Biomedical subjects

Li Sun

Publications and source records attributed to Li Sun.

5 recordsLinked to original sources

PubMed2026

Robotic assistance in total hip arthroplasty: a systematic review and meta-analysis of leg length, cup orientation, and early outcomes.

This review examined whether robotic assistance alters postoperative leg-length discrepancy (LLD), acetabular cup orientation, or early hip-specific outcomes relative to conventional total hip arthroplasty (THA). We searched PubMed and Web of Science through May 2026 for comparative English-language reports. Study eligibility, data extraction, and methodological appraisal were undertaken independently by two reviewers. Mean differences (MDs) and 95% confidence intervals (CIs) were calculated in Review Manager 5.4. Model selection was based on the target estimand and anticipated clinical and methodological diversity; leave-one-out and alternative-model sensitivity analyses were undertaken for heterogeneous outcomes. The protocol is registered with PROSPERO (CRD420261454043). The review included seven studies and 968 participants. Compared with conventional THA, robot-assisted THA yielded a smaller postoperative LLD (MD = -2.02, 95% CI -3.46 to -0.58; P = 0.006) and a higher Harris Hip Score (MD = 2.96, 95% CI 1.12 to 4.80; P = 0.002). Mean cup anteversion was lower in the robotic group (MD = -1.52, 95% CI -2.29 to -0.76; P < 0.0001), whereas cup inclination did not differ (MD = -0.71, 95% CI -3.26 to 1.83; P = 0.58). The robotic group also had higher Forgotten Joint Score (MD = 14.68, 95% CI 5.02 to 24.33; P = 0.003) and Oxford Hip Score values (MD = 2.61, 95% CI 0.71 to 4.51; P = 0.007). Robotic assistance was linked to a modest improvement in leg-length restoration and to higher scores on several early functional measures. The limited number of studies, predominance of nonrandomized designs, and marked heterogeneity in some analyses temper the certainty of these findings.

Humans↗

PubMed2026

A Rapid Poly(ethylene glycol)-Assisted Magnetic Isolation Approach for High-Throughput Extracellular Vesicle Isolation and Subsequent Biomarker Analysis.

Extracellular vesicles (EVs) are crucial mediators of intercellular communication and have the potential to serve as biomarkers for disease diagnosis and therapeutic monitoring. However, most EV isolation methods often require large sample volumes and specialized instruments or involve trade-offs between purity, yield, cost, and scalability. We developed MagPEG, a workflow that combines poly(ethylene glycol) (PEG)-mediated EV aggregation with magnetic beads to provide a simple, reproducible alternative to ultracentrifugation, size-exclusion chromatography, and commercial precipitation kits. Our optimization experiments clarified the PEG concentration, ionic strength, and bead surface chemistry that collectively influence EV aggregation, capture efficiency, and contaminant coprecipitation, allowing us to define conditions that improve purity while maintaining high recovery. Compared with commonly used methods, MagPEG produced EVs with comparable size distribution, EV markers, and proteomic profiles while relying only on standard laboratory supplies. A key feature of the platform is that EVs and EV-associated DNA, RNA, and proteins can be sequentially extracted from the same bead-bound material, reducing sample loss and hands-on time and enabling multiomic analysis for limited clinical or small animal samples. MagPEG is compatible with downstream applications including proteomics, bead-based assays, and miRNA quantification. When applied to human serum, the method supported high-throughput EV proteomic profiling and enabled the identification of Alzheimer's disease-associated protein signatures, illustrating its utility for biomarker discovery. Overall, our results establish MagPEG as a powerful, rapid, scalable, and high-throughput solution for translational applications in biomarker discovery.

Polyethylene Glycols↗

PubMed2026

Neurodevelopmental toxicity of 2-(Methylthio)benzothiazole (MTBT) in zebrafish: Insights into PTGS2- associated dysregulation of the neuroactive ligand-receptor interaction pathway.

