High expression of SIGLEC7 may promote M2-type macrophage polarization leading to adverse prognosis in glioma patients.

Introduction: Gliomas are the most common primary intracranial tumors, known for their high invasiveness and destructiveness. Sialic acid-binding immunoglobulin-like lectin 7 (SIGLEC7) is present in various immune cells, especially macrophages, and significantly affects immune homeostasis and cancer cell response. However, research on the role and prognostic impact of SIGLEC7 in glioma patients is currently limited. Methods: We utilized transcriptomic data from 702 glioma patients in The Cancer Genome Atlas (TCGA) and 693 glioma patients in the Chinese Glioma Genome Atlas (CGGA), along with clinical samples we collected, to comprehensively investigate the impact of SIGLEC7 on glioma expression patterns, biological functions, and prognostic value. We focused on its role in glioma-related immune responses and immune cell infiltration and analyzed its expression at the single-cell level. Finally, we validated the role of SIGLEC7 in gliomas through tissue and cell experiments. Results: SIGLEC7 expression was significantly increased in glioma patients with malignant characteristics. Survival analysis indicated that glioma patients with high SIGLEC7 expression had significantly lower survival rates. Gene function analysis revealed that SIGLEC7 is primarily involved in immune and inflammatory responses and is strongly negatively correlated with tumor-associated immune regulation. Additionally, the expression of most immune checkpoints was positively correlated with SIGLEC7, and immune cell infiltration analysis clearly demonstrated a significant positive correlation between SIGLEC7 expression and M2 macrophage infiltration levels. Single-cell analysis, along with tissue and cell experiments, confirmed that SIGLEC7 enhances macrophage polarization towards the M2 phenotype, thereby promoting glioma invasiveness through the immunosuppressive effects of M2 macrophages. Cox regression analysis and the establishment of survival prediction models indicated that high SIGLEC7 expression is an unfavorable prognostic factor for glioma patients. Discussion: High SIGLEC7 expression predicts poor prognosis in glioma patients and is closely associated with M2 macrophages in the tumor environment. In the future, SIGLEC7 may become a promising target for glioma immunotherapy.

Identification of crosstalk genes and immune characteristics between Alzheimer's disease and atherosclerosis.

Background: Advancements in modern medicine have extended human lifespan, but they have also led to an increase in age-related diseases such as Alzheimer's disease (AD) and atherosclerosis (AS). Growing research evidence indicates a close connection between these two conditions. Methods: We downloaded four gene expression datasets related to AD and AS from the Gene Expression Omnibus (GEO) database (GSE33000, GSE100927, GSE44770, and GSE43292) and performed differential gene expression (DEGs) analysis using the R package "limma". Through Weighted gene correlation network analysis (WGCNA), we selected the gene modules most relevant to the diseases and intersected them with the DEGs to identify crosstalk genes (CGs) between AD and AS. Subsequently, we conducted functional enrichment analysis of the CGs using DAVID. To screen for potential diagnostic genes, we applied the least absolute shrinkage and selection operator (LASSO) regression and constructed a logistic regression model for disease prediction. We established a protein-protein interaction (PPI) network using STRING (https://cn.string-db.org/) and Cytoscape and analyzed immune cell infiltration using the CIBERSORT algorithm. Additionally, NetworkAnalyst (http://www.networkanalyst.ca) was utilized for gene regulation and interaction analysis, and consensus clustering was employed to determine disease subtypes. All statistical analyses and visualizations were performed using various R packages, with a significance level set at p<0.05. Results: Through intersection analysis of disease-associated gene modules identified by DEGs and WGCNA, we identified a total of 31 CGs co-existing between AD and AS, with their biological functions primarily associated with immune pathways. LASSO analysis helped us identify three genes (C1QA, MT1M, and RAMP1) as optimal diagnostic CGs for AD and AS. Based on this, we constructed predictive models for both diseases, whose accuracy was validated by external databases. By establishing a PPI network and employing four topological algorithms, we identified four hub genes (C1QB, CSF1R, TYROBP, and FCER1G) within the CGs, closely related to immune cell infiltration. NetworkAnalyst further revealed the regulatory networks of these hub genes. Finally, defining C1 and C2 subtypes for AD and AS respectively based on the expression profiles of CGs, we found the C2 subtype exhibited immune overactivation. Conclusion: This study utilized gene expression matrices and various algorithms to explore the potential links between AD and AS. The identification of CGs revealed interactions between these two diseases, with immune and inflammatory imbalances playing crucial roles in their onset and progression. We hope these findings will provide valuable insights for future research on AD and AS.

