研究目的
To introduce and evaluate techniques based on Raman spectroscopy for noninvasive observation and analysis of intact live cells and tissues, aiming to improve research in medicine and health.
研究成果
Raman spectroscopy is a powerful, noninvasive tool for live cell and tissue analysis, enabling the detection of molecular changes associated with diseases like cancer at early stages. It has high potential for medical applications, including endoscopic diagnosis and health monitoring, due to its ability to provide molecular information without labeling or invasive procedures. Future developments should focus on improving sensitivity and specificity for broader clinical use.
研究不足
The molecular composition of biological samples is complex, leading to overlapping bands in Raman spectra that can conceal specific molecular information. The technique may not be perfect for all applications, and there are constraints in sensitivity and specificity for early cancer detection. Additionally, the review notes that fluorescent protein techniques have limitations like disturbance of protein localization and inapplicability to human studies without gene modification.
1：Experimental Design and Method Selection:
The paper is a review, so it does not describe a specific experimental design but summarizes existing techniques and applications of Raman spectroscopy for live cell and tissue analysis, including the use of multivariate analysis like PCA and PLSR.
2：Sample Selection and Data Sources:
References various studies involving human and animal samples, such as lung cancer cells, neural cells, colorectal cancer in mice, and human esophageal cancer tissues.
3：List of Experimental Equipment and Materials:
Mentions Raman microscopes, fiber-optic Raman probes, endoscopes, CCD detectors, and specific probes like ball lens-installed hollow optical fiber Raman probe (BHRP).
4：Experimental Procedures and Operational Workflow:
Describes procedures for Raman measurements in live samples, including excitation wavelengths (e.g., 532 nm, 785 nm), data collection, and analysis using statistical methods.
5：Data Analysis Methods:
Involves multivariate analysis techniques such as principal component analysis (PCA), partial least squares regression (PLSR), linear discriminant analysis (LDA), and receiver operating characteristic (ROC) curve analysis for spectral data interpretation.
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