研究目的
Investigating the feasibility of using near-infrared (NIR) spectroscopy for the identification of different gallbladder (GB) diseases (stone, polyp, and cancer) by directly analyzing bile juice samples.
研究成果
The whole-sample-covering NIR scheme was shown to be a simple and versatile method to collect the NIR spectra of dried bile juice samples with full representation of involved bile components. The direct and fast NIR drop-and-dry measurement of bile juice without any further sample treatment or separation is potentially useful to discriminate GB cancer from the other tested GB diseases.
研究不足
The number of samples used in this study is not sufficient to justify clinical applicability of the proposed method. The analysis of relevant individual components in bile juice using LC-MS is necessary to eventually support NIR discrimination results.
1:Experimental Design and Method Selection:
A whole-sample-covering NIR spectroscopy scheme was adopted for the simple drop-and-dry measurement of raw bile juice. A non-NIR absorbing polytetrafluoroethylene (PTFE) plate was chosen as a substrate to form bile juice droplets of a consistent shape. NIR radiation illuminated the whole area of the dried sample for spectral acquisition.
2:Sample Selection and Data Sources:
Bile juice samples were obtained from 20 GB stone, 10 GB polyp, and 4 GB cancer patients after cholecystectomy at Hanyang University Hospital.
3:List of Experimental Equipment and Materials:
PTFE plate, NIR spectrometer (ABB, Canada), concave lens (f: ?20 mm, diameter:
4:4 mm), convex lens (f:
45 mm, diameter: 25.4 mm), aluminum plate with a hole matching the size of a dried sample spot.
5:4 mm), aluminum plate with a hole matching the size of a dried sample spot.
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: Each sample (10 μL) was dropped on a PTFE plate and dried in a desiccator under vacuum conditions for 30 minutes. NIR spectra of the dried samples were recorded using the whole-sample-covering scheme.
6:Data Analysis Methods:
Baseline correction, normalization, and principal component analysis (PCA) were conducted using MATLAB version 2016a.
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