; however, this role is limited and complicated

Effect of the Sodium Silicate Modulus and Slag Content on Fresh and Hardened Properties of Alkali-Activated Fly Ash/Slag

This paper presents the results of an experimental study performed to investigate the effect of activator modulus (SiO2/Na2O) and slag addition on the fresh and hardened properties of alkali-activated fly ash/slag (AAFS) pastes. Four activator moduli (SiO2/Na2O), i.e., 0.0, 1.0, 1.5, and 2.0, and five slag-to-binder ratios, i.e., 0, 0.3, 0.5, 0.7, 1.0, were used to prepare AAFS mixtures. The setting time, flowability, heat evolution, compressive strength, microstructure, and reaction products of AAFS pastes were studied. The results showed that the activator modulus and slag content had a combined effect on the setting behavior and workability of AAFS mixtures. Both the activator modulus and slag content affected the types of reaction products formed in AAFS. The coexistence of N-A-S-H gel and C-A-S-H gel was identified in AAFS activated with high pH but low SiO2 content (low modulus). C-A-S-H gel had a higher space-filling ability than N-A-S-H gel. Thus, AAFS with higher slag content had a finer pore structure and higher heat release (degree of reaction), corresponding to a higher compressive strength. The dissolution of slag was more pronounced when NaOH (modulus of 0.0) was applied as the activator. The use of Na2SiO3 as activator significantly refined the pores in AAFS by incorporating soluble Si in the activator, while further increasing the modulus from 1.5 to 2.0 prohibited the reaction process of AAFS, resulting in a lower heat release, coarser pore structure, and reduced compressive strength. Therefore, in view of the strength and microstructure, the optimum modulus is 1.5

Enhanced Detection Classification via Clustering SVM for Various Robot Collaboration Task

We introduce an advanced, swift pattern recognition strategy for various multiple robotics during curve negotiation. This method, leveraging a sophisticated k-means clustering-enhanced Support Vector Machine algorithm, distinctly categorizes robotics into flying or mobile robots. Initially, the paradigm considers robot locations and features as quintessential parameters indicative of divergent robot patterns. Subsequently, employing the k-means clustering technique facilitates the efficient segregation and consolidation of robotic data, significantly optimizing the support vector delineation process and expediting the recognition phase. Following this preparatory phase, the SVM methodology is adeptly applied to construct a discriminative hyperplane, enabling precise classification and prognostication of the robot category. To substantiate the efficacy and superiority of the k-means framework over traditional SVM approaches, a rigorous cross-validation experiment was orchestrated, evidencing the former's enhanced performance in robot group classification.Comment: This paper has been received by CISCE 2024 Conferenc

Progress in antibacterial applications of nanozymes

Bacterial infections are a growing problem, and antibiotic drugs can be widely used to fight bacterial infections. However, the overuse of antibiotics and the evolution of bacteria have led to the emergence of drug-resistant bacteria, severely reducing the effectiveness of treatment. Therefore, it is very important to develop new effective antibacterial strategies to fight multi-drug resistant bacteria. Nanozyme is a kind of enzyme-like catalytic nanomaterials with unique physical and chemical properties, high stability, structural diversity, adjustable catalytic activity, low cost, easy storage and so on. In addition, nanozymes also have excellent broad-spectrum antibacterial properties and good biocompatibility, showing broad application prospects in the field of antibacterial. In this paper, we reviewed the research progress of antibacterial application of nanozymes. At first, the antibacterial mechanism of nanozymes was summarized, and then the application of nanozymes in antibacterial was introduced. Finally, the challenges of the application of antibacterial nanozymes were discussed, and the development prospect of antibacterial nanozymes was clarified

Monocular Localization with Semantics Map for Autonomous Vehicles

Accurate and robust localization remains a significant challenge for autonomous vehicles. The cost of sensors and limitations in local computational efficiency make it difficult to scale to large commercial applications. Traditional vision-based approaches focus on texture features that are susceptible to changes in lighting, season, perspective, and appearance. Additionally, the large storage size of maps with descriptors and complex optimization processes hinder system performance. To balance efficiency and accuracy, we propose a novel lightweight visual semantic localization algorithm that employs stable semantic features instead of low-level texture features. First, semantic maps are constructed offline by detecting semantic objects, such as ground markers, lane lines, and poles, using cameras or LiDAR sensors. Then, online visual localization is performed through data association of semantic features and map objects. We evaluated our proposed localization framework in the publicly available KAIST Urban dataset and in scenarios recorded by ourselves. The experimental results demonstrate that our method is a reliable and practical localization solution in various autonomous driving localization tasks

A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer

Purpose: To evaluate the potential of machine learning (ML)-based radiomics approach for predicting tumor mutation burden (TMB) in gastric cancer (GC).Methods: The contrast enhanced CT (CECT) images with corresponding clinical information of 256 GC patients were retrospectively collected. Patients were separated into training set (n = 180) and validation set (n = 76). A total of 3,390 radiomics features were extracted from three phases images of CECT. The least absolute shrinkage and selection operator (LASSO) model was used for feature screening. Seven machine learning (ML) algorithms were employed to find the optimal classifier. The predictive ability of radiomics model (RM) was evaluated with receiver operating characteristic. The correlation between RM and TMB values was evaluated using Spearman’s correlation coefficient. The explainability of RM was assessed by the Shapley Additive explanations (SHAP) method.Results: Logistic regression algorithm was chosen for model construction. The RM showed good predictive ability of TMB status with AUCs of 0.89 [95% confidence interval (CI): 0.85–0.94] and 0.86 (95% CI: 0.74–0.98) in the training and validation sets. The correlation analysis revealed a good correlation between RM and TMB levels (correlation coefficient: 0.62, p < 0.001). The RM also showed favorable and stable predictive accuracy within the cutoff value range 6–16 mut/Mb in both sets.Conclusion: The ML-based RM offered a promising image biomarker for predicting TMB status in GC patients