Biomedical subjects

Yingjie Wang

Publications and source records attributed to Yingjie Wang.

4 recordsLinked to original sources

PubMed2026

Integrated Optimization, Genomic Characterization, and Functional Evaluation of Biogenic Selenium Nanoparticles from Bacillus licheniformis BLN313: Antibacterial and Anticancer Potential.

Microbial synthesis of selenium nanoparticles (SeNPs) offers a sustainable alternative to chemical routes, but the genetic basis of selenium handling in Bacillus remains poorly defined, which limits rational strain selection. Here, SeNP production, physicochemical characterization, and closed-genome sequencing are combined for Bacillus licheniformis BLN313. Selenite reduction peaked at 500 µg/mL Na2SeO3 (88.8% conversion; 444 ± 27 µg/mL Se0); at higher concentrations, conversion efficiency and viability diverged, indicating that tolerance and reductive capacity are distinct traits. Purified SeNPs were spherical and partially crystalline trigonal Se0 (TEM 190 ± 52 nm; DLS 166 nm, PDI 0.03; zeta potential -20.8 mV), carrying a proteinaceous capping layer confirmed by XPS, EDS, and FTIR and shown by LC-MS to be enriched in cell wall-derived metabolites. The particles were bactericidal against Micrococcus luteus (MIC 62.5 µg/mL) and Klebsiella pneumoniae (MIC 250 µg/mL) and reduced MCF-7 viability (IC50 2.7 µg/mL) while sparing MCF-10A cells. The 4.11 Mb genome (46.3% GC; ANI 99.7%, dDDH 97.8%) encodes SulP and Pit transporters, multiple trxB copies, and sulfur-metabolism and oxidative-stress genes, defining a candidate gene set for selenium uptake, reduction and detoxification. BLN313 thus provides a genetically defined platform for SeNP production in biomedical and environmental applications.

Selenium↗

PubMed2026

The hidden threat from food-derived carbon dots: Formation, biodistribution, and potential health risks.

Food-derived carbon dots (CDs) are a new class of carbon-based nanoparticles generated during the thermal processing of food matrices. These nanomaterials have been extensively studied for their unique fluorescence, good biocompatibility, and tunable surface chemistry in food detection, intelligent packaging, and biomedical applications. However, their nanoscale size and high surface activity have raised safety concerns regarding biological interactions, in vivo biodistribution, and potential long-term health hazards. Although CDs have traditionally been regarded as low-toxicity materials due to their favorable biocompatibility, the potential hidden risks of CDs have not received sufficient attention. CDs exhibit dose-dependent toxicity, not only accumulating in various tissues and organs but also potentially inducing oxidative stress and interfering with cellular metabolic functions. Therefore, this review summarizes the advances in sources, synthetic strategies, and core properties of CDs, with a special focus on in vivo biological interactions, fates, and potential safety challenges. In addition, it is proposed that the standardized detection and risk assessment system should be established to further explore the long-term health effects of CDs under real dietary exposure, thereby ensuring their safety and sustainable application.

Carbon Quantum Dots↗

PubMed2025

Genome-Wide Analysis of the AT-Hook Gene Family in Malus sieversii and Functional Characterization of MsAHL13.

AT-hook motif nuclear-localized (AHL) proteins are pivotal in plant growth, development, and stress responses. Nevertheless, there is limited research on AHL proteins in Malus sieversii. Our study identified 25 AHL genes from the M. sieversii genome, named MsAHL1-MsAHL25. The encoded protein sequences had lengths ranging from 195 to 554 amino acids, molecular weights from 19.17 to 58.53 kDa, and isoelectric points from 4.67 to 10.09. Chromosomal mapping revealed that these 25 genes were unevenly distributed across 10 chromosomes. Collinearity analysis of AHL genes in M. sieversii implied that gene loss might have occurred during its evolution. The phylogenetic tree classified the AHL proteins of M. sieversii into two subfamilies, showing a close relationship with multiple proteins of M. domestica. Promoter analysis indicated that the AHL genes in M. sieversii harbored numerous stress- and hormone-responsive elements, suggesting their potential role in various stress responses. qRT-PCR analysis of six representative MsAHLs under biotic and abiotic stresses demonstrated that the expression of MsAHL13, MsAHL15, and MsAHL17 was significantly upregulated under salt, drought, and cold stresses, while MsAHL01 expression was inhibited under low-temperature stress. All six MsAHLs were induced by the pathogen Valsa mali. Subcellular localization analysis of the specifically expressed protein MsAHL13 showed its nuclear location. Furthermore, luciferase and yeast two-hybrid assays confirmed the in vitro physical interaction between the MsAHL13 and MsMYB1 proteins. This research offers an important theoretical basis for further exploration of the functional mechanisms of this gene family in responding to environmental stresses.

Malus sieversii↗

PubMed2025

Integration of single cell multiomics data by deep transfer hypergraph neural network.

Multi-omics characterization of individual cells offers remarkable potential for analyzing the dynamics and relationships of gene regulatory states across millions of cells. How to integrate multimodal data is an open problem, existing integration methods struggle with accuracy and modality-specific biological variation retention. In this paper, we present scHyper (scalable, interpretable machine learning for single cell integration), a low-code and data-efficient deep transfer model designed for integrating paired and unpaired single-cell multimodal data. We benchmark scHyper against datasets from different multimodal data. ScHyper learns a low-dimensional representation and aligns the covariance matrices of the measured modalities, achieving high accuracy even with large scale atlas-level datasets with low memory and computational time across different cell lines, shedding light on regulatory relationships between different types of omics. Altogether, we show that scHyper is a versatile and robust tool for cell-type label transfer and integration from multimodal single-cell datasets.

Single-Cell Analysis↗