Discovery of novel protective agents for infection-related delirium through bispectral electroencephalography

通过双频谱脑电图发现感染相关谵妄的新型保护剂

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作者:Tsuyoshi Nishiguchi, Kyosuke Yamanishi, Shivani Patel, Johnny R Malicoat, Nathan James Phuong, Tomoteru Seki, Takaya Ishii, Bun Aoyama, Akiyoshi Shimura, Nipun Gorantla, Takehiko Yamanashi, Masaaki Iwata, Andrew A Pieper, Gen Shinozaki

Abstract

Delirium is a multifactorial medical condition of waxing and waning impairment across various domains of mental functioning over time. Importantly, delirium is also one of the greatest risk factors for prolonged hospitalization, morbidity, and mortality. Studying this important condition is challenging due to the difficulty in both objective diagnosis in patients and validation of laboratory models. As a result, there is a lack of protective treatments for delirium. Our recent studies report the efficacy of bispectral electroencephalography (BSEEG) in diagnosing delirium in patients and predicting patient outcomes, advancing the concept that this simple measure could represent an additional vital sign for patients. Here, we applied BSEEG to characterize and validate a novel lipopolysaccharide (LPS) mouse model of infection-related delirium. We then applied this model to evaluate the protective efficacy of three putative therapeutic agents: the conventional antipsychotic medication haloperidol, the neuroprotective compound P7C3-A20, and the antibiotic minocycline. Aged mice were more susceptible than young mice to LPS-induced aberration in BSEEG, reminiscent of the greater vulnerability of older adults to delirium. In both young and old mice, P7C3-A20 and minocycline administration prevented LPS-induced BSEEG abnormality. By contrast, haloperidol did not. P7C3-A20 and minocycline have been shown to limit different aspects of LPS toxicity, and our data offers proof of principle that these agents might help protect patients from developing infection-related delirium. Thus, utilization of BSEEG in a mouse model for infection-related delirium can identify putative therapeutic agents for applications in patient clinical trials.

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