2-(Methylthio)benzothiazole (MTBT), an important derivative of benzothiazoles, has extensive applications in industrial processes, pharmaceuticals, and environmental monitoring. It can enter aquatic environments through surface runoff and has been detected at relatively high concentrations in various environmental systems. However, studies investigating the aquatic toxicity of MTBT remain limited. In this study, zebrafish embryos were exposed to MTBT at concentrations of 0, 10, 100, and 1000 μg/L for 144 h to evaluate its developmental and neurotoxic effects. MTBT exposure significantly reduced the survival rate, hatching rate, spontaneous movement, and body length of zebrafish larvae. MTBT also impaired locomotor behavior, reduced fluorescence of Tg(huc:eGFP) larvae in the central nervous system and inhibited motor neuron axonal development. Protein-protein interaction network and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses indicated that MTBT-induced neurotoxicity may be associated with disruption of the neuroactive ligand-receptor interaction pathway. Further validation experiments revealed that MTBT induced oxidative stress, inflammation, and apoptosis, suggesting that these adverse effects may underlie its neurodevelopmental toxicity. Collectively, these findings provide biological evidence that MTBT induces neurodevelopmental toxicity in zebrafish larvae and suggest that dysregulation of the PTGS2-related neuroactive ligand-receptor interaction pathway may be involved in this process.

2-(Methylthio)benzothiazole (MTBT)↗

PubMed2025

Identification and analysis of metabolic reprogramming-related genes in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is notorious for its rapid progression, tendency to metastasize, high recurrence rates, dismal outcomes, and limited treatment options, underscoring the urgent need to uncover new biomarkers and molecular pathways to enhance diagnosis, prognosis, and therapeutic strategies. Metabolic reprogramming continues to play a role throughout the life cycle of cancer, evolving and adapting. In this study, we aimed to identify specific genes associated with metabolic reprogramming in TNBC, which can potentially become unique biomarkers of this cancer. TNBC datasets retrieved from the Gene Expression Omnibus were employed to pinpoint genes exhibiting altered expression linked to tumor metabolic reprogramming. Key genes were accurately screened through machine learning algorithms, and then externally verified using the TBNC dataset based on the Cancer Genome Atlas database. Finally, immunohistochemical methods were used to clinically confirm the differential expression and trends of these key genes. Our analysis accurately identified four genes-CLEC7A, IRS1, RSPO3, and ALB-that are closely correlated with the metabolic reprogramming characteristics of cancer, and could be regarded as innovative biomarkers for TNBC. This opens a new avenue for further investigation into the mechanisms of metabolic reprogramming in TNBC and new treatment strategies.

Humans↗

PubMed2025

Characterization of gut microbiota and metabolites in renal transplant recipients during COVID-19 and prediction of one-year allograft function.

BACKGROUND: The gut-lung-kidney axis is pivotal in immune-related kidney diseases, with gut dysbiosis potentially exacerbating the severity of Coronavirus disease 2019 (COVID-19) in recipients of kidney transplant. This study aimed to characterize the gut microbiome and metabolome in renal transplant recipients with COVID-19 pneumonia over a one-year follow-up period. METHODS: A total of 30 renal transplant recipients were enrolled, comprising 17 with COVID-19 pneumonia, six with mild COVID-19, and seven without COVID-19. Fecal samples were collected at the onset of infection for gut microbiome and metabolome analysis. Generalized Estimating Equations (GEE) model and Latent Class Growth Mixed Model (LCGMM) were employed to dissect the relationships among clinical characteristics, laboratory tests, and gut microbiota and metabolites. RESULTS: Four microbial phyla (Deferribacteres, TM7, Fusobacteria, and Gemmatimonadetes) and 13 genera were significantly enriched across three recipients groups, correlating with baseline inflammatory response and allograft function. Additionally, 52 differentially expressed metabolites were identified, with seven significantly correlating with eight altered microbiota genera. LCGMM revealed two distinct classes of recipients, with those suffering from COVID-19 pneumonia exhibiting significantly elevated serum creatinine (Scr) trajectories over the one-year period. GEE further identified 12 genera and 181 metabolites closely associated with these trajectories; a multivariable model incorporating gut metabolites of 1-Caffeoylquinic Acid and PMK was found to effectively predict one-year allograft function. CONCLUSIONS: Our study indicates a possible interaction between the composition of the gut microbiota and metabolites community and COVID-19 in renal transplant recipients, particularly in relation to disease severity and the prediction of one-year allograft function.

Humans↗