Biomedical subjects

Juan Liu

Publications and source records attributed to Juan Liu.

3 recordsLinked to original sources

PubMed2026

A systematic review and network meta-analysis of single nucleotide polymorphisms associated with oral submucous fibrosis risk.

BACKGROUND: Oral submucous fibrosis (OSF) is a chronic and insidious oral disease characterized by hyalinization of the subepithelial connective tissue and progressive fibrosis of the oral submucosa. It is a precancerous condition of oral squamous cell carcinoma. Studies have demonstrated that single nucleotide polymorphisms (SNPs) are closely associated with susceptibility to OSF. This study aims to comprehensively evaluate the association between SNPs and OSF risk and to rank the strength of the association between different genetic models and OSF susceptibility. METHODS: Literature related to OSF was comprehensively searched from PubMed, Web of Science, Embase, Cochrane Library, CNKI, and Wangfang databases up to July 2025. Full-text case-control studies with patients diagnosed with OSF were included. Quality assessment was performed to evaluate the risk of bias. RevMan 5.4, GeMTC 0.14.3, and STATA 17.0 were used for the pairwise and Bayesian network meta-analysis. RESULTS: A total of 24 studies with 2545 cases and 3772 controls, covering 13 SNPs in 11 genes, were included in our meta-analysis. We found that CYP1A1 rs4646903:T>C, CYP1A1 rs1048943:A>G, GSTT1 null genotype, GSTM1 null genotype, and XRCC3 rs861539:C>T were associated with an increased risk of OSF, while MMP2 rs243865:C>T and MMP3 rs3025058: 5A>6A were associated with a decreased risk of OSF. Further Bayesian network meta-analysis indicated the top 5 genetic models with the highest association with OSF risk in network group 1 were the dominant model, homozygous model, allelic model, and recessive model of CYP1A1 rs1048943:A>G (ranked 1-4), and the heterozygous/dominant model of CYP1A1 rs4646903:T>C (both ranked 5). While the allelic models of XRCC3 rs861539:C>T and MMP3 rs3025058: 5A>6A ranked first for predicting OSF in group 2 and group 3, respectively. CONCLUSION: Some specific SNPs are significantly related to the risk of OSF. Among them, the dominant model of CYP1A1 rs1048943:A>G may be the most strongly associated genetic model with OSF risk. Future large-sample, well-designed studies with detailed genotype data are needed to validate the roles of these SNPs in OSF risk.

Humans↗

PubMed2026

Chemotactic sensing of extracellular antibiotic resistance genes enables their efficient removal by Stutzerimonas stutzeri.

The dissemination of antibiotic resistance genes (ARGs) in wastewater environments poses a severe threat to public health. Extracellular ARGs (eARGs) persist as free DNA fragments that are refractory to efficient removal by conventional physicochemical treatment technologies. Here, we isolated Stutzerimonas stutzeri CHY07 from municipal sewage and demonstrated that extracellular DNA fragments, including eARGs, can serve as chemoattractants for environmental bacteria. Through genomic mining, molecular docking, surface plasmon resonance (SPR), isothermal titration calorimetry (ITC) and protein-ligand interaction profiling, we identified the chemoreceptor Mcp16 as the primary sensor of extracellular DNA and revealed that it achieves sequence-independent recognition of the DNA phosphate backbone. We further established the endogenous pentapeptide VRSVR as a methylation substrate for CheR and constructed the engineered strain CHY07-2 (mcp16::VRSVR) using an SSB/CRISPR-Cas9 ribonucleoprotein (RNP) system. This strain exhibited significantly enhanced chemotactic responsiveness, achieving 72-h removal efficiencies of 96.56% and 91.60% for low- and high-molecular-weight eARGs in non-sterile WWTP secondary effluent; conversely, mcp16 deletion markedly attenuated both chemotaxis and removal, whereas in situ complementation restored them. These findings reveal a "chemotaxis-contact-removal" cascade - with a proposed self-reinforcing loop - in eARG-removing bacteria, providing both a theoretical framework and a technical paradigm for enhancing pollutant removal through targeted amplification of microbial chemotaxis.

Chemotaxis↗

PubMed2026

SIVA: diagonal integration of spatial multi-omics data via spatially informed variational autoencoders and anchor guidance.

MOTIVATION: Understanding cellular states and regulatory programs requires integrative analysis of multiple omics layers. Although recent spatial sequencing technologies allow molecular profiling of cells within their tissue context, paired spatial multi-omics assays are still limited by technical complexity and cost. This creates a pressing need for diagonal integration methods that enable joint analysis of unpaired spatial omics datasets. RESULTS: We propose SIVA, a deep generative framework based on Spatially-Informed Variational Autoencoders with Anchor Guidance, for diagonal integration of spatial multi-modal data. SIVA employs modality-specific variational autoencoders (VAEs) with a hybrid latent embedding that integrates Gaussian process and standard Gaussian priors, enabling joint modeling of spatially structured variation and dominant underlying data distributions across modalities. To facilitate cross-modal alignment in the absence of one-to-one cell correspondence, SIVA adopts a dual integration strategy combining global distribution alignment via Maximum Mean Discrepancy and local correspondence guidance using mutual nearest neighbor anchors. Extensive experiments across multiple cross-slice integration scenarios demonstrate that SIVA achieves robust and accurate integration of unpaired spatial omics datasets, consistently outperforming existing methods. AVAILABILITY AND IMPLEMENTATION: The source codes are available at https://github.com/PelenJiang/SIVA.

Autoencoder↗