Zero-liquid discharge technologies for desulfurization wastewater: A review.

Zero-Liquid Discharge (ZLD) has received a lot of attention due to water scarcity and pollution. This article reviews the treatment of desulfurization wastewater in pretreatment, concentration and solidification with the trend of zero-liquid discharge in China. We summarize the advantages, disadvantages, performance, benefits and other characteristics of different ZLD technologies. Membrane-based technology is an effective means of recycling wastewater in ZLD systems. Therefore, we focus on the application of bipolar membrane electrodialysis (BMED) in desulfurization wastewater and high-salt wastewater treatment, discussing its limitations and solutions. In addition, several Chinese ZLD cases and economic analysis are introduced. It is believed that ZLD will become a new trend in desulfurization wastewater treatment in the future. Therefore, exploring new materials and technologies with low cost and high efficiency is the focus of future work.

[Assessment of Ecosystem Health of an Urban River Based on the Microbe Index of Biotic Integrity (M-IBI)].

The index of biotic integrity (IBI) has been widely used in river ecosystem health assessment. However, few studies have reported the application of microbial communities in ecosystem health assessment so far, especially for urban rivers. In this study, the Illumina high-throughput sequencing technique was applied to analyze the microbial community diversity and composition of five urban rivers selected in Zhejiang Province. Canonical correlation analyses (CCA) and Spearman correlation analysis were used to evaluate the relationship between each taxonomic group and the water quality properties to select the most sensitive taxonomic groups as candidate indexes. The functional metrics, including the relative abundance of pathogenic bacteria, pollutant-degrading bacteria, and nutrient cycling bacteria were also selected as candidate indexes. Based on the distribution range, discriminatory power, and Pearson's correlation analysis for candidate indexes, five metrics, including the Shannon-index, the number of microbial phyla, the relative abundance of Verrucomicrobia, Chlorobi, and Mycobacterium were selected to establish a biotic integrity index of microbes (M-IBI) evaluation system. A ratio score system was used to get metrics into a uniform score for all sampling points, and the results showed that among the urban river samples studied, most of them (40.9%) were at "Great" level, 45.5% were at "Good" level, 9.1% were at "Moderate" level, and 4.5% were at "Bad" level. The index of M-IBI effectively discriminated the least, medium, and highly impaired sampling points and provided a good match with the water quality (R=0.753, P<0.01), indicating that the M-IBI has potential as an index to evaluate the health of urban river ecosystems.

Fungal community dynamics and driving factors during agricultural waste composting.

This study was conducted to identify the driving factors behind fungal community dynamics during agricultural waste composting. Fungal community abundance and structure were determined by quantitative PCR and denaturing gradient gel electrophoresis analysis combined with DNA sequencing. The effects of physico-chemical parameters on fungal community abundance and structure were evaluated by least significant difference tests and redundancy analysis. The results showed that Cladosporium bruhnei, Hanseniaspora uvarum, Scytalidium thermophilum, Tilletiopsis penniseti, and Coprinopsis altramentaria were prominent during the composting process. The greatest variation in the distribution of fungal community structure was statistically explained by pile temperature and total organic carbon (TOC) (P < 0.05). A significant amount of the variation (74.6 %) was explained by these two parameters alone. Fungal community abundance was found to be significantly related to pH, while pH was significantly influenced by pile temperature and nitrate levels (P < 0.05), and these parameters were found to be the most likely to influence or be influenced by the fungal community during composting.

Adsorptive removal of trace oxytetracycline from water by acid-modified zeolite: influencing factors.

Because of the wide use of antibiotics in the livestock industry, trace tetracycline antibiotics are frequently detected in swine wastewater and water bodies near pig farms. Based on natural zeolite, modified zeolite was synthesized by treatment with nitric acid. As one kind of typical tetracyclines, oxytetracycline (OTC) was chosen as the target adsorbate. Removal of trace OTC by modified zeolite and the effects of several main water matrices on OTC adsorption were studied in detail. OTC removal efficiency by acid-modified zeolite was about 90%, compared to less than 20% by natural zeolite. In general, in acidic conditions the removal efficiency of OTC by modified zeolite was about 90%, which was much higher than 20-35% in alkaline conditions. An increase in ionic strength from 0.01 to 1.0 M led to a decrease in adsorption efficiency from 90 to 27%. The presence of 10.0 mg L(-1) dissolved humic acid accelerated sorption of OTC on modified zeolite, while 100.0 mg L(-1) humic acid resulted in the opposite effect. An increase in temperature contributed to enhancing the adsorption efficiency.