Based on Visible Resonance Raman (VRR) method, we have developed a novel label-free portable VRR LRR2000 Raman analyzer with a portable fiber-optic probe and used it for the classification of human gliomas ex vivo and for the analysis of changes in tumor chemical compositions in molecular level. The purpose of this study was to examine the performance of the LRR2000 Raman analyzer as an optical biopsy tool for detecting human brain tumors compared to the commercial laboratory HR800 and WITec300 micro confocal Raman spectroscopy instruments. As of 2018, a total 1,938 VRR spectra were collected using LRR2000, HR800 and WITec300 Raman system, ex vivo. Identification of the four grades of glioma tumors and control tissues was performed based on the characteristic native molecular fingerprints. LRR2000 demonstrated consistent diagnostic results with HR800 and WITec300 Raman systems. LRR2000 showed the advantages of high speed, convenience and low cost compared to the two confocal micro Raman systems. Using artificial intelligence (AI)-based analysis of part of the data, the cross-validated accuracy for identifying glioma tumors is ~90% compared with gold standard histopathology examination.
Laser-induced fluorescence (LIF) technique was used to generate spectral signatures of endogenous fluorophores relevant to the tissue molecular composition changes in human brain glioma tumors. The goal is to study the changes of fluorescence emission spectra from endogenous fluorophores in human brain glioma of different grades, and to find new biomarkers for prognostic optical molecular pathological diagnosis. Two hundred and thirty-seven (237) native fluorescence spectra from 61 subjects were measured using LabRAM HR Evolution micro photoluminescence (PL) system for four grades of glioma tumors in ex-vivo. The differences of four grades of glioma tumors were identified by the characteristic fluorophores fingerprints under the excitation laser wavelength at UV 325nm. To our best knowledge, this is the first report for human brain study using this technique. The fluorescence peaks of biomarkers with major contribution were found, including tryptophan, collagen, elastin, reduced nicotinamide adenine dinucleotide (NADH), flavin adenine dinucleotide (FAD) and phospholipids that play important roles in the cellular energy metabolism and glycolysis pathway. The ratios of peak intensities and the peak positions in fluorescence spectra of may be used to diagnose human brain diseases or to guide biopsy during surgical resection.
In this work, we propose a novel high-precision frequency estimation method based on harmonic expansion technique and a simple FFT-based algorithm. By increasing the information content through harmonic expansion in spectral domain instead of sampling time length in time domain, the proposed method can greatly improve the frequency estimation precision, needless to introduce other complex algorithms. The harmonic expansion process is to synthetize multiple harmonic components of the fundamental frequency of the input signal, which are detected to perform high-precision frequency estimation. The proposed method is analyzed in theory and numerical analysis, and demonstrated in experiment. The harmonic expansion in the experiment is achieved by microwave photonics technology through optical comb generation by electro-optical modulation. The signal optical comb containing wideband optical harmonic components are downconverted into low frequency band in electrical domain through optical harmonic sampling. Through digital signal processing on the 2th ~ 12th harmonic components with the FFT-based algorithm, the frequency estimation precision of a single RF tone is improved by about dozens of times as compared with the measurement value of the 1th fundamental frequency. This method is also compatible with other existing FFT-based high-precision frequency estimation algorithms and has the potential for a variety of application scenarios, such as Radar/LIDAR, spectrum sensing, vibration measurement and electronic reconnaissance.
Visible resonant Raman (VRR) spectroscopy provides an effective way to enhance Raman signal from particular
bonds associated with key molecules due to changes on molecular level. This paper reports on the VRR use for
detection of human brain the control and gliomas of three grades. From the RR spectra additional two molecular
vibrational biomarkers at 1129cm-1 and 1338cm-1, for the four types of brain tissues are significantly different in
intensity. The new RR spectral peaks can be used as molecular biomarkers to evaluate glioma grades and identify
the margin of gliomas from the controls. The metabolic process of glioma cells based on the RR spectral changes
may reveal the Warburg hypothesis.
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