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Excessive levels of heavy metal pollutants in the environment pose significant threats to human health and ecosystem stability. Consequently, the accurate and rapid detection of heavy metal ions is critically important. A AgNPs@CeO2/Nafion composite was prepared by dispersing nano-ceria (CeO2) in a Nafion solution and incorporating silver nanoparticles (AgNPs). The morphology, microstructure, and electrochemical properties of the modified electrode materials were systematically characterized using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), and cyclic voltammetry (CV). By leveraging the oxygen vacancies and high electron transfer efficiency of CeO2, the strong adsorption capacity of Nafion, and the superior conductivity of AgNPs, an AgNPs@CeO2/Nafion/GCE electrochemical sensor was developed. Under optimized conditions, trace Pb2+ in water was detected using square wave anodic stripping voltammetry (SWASV). The sensor demonstrated a linear response for Pb2+ within the concentration range of 1–100 μg·L−1, with a detection limit of 0.17 μg·L−1 (S/N = 3). When applied to real water samples, the method achieved recovery rates between 93.7% and 110.3%, validating its reliability and practical applicability.

The integration of semantic simultaneous localization and mapping (SLAM) with 3D object detection in indoor scenes is a significant challenge in the field of robot perception. Existing methods typically rely on expensive sensors and lack robustness and accuracy in complex environments. To address this, this paper proposes a novel 3D semantic SLAM framework that integrates Oriented FAST and Rotated BRIEF-SLAM2 (ORB-SLAM2), 3D object detection, and multi-object tracking (MOT) techniques to achieve efficient and robust semantic environment modeling. Specifically, we employ an improved 3D object detection network to extract semantic information and enhance detection accuracy through category balancing strategies and optimized loss functions. Additionally, we introduce MOT algorithms to filter and track 3D bounding boxes, enhancing stability in dynamic scenes. Finally, we deeply integrate 3D semantic information into the SLAM system, achieving high-precision 3D semantic map construction. Experiments were conducted on the public dataset SUNRGBD and two self-collected datasets (robot navigation and XR glasses scenes). The results show that, compared with the current state-of-the-art methods, our method demonstrates significant advantages in detection accuracy, localization accuracy, and system robustness, providing an effective solution for low-cost, high-precision indoor semantic SLAM.

Previous research works indicate that natural exopolysaccharide (EPS) produced by lactic acid bacteria Lactobacillus plantarum HY7714 might be effective at anti-photoaging and due to its function as antioxidants against reactive oxygen species (ROS). Through regulating the expression level of FTH1, intracellular iron dyshomeostasis induced by UV irradiation might be relieved. This research paper investigates on the regulation of exopolysaccharides on FTH1 expression, ferritin concentration and level of lipid peroxidation in HaCaT cells under UVA+UVB exposure. In our research, we found that HaCaT cell viability is higher when treated with exopolysaccharides, which indicates that exopolysaccharides could effectively improve cell viability under UV radiation. Also, the presence of EPS and FTH1 are shown to decrease lipid peroxidation level and reduce intracellular ferritin concentration, which demonstrates that EPS could upregulate the FTH1 expression level to mediate the ferritin concentration in the HaCaT cells which would further balance iron homeostasis. Ultimately, EPS are proved to decrease cell death under UV radiation through modulating FTH1 protein expression.

Background: With the progress of the times, teenagers' social ways have changed, and they are more dependent on online social communication, thus ignoring the reality, resulting in serious social anxiety, which is closely related to their self-esteem. Methods: A total of 16 articles on this relationship were included through literature search and screening. Results: Self-esteem and social anxiety are closely related, showing negative correlation, and network dependence is the intermediary relationship between them. It is also found that the level of self-esteem formed by different genders can also affect the level of social anxiety. Males generally have higher self-esteem and lower level of social anxiety than Females. Conclusion: The level of self-esteem can help regulate social anxiety, Teenagers with low self-esteem are more likely to have social anxiety through Internet dependence, there is a direct relationship between the self-esteem and social anxiety. In the future researchers should further explore the causal relationship.

