在衰老小鼠中使用计算视频分析来评估慢性单一疗法,多种药物和处方的效果

Kenji Fujita, John Mach, Sarah N Hilmer
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引用次数: 0

摘要

在老年人的临床研究中,多重用药(使用≥5种药物)和药物负担指数(DBI;暴露于抗胆碱能和镇静药物)与身体功能受损和虚弱有关。我们使用计算机视频分析衰老小鼠来检查药物对形态学和步态功能的影响。方法对12月龄中年雄性小鼠分别给予不同DBI评分的多药方案治疗剂量或高DBI多药方案的单药治疗。在21个月大的时候,一半接受治疗的动物取消了他们的药物处方(停药)。分别于12、15、18、21、24个月记录裸地录像和小鼠临床衰弱指数。在将开源神经网络应用于视频之后,使用具有变化点检测的状态空间模型分析获得的特征,以检测治疗组和对照组之间随时间的差异。结果对278只小鼠进行了49项形态学和步态特征的测量。成分单一疗法的效果总和不等于综合疗法的效果。与临床数据一致,随着DBI评分的增加,多药方案观察到更大的步态和姿势变化。不同药物的处方解除效果不同,包括可逆的、不可逆的和新的变化。不同的药物暴露对步态、姿势和虚弱预测有不同的影响。结论临床前数据的计算视频分析是一种高通量、高灵敏度检测衰老药物效应的有前途的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using computational video analysis in ageing mice to evaluate the effects of chronic monotherapy, polypharmacy and deprescribing over time
Background In clinical studies of older adults, polypharmacy (use of ≥ 5 drugs) and the Drug Burden Index (DBI; measures exposure to anticholinergic and sedative drugs) are associated with impaired physical function and frailty. We used computational video analysis of aging mice to examine the impact of medications on morphometric and gait function. Methods Middle-aged (12 month) male mice were administered therapeutic doses of medications in polypharmacy regimens with different DBI scores or monotherapy with medications from the High DBI polypharmacy regimen. At age 21 months, half of the treated animals had their medications deprescribed (discontinued). Open field videos and mouse clinical frailty index were recorded at 12, 15, 18, 21 and 24 months. After applying open-source neural networks to the videos, the gained features were analysed to detect differences between the treatment groups and control over time using a state-space model with change point detection. Results We measured 49 morphometric and gait features for 278 mice. The sum of effects of constituent monotherapies did not equal the effects of polypharmacy. Consistent with clinical data, greater gait and posture changes were observed with polypharmacy regimens with increasing DBI scores. Deprescribing effects varied between medications, consisting of reversible, irreversible and novel changes. Different medication exposures had different effects on gait, posture and the prediction of frailty. Conclusion Computational video analysis of preclinical data is a promising tool for high-throughput, sensitive detection of medication effects in aging.
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