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Amyloid beta in nasal secretions may be a potential biomarker of Alzheimer’s disease
We investigated the level of amyloid beta (Aβ) in nasal secretions of patients with Alzheimer’s disease dementia (ADD) using interdigitated microelectrode (IME) biosensors and determined the predictive value of Aβ in nasal secretions for ADD diagnosis. Nasal secretions were obtained from 35 patients...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428828/ https://www.ncbi.nlm.nih.gov/pubmed/30899050 http://dx.doi.org/10.1038/s41598-019-41429-1 |
Sumario: | We investigated the level of amyloid beta (Aβ) in nasal secretions of patients with Alzheimer’s disease dementia (ADD) using interdigitated microelectrode (IME) biosensors and determined the predictive value of Aβ in nasal secretions for ADD diagnosis. Nasal secretions were obtained from 35 patients with ADD, 18 with cognitive decline associated with other neurological disorders (OND), and 26 cognitively unimpaired (CU) participants. Capacitance changes in IMEs were measured by capturing total Aβ (ΔC(tAβ)). After 4-(2-hydroxyethyl)-1-piperazinepropanesulfonic acid (EPPS) was injected, additional capacitance changes due to the smaller molecular weight Aβ oligomers disassembled from the higher molecular weight oligomeric Aβ were determined (ΔC(oAβ)). By dividing two values, the capacitance ratio (ΔC(oAβ)/ΔC(tAβ)) was determined and then normalized to the capacitance change index (CCI). The CCI was higher in the ADD group than in the OND (p = 0.040) and CU groups (p = 0.007). The accuracy of the CCI was fair in separating into the ADD and CU groups (area under the receiver operating characteristic curve = 0.718, 95% confidence interval = 0.591–0.845). These results demonstrate that the level of Aβ in nasal secretions increases in ADD and the detection of Aβ in nasal secretions using IME biosensors may be possible in predicting ADD. |
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