Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Chemico-biological interactions
Carbon quantum dots exacerbate Parkinson's disease-like pathology through microglia-mediated neuroinflammatory responses with toxicological implications
The global burden of Parkinson's disease (PD) is projected to double by 2050, with early-onset cases demonstrating accelerated progression and limited therapeutic options. Recently, carbon quantum dots (CQDs) emerge as promising nanoplatforms for PD theragnostic, but their paradoxical role in exacerbating neuroinflammation and neurodegeneration shows the urgent need for mechanistic safety evaluations to bridge their therapeutic potential and neurotoxic risks. This study conducted a neuro-safety assessment of CQDs based on MPP+-induced mice and cell PD models to stimulate the theragnostic scenarios of CQDs in PD patients in the real-world. The results showed that CQDs not only promoted the onset and progression of PD-like damages in mice and cells but also exacerbated PD-related neurological dysfunction and structural damages in PD models. The microglia-mediated neuroinflammatory reactions were identified to be pivotal mediators of CQDs exacerbating PD-like damages based on machine learning (ML) approaches. According to these key toxic targets, we established molecular docking technology and network pharmacology to propose interventional strategies, including functional modification of CQDs and anti-neuroinflammatory drugs to alleviate the exacerbation of PD-like damages induced by CQDs, thereby ensuring the safe application of CQDs in the diagnosis and treatment of PD.
25 May 2026
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Deep Learning Enables Pixel-Level Nanoparticle Distribution Mapping in Routine Histological Sections by Integrating Cancer Associated Fibroblasts Features
The efficient accumulation and uniform distribution of nanomedicine within tumors are critical for achieving therapeutic outcomes. However, conventional medical imaging technologies struggle to efficiently and accurately detect nanoparticles (NPs) distribution, especially in resolving their cellular-level spatial heterogeneity. Existing deep-learning-based predictive models focus on tumor cell density and vascular distribution but do not address the complex spatial relationship between cancer-associated fibroblasts (CAFs) and drug distribution. This study presents NanoNet, a deep-learning framework that leverages fibroblast activation protein (FAP) immunostaining to spatially characterize CAFs and predict NPs distribution at high resolution. NanoNet achieved high predictive accuracy (ICC = 0.963, R2 = 0.9849) by transforming tumor section images into pixel-level NPs distribution maps. The FAP channel contributed substantially to predictive accuracy, indicating its important role in guiding NPs behavior. This study provides a spatially resolved predictive framework that enables pixel-level predictions of NPs distribution from conventional histological sections, with potential applications for optimizing nanomedicine design and personalized nanomedicine.
15 Apr 2026
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