A binder-free Co3O4 nanoneedle electrode grown directly on nickel foam (Co3O4@NF) was fabricated by hydrothermal synthesis followed by calcination and evaluated for electrocatalytic nitrate reduction to ammonium. The integrated three-dimensional architecture combines the catalytic activity of Co3O4 with the high conductivity and open porosity of nickel foam, thus exposing abundant active sites, shortening electron-transfer pathways, and facilitating mass transport. Among the electrodes prepared at different calcination temperatures, Co3O4@NF calcined at 400 °C delivered the best performance. Under the optimal conditions of −1.4 V vs. Ag/AgCl, pH 7, and an initial NO3−-N concentration of 50 mg L−1, the electrode achieved 83.4% nitrate removal within 480 min together with 98.7% ammonium selectivity. Electrochemical measurements revealed a markedly enlarged electrochemically active surface area and reduced charge-transfer resistance after Co3O4 loading. Mechanistic analyses via TBA quenching experiments and DFT calculations revealed that both the direct pathway and the hydrogen-assisted indirect pathway were operative, with the indirect pathway being dominant due to its lower free energy barrier while maintaining negligible nitrite accumulation. The electrode also showed good cycling stability and retained high ammonium selectivity in real water matrices. These results demonstrate that binder-free Co3O4 nanoneedles supported on nickel foam constitute a promising cathode architecture for coupling nitrate removal with ammonia recovery.

Background Human papillomavirus (HPV) infection represents a major public health concern in both women and men, contributing to a substantial anogenital and oropharyngeal disease burden. Nevertheless, comprehensive data on the genotype-specific epidemiology of HPV in the male population in Wuhan, China, are lacking. This study was conducted to address this gap. Methods We conducted a retrospective cross-sectional study of 2,999 male subjects undergoing HPV testing at Renmin Hospital of Wuhan University (January 2017 to July 2025) to analyze the prevalence and genotypic distribution of HPV. Results Among 2,999 males, overall HPV prevalence was 38.6%, predominantly low-risk single-type infections. The top five genotypes were HPV6, HPV11, HPV16, HPV52, and HPV59. Although 20–39 year-olds comprised the largest proportion of cases, peak prevalence occurred in youth (≤ 20y) and elderly (≥ 60y) groups ( P  < 0.001 for age difference). HPV6 was the most common genotype in every age stratum. Conclusion In this group of males in Wuhan, HPV infection prevalence was relatively high and dominated by low-risk, single-type infections. Adolescents (≤ 20 years) and older men (≥ 60 years) are at highest risk. These findings support enhanced HPV education targeting youth and the elderly and broader promotion of HPV vaccination among males.

To identify potential diagnostic markers of lupus nephritis (LN) based on bioinformatics and machine learning and to explore the significance of immune cell infiltration in this pathology. Seven LN gene expression datasets were downloaded from the GEO database, and the larger sample size was used as the training group to obtain differential genes (DEGs) between LN and healthy controls, and to perform gene function, disease ontology (DO), and gene set enrichment analyses (GSEA). Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to identify candidate biomarkers. The diagnostic value of LN diagnostic gene biomarkers was further evaluated in the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from LN patients and to analyze their correlation with diagnostic markers. Thirty and twenty-one DEGs were screened in kidney tissue and peripheral blood, respectively. Both of which covered macrophages and interferons. The disease enrichment analysis of DEGs in kidney tissues showed that they were mainly involved in immune and renal diseases, and in peripheral blood it was mainly enriched in cardiovascular system, bone marrow, and oral cavity. The machine learning algorithm combined with external dataset validation revealed that C1QA(AUC = 0.741), C1QB(AUC = 0.758), MX1(AUC = 0.865), RORC(AUC = 0.911), CD177(AUC = 0.855), DEFA4(AUC= 0.843)and HERC5(AUC = 0.880) had high diagnostic value and could be used as diagnostic biomarkers of LN. Compared to controls, pathways such as cell adhesion molecule cam, and systemic lupus erythematosus were activated in kidney tissues; cell cycle, cytoplasmic DNA sensing pathways, NOD-like receptor signaling pathways, proteasome, and RIG-1-like receptors were activated in peripheral blood. Immune cell infiltration analysis showed that diagnostic markers in kidney tissue were associated with T cells CD8 and Dendritic cells resting, and in blood were associated with T cells CD4 memory resting, suggesting that CD4 T cells, CD8 T cells and dendritic cells are closely related to the development and progression of LN. C1QA, C1QB, MX1, RORC, CD177, DEFA4 and HERC5 could be used as new candidate molecular markers for LN. It may provide new insights into the diagnosis and molecular treatment of LN in the future.

