Publications by authors named "M X Lu"

The bisphenols (BPs) contaminants with distinctive endocrine-disrupting properties have garnered significant attention. A new analytical methodology was proposed for the sensitive detection of hazardous BPs in efficient and food safety monitoring. The approach utilizes an ionic liquid-modified covalent organic framework (SCOF-V/IL-5F) as a solid-phase extraction adsorbent to enrich harmful BPs.

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The increasing presence of nanoplastics (NPs) and synthetic antioxidants like 3-tert -Butyl-4-hydroxyanisole (3-BHA) in the environment has attracted widespread attention about their combined toxicological effects on human health, particularly on renal function. This study explored to the combined impacts of NPs and 3-BHA at environmentally relevant concentrations on sub-chronic kidney injury in mice. Firstly, our results confirmed that the accumulation of 80 nm NPs in renal tissues, leading to structural abnormalities such as reduced mitochondrial cristae and increased empty bubbles in mice by transmission electron microscope (TEM) analysis.

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RNA helicase DDX3X is generally implicated in inflammasome activation and anti-viral responses. We characterize the common features of scattered DDX3X mutations in lymphoid cancers using molecular dynamics simulation and crystallization, thereby demonstrating their crucial role in Epstein-Barr virus (EBV) lytic gene-driven oncogenic processes. The DDX3X mutation is significantly related to impaired stimulator of interferon genes (STING)/ interferon regulatory factor 7 (IRF-7)/interferon (IFN)-α/β-mediated innate immunity, overexpression of EBV lytic gene BNLF2b, and increased formation of R-loops.

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MXene-based peroxidase (POD)-like nanozymes demonstrate significant potential in biomedical applications due to their 2D structure, tunable catalytic activity, and interfacial effects. This review summarizes recent advances in MXene-POD nanozyme design, focusing on interfacial effects modulation via external stimuli (e.g.

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Vision foundation models have demonstrated vast potential in achieving generalist medical segmentation capability, providing a versatile, task-agnostic solution through a single model. However, current generalist models involve simple pre-training on various medical data containing irrelevant information, often resulting in the negative transfer phenomenon and degenerated performance. Furthermore, the practical applicability of foundation models across diverse open-world scenarios, especially in out-of-distribution (OOD) settings, has not been extensively evaluated.

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