Biomarkers for cognitive dysfunction diseases and method for detecting cognitive dysfunction disease using biomarkers
a cognitive dysfunction and biomarker technology, applied in the field of biomarkers for cognitive dysfunction diseases and methods for detecting cognitive dysfunction diseases using biomarkers, can solve problems such as memory impairment, impairment of ability, and pain in the social life function of patients
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Benefits of technology
Problems solved by technology
Method used
Image
Examples
experiment example 1
[0140]Among the marker proteins associated to neurodegenerative diseases including Alzheimer's disease, those of the complement system, namely, complement C3, complement C4, and complement factor H and those involved in suppressing the formation of Aβ fibers responsible for cerebral amyloidosis, namely, transthyretin and alpha-2-macroglobulin are employed as markers, and the biological samples obtained from study subjects were examined by an immunoassay method, thereby detecting cognitive dysfunction diseases based on the existence or the level of the marker as an index.
[0141]As a method for detection, a multiplex immunoassay method which simultaneously detects multiple markers (analytes) in the biological samples was employed.
(1) Serum Samples
[0142]The term in each parenthesis is abbreviated hereinafter as indicated just before the parenthesis.
[0143]The serum samples obtained from 37 AD (Alzheimer's disease) cases, 22 NDC(subjects having no mental diseases) cases, and 39 MCI (mild ...
experiment example 2
(1) Principle of Logistic Regression Analysis
[0162]This method allows the coefficient of each parameter relevant to a biomarker to be obtained from a data set to give the discrimination probability on the patient basis in the two disease categories (normal and disease). A relatively detailed explanation relating to the logistic regression can be obtained from the followings: Non-Patent Document 3: Czepiel, S A, http: / / czep.net / stat / mlelr.pdf, 2010, Maximum likelihood estimation of logistic regression models: theory and implementation. This explanation includes the analysis based on Newton-Raphson method.
[0163]The principle of this analytical procedure is as follows. When assuming the probability of occurrence of a certain event as P, then Equation 1:
F(Z)=p=11+e-Z(1)
is known to be approximated to a cumulative standard normal distribution (Non-Patent Document 4: Bowling, S R, et al. JIEM, 2009, 2: 114-127, A logistic approximation to the cumulative normal distribution.). Using this ap...
PUM
| Property | Measurement | Unit |
|---|---|---|
| chemiluminescent immunoassay | aaaaa | aaaaa |
| enzyme activity assay | aaaaa | aaaaa |
| high pressure liquid chromatography | aaaaa | aaaaa |
Abstract
Description
Claims
Application Information
Login to View More 


