Biomedical subjects

Yuqian Liu

Publications and source records attributed to Yuqian Liu.

3 recordsLinked to original sources

PubMed2026

Reassessment of the effect of oral l-arginine on blood pressure: A systematic review and meta-analysis based on ambulatory blood pressure monitoring.

OBJECTIVE: This meta-analysis aimed to evaluate the effect of oral l-arginine supplementation on ambulatory blood pressure (ABP). METHODS: A systematic search of PubMed, Cochrane Library, Embase, and Web of Science databases was conducted from their inception through March 1, 2026. Randomized controlled trials (RCTs) assessing the effects of oral l-arginine intervention were included. Outcome measures included 24-h systolic blood pressure (24h SBP), 24-h diastolic blood pressure (24h DBP), daytime systolic blood pressure (dSBP), daytime diastolic blood pressure (dDBP), nighttime systolic blood pressure (nSBP), and nighttime diastolic blood pressure (nDBP). Meta-analysis was performed using Stata 17.0. The weighted mean difference (WMD) was used as the effect size, and the results were pooled with 95% confidence intervals (CIs). RESULTS: A total of 5 RCTs comprising 202 participants were included. Meta-analysis results demonstrated that oral l-arginine significantly reduced 24h SBP (WMD = -4.23 mmHg, 95% CI [-5.87, -2.58]; P < 0.01) and 24h DBP (WMD = -3.04 mmHg, 95% CI [-4.48, -1.59]; P < 0.01). Significant reductions were also observed for dSBP (WMD = -4.16 mmHg, 95% CI [-5.90, -2.41]; P < 0.01) and dDBP (WMD = -4.25 mmHg, 95% CI [-5.85, -2.66]; P < 0.01). Furthermore, oral l-arginine significantly lowered nSBP (WMD = -5.70 mmHg, 95% CI [-7.81, -3.58]; P < 0.01) and nDBP (WMD = -4.18 mmHg, 95% CI [-6.27, -2.09]; P < 0.01). CONCLUSION: Oral l-arginine supplementation significantly reduces ABP. However, the number of included studies was limited, and further validation through additional relevant research is warranted.

Arginine↗

PubMed2025

MRDtarget: A heuristic Gaussian approach for optimizing targeted capture regions to enhance Minimal Residual Disease detection.

Molecular residual disease (MRD) detection, initially developed for hematologic malignancies, has become a critical biomarker for monitoring solid tumors. MRD detection primarily relies on circulating tumor DNA (ctDNA) analysis using next-generation sequencing, offering high sensitivity and broad genomic coverage. However, challenges remain in designing cost-effective panels that maximize mutation detection while maintaining biological relevance. Fixed panels often lack sufficient patient-specific mutation coverage, while WES-based personalized MRD assays, despite their high sensitivity, are costly and less accessible. We developed a tumor comprehensive genomic profiling (CGP)-informed personalized MRD assay to detect tumor-derived mutations, which allowed us to design patient-specific personalized panels and meanwhile, provide a cost-effective alternative to whole exome sequencing (WES). To address these limitations, we developed MRDtarget, a heuristic multivariate Gaussian model-based targeted capture region selection method. By expanding beyond traditional hotspot regions, MRDtarget optimizes variant tracking for MRD detection, significantly improving sensitivity. Using a Bayesian inference-based heuristic approach, MRDtarget integrates multi-feature informativeness rates to identify optimal genomic regions for capture. Experimental results demonstrate that MRDtarget enables the detection of more variants per patient. This study underscores the importance of rational panel design to improve MRD sensitivity and provides a novel approach to enhance precision diagnostics and treatment for solid tumor patients.

Humans↗

PubMed2025

MRDagent: iterative and adaptive parameter optimization for stable ctDNA-based MRD detection in heterogeneous samples.

MOTIVATION: Minimal residual disease (MRD) as critical biomarker for cancer prognosis and management plays a crucial role in improving patient outcomes. However, detecting MRD via next-generation sequencing-based circulating tumor DNA variant calling remains unstable due to the extremely low variant allele frequency and significant inter- and intra-sample heterogeneity. Although parameter optimization can theoretically enhance the detection performance of variants, achieving stable MRD detection remains challenging due to three key factors: (i) the necessity for individualized parameter tuning across numerous heterogeneous genomic intervals within each sample, (ii) the tightly interdependent parameter requirements across different stages of variant detection workflows, and (iii) the limitations of current automated parameter optimization methods. RESULTS: In this study, we propose MRDagent, a novel variant detection tool designed specifically for MRD detection. MRDagent incorporates an iterative and self-adaptive optimization framework capable of handling unknown objectives, varying constraints, and highly coupled parameters across stages. A key innovation of MRDagent is the integration of a convolutional neural network-based meta-model, trained on historical data to enable rapid parameter prediction. This significantly enhances computational efficiency and generalization performance. Extensive evaluations on simulated and real-world datasets demonstrate MRDagent's superior and stable performance, providing an efficient, reliable solution for MRD detection in clinical and high-throughput research applications. AVAILABILITY AND IMPLEMENTATION: MRDagent is freely available at https://github.com/aAT0047/MRDagent.git. The corresponding dataset and software archive are available at Zenodo: https://doi.org/10.5281/zenodo.15458496.

Circulating Tumor DNA↗