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Intrinsic fluorescence spectra of fresh normal and cancerous human breast tissues were measured using two selective excitation wavelengths including 290nm and 340nm. Dual-wavelength excitation may reveal more molecular information than single-wavelength excitation. In the meantime, it is significantly faster than the acquisition of excitation-emission (EEM) matrix. Unsupervised machine learning algorithms principal component analysis (PCA) and non-negative matrix factorization (NMF) were used to reduce the dimensionality of the spectral data. The relative concentrations of the basis spectra retrieved by PCA and NMF were considered features of the samples and used to distinguish normal and malignant tissues. The performances of classification using support vector machine (SVM) based on PCA and NMF features were compared. The classification using spectral data with dual-wavelength excitation was compared with single-wavelength excitation. Classification based on NMF-retrieved components from spectral data with dual-wavelength excitation yielded the best performance. © 2019 SPIE.
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ZnO and Fe-doped ZnO nanoparticles were analyzed in ethanol solution and dry powder form using fluorescence spectroscopy. Near-band-edge emission (NBE) and defect emission (DE) peaks were studied. A blue-shift was observed with the NBE emission peak. © OSA 2019. The Author(s).
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Native fluorescence spectra play important roles in cancer detection. It is widely acknowledged that the emission spectrum of a tissue is a superposition of spectra of various salient fluorophores. However, component quantification is essentially an ill-posed problem. To address this problem, the native fluorescence spectra of normal human very low (LNCap), moderately metastatic (DU-145), and advanced metastatic (PC-3) cell lines were studied by the selected wavelength of 300 nm to investigate the key fluorescent molecules such as tryptophan, collagen and NADH. The native fluorescence spectra of cancer cell lines at different risk levels were analyzed using various machine learning algorithms for feature detection and develop criteria to separate the three types of cells. Principal component analysis (PCA), nonnegative matrix factorization (NMF), and partial least squares fitting were used separately to reduce dimension, extract features and detect biomolecular alterations reflected in the spectra. The scores corresponding to the basis spectra were used for classification. A linear support vector machine (SVM) was used to classify the spectra of the cells with different metastatic ability. In detection of signals coming from tryptophan and NADH with observed data corrupted by noise and inference, a sufficient statistic can be obtained based on the basis spectra retrieved using nonnegative matrix factorization. This work shows changes of relative contents of tryptophan and NADH obtained from native fluorescence spectroscopy may present potential criteria for detecting cancer cell lines of different metastatic ability. © 2018 SPIE.
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Pancreatic islet dysfunction leading to insufficient glucose-stimulated insulin secretion triggers the clinical onset of diabetes. How islet dysfunction develops is not well understood at the cellular level, partly owing to the lack of approaches to study single islets longitudinally in vivo. Here, we present a noninvasive, high-resolution system to quantitatively image real-time glucose metabolism from single islets in vivo, currently not available with any other method. In addition, this multifunctional system simultaneously reports islet function, proliferation, vasculature and macrophage infiltration in vivo from the same set of images. Applying our method to a longitudinal high-fat diet study revealed changes in islet function as well as alternations in islet microenvironment. More importantly, this label-free system enabled us to image real-time glucose metabolism directly from single human islets in vivo for the first time, opening the door to noninvasive longitudinal in vivo studies of healthy and diabetic human islets. © 2016. Published by The Company of Biologists Ltd.
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Resonance Raman spectroscopy is used for rapid detection of skin BCC cancer. The cross-validated classification accuracy is achieved to be as high as 98% using nonnegative matrix factorization along with support vector machine statistical method. © OSA 2017.
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A VRR-LRR analyzer with handheld fiber‐optic probe is reported for the first time for diagnosis of brain GBM in vivo. The sensitivity for identification is 80% compared with histopathology examination. © OSA 2019. The Author(s).
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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. © 2019 SPIE.
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