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一种用于研究犬类睡眠-觉醒周期的功能线性建模方法。

A functional linear modeling approach to sleep-wake cycles in dogs.

机构信息

Department of Clinical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA.

Department of Anthropology, University of Toronto, Toronto, ON, Canada.

出版信息

Sci Rep. 2020 Dec 17;10(1):22233. doi: 10.1038/s41598-020-79274-2.

Abstract

The study of companion (pet) dogs is an area of great translational potential, as they share a risk for many conditions that afflict humans. Among these are conditions that affect sleep, including chronic pain and cognitive dysfunction. Significant advancements have occurred in the ability to study sleep in dogs, including development of non-invasive polysomnography; however, basic understanding of dog sleep patterns remains poorly characterized. The purpose of this study was to establish baseline sleep-wake cycle and activity patterns using actigraphy and functional linear modeling (FLM), for healthy, adult companion dogs. Forty-two dogs were enrolled and wore activity monitors for 14 days. FLM demonstrated a bimodal pattern of activity with significant effects of sex, body mass, and age; the effect of age was particularly evident during the times of peak activity. This study demonstrated that FLM can be used to describe normal sleep-wake cycles of healthy adult dogs and the effects of physiologic traits on these patterns of activity. This foundation makes it possible to characterize deviations from normal patterns, including those associated with chronic pain and cognitive dysfunction syndrome. This can improve detection of these conditions in dogs, benefitting them and their potential as models for human disease.

摘要

伴侣(宠物)犬的研究具有巨大的转化潜力,因为它们与人类共患许多疾病。其中包括影响睡眠的疾病,包括慢性疼痛和认知功能障碍。在研究犬类睡眠方面已经取得了重大进展,包括开发非侵入性多导睡眠图;然而,对犬类睡眠模式的基本理解仍然描述不足。本研究旨在使用活动计和功能线性模型(FLM)为健康的成年伴侣犬建立基线睡眠-觉醒周期和活动模式。42 只狗被招募并佩戴活动监测器 14 天。FLM 显示出活动的双峰模式,具有性别、体重和年龄的显著影响;年龄的影响在活动高峰期尤为明显。这项研究表明,FLM 可用于描述健康成年犬的正常睡眠-觉醒周期,以及生理特征对这些活动模式的影响。这为描述正常模式的偏差奠定了基础,包括与慢性疼痛和认知功能障碍综合征相关的模式。这可以提高犬类这些疾病的检测能力,使它们及其作为人类疾病模型的潜力受益。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/86ae/7747556/184a7ca5cd22/41598_2020_79274_Fig1_HTML.jpg

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