| Primary information |
|---|
| SALID | SAL_11066 |
| Biomarker name | Serpin B13 |
| Biomarker Type | Diagnostic |
| Sampling Method | Study included 47 emergency physicians ( fatigue and non-fatigue) while awake and after continuous as duty for 18-24 hrs. |
| Collection Method | Stimulated whole saliva accumulated at the sublingual site was collected following threetime mouth-rinse with sterile saline. |
| Analysis Method | Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis |
| Collection Site | Whole Saliva |
| Disease Category | Other |
| Disease/Condition | Fatigue |
| Disease Subtype | NA |
| Fold Change/ Concentration | NA |
| Up/Downregulated | NA |
| Exosomal | NA |
| Organism | Homo sapiens |
| PMID | 30556895 |
| Year of Publication | 2018 |
| Biomarker ID | Q9UIV8 |
| Biomarker Category | Protein |
| Sequence | MDSLGAVSTRLGFDLFKELKKTNDGNIFFSPVGILTAIGMVLLGTRGATASQLEEVFHSEKETKSSRIKAEEKEVIENTEAVHQQFQKFLTEISKLTNDYELNITNRLFGEKTYLFLQKYLDYVEKYYHASLEPVDFVNAADESRKKINSWVESKTNEKIKDLFPDGSISSSTKLVLVNMVYFKGQWDREFKKENTKEEKFWMNKSTSKSVQMMTQSHSFSFTFLEDLQAKILGIPYKNNDLSMFVLLPNDIDGLEKIIDKISPEKLVEWTSPGHMEERKVNLHLPRFEVEDGYDLEAVLAAMGMGDAFSEHKADYSGMSSGSGLYAQKFLHSSFVAVTEEGTEAAAATGIGFTVTSAPGHENVHCNHPFLFFIRHNESNSILFFGRFSSP |
| Title of study | Discovery and identification of fatigue-related biomarkers in human saliva |
| Abstract of study | OBJECTIVE: To identify stable and specific biomarkers/biomarker combinations for fatigue assessment and establish a discriminant model.PATIENTS AND METHODS: 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.RESULTS: 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%).CONCLUSIONS: We identified 30 fatigue-related protein markers from saliva. We also established a fatigue assessment model for emergency physicians using salivary biomarkers. |