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Xuan Fan

Publications and source records attributed to Xuan Fan.

2 recordsLinked to original sources
PubMed2026

Insights into iron-enhanced denitrification coupled with antibiotic resistant genes control in biochar-based biofiltration systems.

In biofiltration (BF) systems, biochar can enhance pollutant removal by promoting biofilm formation. Its abundant pore structure can also sequester antibiotics away from microbial cells, thereby reducing its bioavailability and accumulation of antibiotic resistance genes (ARGs). However, dense biofilms favor horizontal ARG transfer, especially among denitrifying bacteria, which are prone to stress under low influent C/N conditions. In this study, a strategy combining iron minerals was proposed to alleviate the ARG accumulation in BF systems. Compared with magnetite, goethite and siderite released Fe2+ through microbial dissimilatory iron reduction and chemical dissolution respectively, thereby driving iron‑autotrophic denitrification and enhancing the activity of electron‑transfer mediators (cytochrome c and Fe-S proteins). As a result, the level of nitrosative stress was reduced with significant downregulation of related genes (hmp, hcp, norR, and etc.), which was a driving force for conjugative transfer of ARGs. Specifically, the excessive accumulation of tryptophan and shortage of methionine were thus alleviated, which contributed to the regulation of global repressor gene expression and the mitigation of ARG conjugative transfer. With the combination of goethite or siderite in BF systems, the abundance of resistance genome in biofilm exhibited a reduction of 52.68 ± 3.80% and 41.26 ± 4.20%, respectively, which could effectively reduce the environment-ecological risk of antibiotic and ARGs.

Denitrification↗
PubMed2026

SCMO: a deep learning model integrating the single-cell resolution TME ecosystem and multi-omics for survival prediction in CRC patients.

BACKGROUND: Colorectal cancer (CRC) remains a leading cause of global cancer mortality, highlighting the need for precise survival prediction to guide clinical decisions. Although tissue-level multi-omics is widely utilized for survival prediction, its limited resolution cannot capture tumor heterogeneity. Single-cell RNA sequencing (scRNA-seq) enables dissection of the tumor microenvironment (TME) at cellular resolution, supporting personalized prognostic assessment. METHODS: We collected 213 CRC scRNA-seq samples and established a CRC-specific TME atlas comprising 339,060 cells. Using this atlas as a reference, we deconvolved bulk RNA-seq data from TCGA-CRC cohort with the EcoTyper algorithm to reconstruct TME features. Clinical, genomic, and transcriptomic data were obtained from the Xena platform; microbial data were sourced from the BIC database. We integrated TME and multi-omics features through a self-normalizing neural network to construct a deep learning model (single-cell resolution TME ecosystem with multi-omics data [SCMO]) for survival prediction. To enhance interpretability, we utilized the Integrated Gradients algorithm and spatial transcriptomic data to analyze multi-omics and TME features. We performed anticancer drug screening with tumor necrosis factor receptor-associated protein 1 (TRAP1), a critical feature according to the Integrated Gradients algorithm, as a potential target. RESULTS: We identified 13 survival-related TME features from the CRC-specific atlas: 12 cell states and one multi-cellular ecosystem. SCMO, which combined TME and multi-omics features, improved survival prediction and outperformed existing methods, achieving a concordance index of 0.762. The SCMO demonstrated robust performance for long-term predictions, achieving areas under the curve (AUCs) of 0.752, 0.772, and 0.869 for 1-, 3-, and 5-year predictions in the training set, with corresponding test set AUCs of 0.639, 0.756, and 0.772. TME features from the SCMO model revealed that ecosystem density increased with CRC malignancy. Multi-omics features included TRAP1 as a potential drug target. Drug screening identified saikosaponin A as a novel TRAP1 inhibitor, and its anticancer activity was validated in vitro. We developed SCMO-Lite, a simplified model incorporating 12 high-attribution-weight multi-omics features, which demonstrated robust risk stratification. CONCLUSIONS: SCMO combines analytical precision with biological interpretability, offering novel insights for oncology survival prediction.

Humans↗