Hebei University of Engineering, Affiliated Hospital, College of Medicine, Handan, China.
Eur Rev Med Pharmacol Sci. 2018 Dec;22(23):8519-8536. doi: 10.26355/eurrev_201812_16553.
To identify stable and specific biomarkers/biomarker combinations for fatigue assessment and establish a discriminant model.
Saliva was collected and electroencephalogram analysis was performed for 47 emergency physicians while awake and after continuoutas duty for 18-24 h. Physicians were divided into the fatigue and non-fatigue groups. Protein spectra of completely quantified saliva specimens were identified before and after long working hours using mass spectrometry. Data were analyzed through Proteome Discoverer software combined with SEQUEST to search protein databases. Proteins were characterized by collision-induced dissociation spectra. A global internal standard (GIS) was added to each group of samples and labeled by tandem mass tags m/z 131.1. All data were compared with GIS, and data between groups were further compared. Qualitative and quantitative data on proteins were exported for fatigue-related proteomic analysis, and a fatigue assessment model was established.
We identified 767 salivary proteins in the fatigue group. The correct rates of the discriminant function of the non-fatigue and fatigue groups were 97.1% and 91.7%, respectively (the total correct rate was 95.7%).
We identified 30 fatigue-related protein markers from saliva. We also established a fatigue assessment model for emergency physicians using salivary biomarkers.
确定用于疲劳评估的稳定且特异的生物标志物/生物标志物组合，并建立判别模型。
对 47 名急诊医生在清醒时和连续工作 18-24 小时后采集唾液并进行脑电图分析。将医生分为疲劳组和非疲劳组。使用质谱法在长时间工作前后对完全定量的唾液标本的蛋白质谱进行鉴定。通过 Proteome Discoverer 软件与 SEQUEST 联合分析数据，以搜索蛋白质数据库。通过碰撞诱导解离光谱对蛋白质进行表征。向每组样品中添加全局内标 (GIS)，并用串联质量标签 m/z 131.1 标记。将所有数据与 GIS 进行比较，并进一步比较组间数据。导出与疲劳相关的蛋白质组学分析的蛋白质定性和定量数据，并建立疲劳评估模型。
我们在疲劳组中鉴定出 767 种唾液蛋白。非疲劳组和疲劳组的判别函数的准确率分别为 97.1%和 91.7%（总准确率为 95.7%）。
我们从唾液中鉴定出 30 种与疲劳相关的蛋白质标志物。我们还使用唾液生物标志物为急诊医生建立了疲劳评估模型。