Metal–organic frameworks (MOFs) have gained significant prominence as sensing materials owing to their unique properties. However, understanding the correlation between the morphology, properties, and sensing performance in these MOF-based sensors remains a challenge, limiting their applications and potential for improvement. In this study, Zr-MOF was chosen as an ideal model to explore the impact of the MOF morphology on the sensing performance, given its remarkable stability and structural variability. Three luminescent MOFs (namely rod-like Zr-LMOF, prismoid-like Zr-LMOF, and ellipsoid-like Zr-LMOF) were synthesized by adjusting the quantities of the benzoic acid and the reaction time. More importantly, the sensing performance of these Zr-LMOFs in response to aflatoxin B1 (AFB1) was thoroughly examined. Notably, the ellipsoid-like Zr-LMOF exhibited significantly higher sensitivity compared to other Zr-LMOFs, attributed to its large specific surface area and pore volume. Additionally, an in-depth investigation into the detection mechanism of AFB1 by Zr-LMOFs was conducted. Building upon these insights, a ratiometric fluorescence sensor was developed by coordinating Eu3+ with ellipsoid-like Zr-LMOF, achieving a remarkably lower detection limit of 2.82 nM for AFB1. This study contributes to an improved comprehension of the relationship between the MOF morphology and the sensing characteristics while presenting an effective approach for AFB1 detection.

Objective To generate an image‐driven biomarker (Rad_score) to predict tumor‐infiltrating regulatory T lymphocytes (Treg) in breast cancer (BC). Methods Overall, 928 BC patients were enrolled from the Cancer Genome Atlas (TCGA) for survival analysis; MRI (n = 71 and n = 30 in the training and validation sets, respectively) from the Cancer Imaging Archive (TCIA) were retrieved and subjected to repeat least absolute shrinkage and selection operator for feature reduction. The radiomic scores (rad_score) for Treg infiltration estimation were calculated via support vector machine (SVM) and logistic regression (LR) algorithms, and validated on the remaining patients. Results Landmark analysis indicated Treg infiltration was a risk factor for BC patients in the first 5 years and after 10 years of diagnosis (p = 0.007 and 0.018, respectively). Altogether, 108 radiomic features were extracted from MRI images, 4 of which remained for model construction. Areas under curves (AUCs) of the SVM model were 0.744 (95% CI 0.622–0.867) and 0.733 (95% CI 0.535–0.931) for training and validation sets, respectively, while for the LR model, AUCs were 0.771 (95% CI 0.657–0.885) and 0.724 (95% CI 0.522–0.926). The calibration curves indicated good agreement between prediction and true value (p > 0.05), and DCA shows the high clinical utility of the radiomic model. Rad_score was significantly correlated with immune inhibitory genes like CTLA4 and PDCD1. Conclusions High Treg infiltration is a risk factor for patients with BC. The Rad_score formulated on radiomic features is a novel tool to predict Treg abundance in the tumor microenvironment. The risk exert by Treg infiltration on breast cancer patients was confirmed via landmark analysis. Then, a radiomic score to estimate Treg abundance in the tumor microenvironment was formulated, which was found to be correlated to inhibitory immune genes.