Oral administration of microbiome-friendly graphene quantum dots as therapy for colitis
Source abstract
Abstract
Abstract Graphene-based nanomaterials exhibit relatively high biocompatibility with low toxicity, of which a growing body of evidence has proved its feasibility, particularly as alternative drugs for various rare diseases. In response to the inevitable tide, we previously demonstrated that intraperitoneal (i.p.) injected graphene quantum dots (GQDs) retrieve the experimental colitis. Nevertheless, it is still requested to verify the effect of oral administration for the actual application of GQDs as an alternative remedy. GQDs (1 mg ml −1 , 300 μ l/injection) were orally administered to dextran sulfate sodium-induced mice every 3 d, and the therapeutic effects were monitored by changes of body weights, disease activity index and colon length. To address GQDs’ maintenance of therapeutic efficacy even after passing the gastrointestinal tract, its physicochemical properties were investigated after exposure to a low pH environment. Furthermore, we evaluated the impact of GQDs on intestinal microbiota by determining bacterial viability. As a result, repetitive oral administration of GQDs resolved the symptoms of colitis, such as body weight loss and secretion of inflammatory cytokines, and efficiently suppressed intestinal inflammation, similar to the previous i.p. injection. GQDs were confirmed to retain its properties after exposure to the acidic environment. No significant toxicity was found in vivo and on the microbiota, which is critical in terms of a direct correlation between GQDs and recipients’ intestinal environment. Taken together, we demonstrated that oral administration of GQDs could ameliorate experimental colitis without any harmful effect, which potentiates GQDs as an alternative microbiome-friendly treatment for colitis.
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Research context
OpenAlex topics: Carbon and Quantum Dots Applications · Graphene and Nanomaterials Applications · Advanced Drug Delivery Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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