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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Comparative analysis of key phagocytic genes in humans and mice using machine learning integrated with single-cell RNA sequencing
Microglial phagocytosis is essential for neurological recovery after intracerebral hemorrhage (ICH). Using single-cell RNA sequencing, we compared microglial responses in murine and human ICH and identified striking species-specific temporal patterns. Murine microglia exhibited a sustained enhancement of phagocytic activity, whereas human microglia showed only a transient increase followed by a decline and persistent inflammation. To identify genes associated with phagocytic differences, we evaluated five machine learning models and selected XGBoost as the best-performing model. This analysis identified Tlr2 in mice and CLEC7A in humans as genes associated with microglial phagocytic status. Inferred transcription factor activity analysis further revealed stronger phagocytosis- and inflammation-associated transcriptional activity in murine phagocytic microglial subclusters, whereas human microglia were predominantly characterized by inflammation-associated transcription factors. Consistent with these results, Tlr2 expression was markedly increased at day 14 in single-cell data, and immunostaining confirmed its colocalization with IBA1+ microglia and upregulation at days 3 and 7 after ICH. Together, our findings demonstrate that integrating single-cell RNA sequencing with machine learning facilitates the identification of phagocytosis-associated genes and reveals both conserved and divergent patterns of microglial phagocytosis, providing new insights into species-specific responses to ICH.
6 Apr 2026
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