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Worldwide breast cancer incidence has increased by more than twenty percent in the past decade. It is also known that in that time, mortality due to the affliction has increased by fourteen percent. Using optical-based diagnostic techniques, such as Raman spectroscopy, has been explored in order to increase diagnostic accuracy in a more objective way along with significantly decreasing diagnostic wait-times. In this study, Raman spectroscopy with 532-nm excitation was used in order to incite resonance effects to enhance Stokes Raman scattering from unique biomolecular vibrational modes. Seventy-two Raman spectra (41 cancerous, 31 normal) were collected from nine breast tissue samples by performing a ten-spectra average using a 500-ms acquisition time at each acquisition location. The raw spectral data was subsequently prepared for analysis with background correction and normalization. The spectral data in the Raman Shift range of 750- 2000 cm-1 was used for analysis since the detector has highest sensitivity around in this range. The matrix decomposition technique nonnegative matrix factorization (NMF) was then performed on this processed data. The resulting leave-oneout cross-validation using two selective feature components resulted in sensitivity, specificity and accuracy of 92.6%, 100% and 96.0% respectively. The performance of NMF was also compared to that using principal component analysis (PCA), and NMF was shown be to be superior to PCA in this study. This study shows that coupling the resonance Raman spectroscopy technique with subsequent NMF decomposition method shows potential for high characterization accuracy in breast cancer detection.

Early detection of prostate cancer is critical for the success of cancer therapy. It is believed that the biochemical changes that cause the optical spectra changes would appear earlier than the histological aberration. The aim of this ex vivo study was to evaluate the ability of Stokes Shift Spectra (S3) to identify human prostate cancerous tissues from the normal. Fifteen (15) pairs of with pathologically confirmed human prostate cancerous and normal tissues underwent Stokes Shift Spectra measurements with selective wavelength interval of 40 nm. The spectra were then analyzed using machine learning (ML) algorithms to classify the two types of tissues. The ML algorithms including principal component analysis (PCA) and nonnegative matrix factorization (NMF) were used for dimension reduction and feature detection. The characteristic component spectra were used to identify the key fluorophores related to carcinogenesis. The results show that these key fluorophores within tissue, e.g., tryptophan, collagen, and NADH, have different relative concentrations between cancerous and normal tissues. A multi-class classification was performed using support vector machines (SVMs). A leave-one-out cross validation was used to evaluate the performance of the classification with the gold standard histopathological results as the ground truth. The results with high sensitivity and specificity indicate that the S3 method is effective for detecting changes of fluorophore composition in human prostate tissues due to the development of cancer. © 2021 SPIE.

This chapter first reviews the research and applications of nonresonance and resonance Raman spectroscopy for analysis of human brain normal and abnormal tissues. Next, special emphasis is made on our recent achievements of visible resonance Raman (VRR) technique in primary human brain tumor disease investigation and diagnosis. Visible resonance Raman (VRR) spectroscopy technique uses excitation of visible light (532 nm) to evaluate the resonant and nonresonant molecular vibrational modes in biological tissues. The VRR signal intensities are enhanced by two to three orders of magnitude for faster use in medical applications in quasi real time. VRR opens up a new stainless “molecular optics based histopathology” diagnosis approach. © 2019 Elsevier Ltd. All rights reserved.

The unique advantage of visible resonance Raman (VRR) spectroscopy using 532 nm excitation wavelength for biological samples is the resonance enhancement of vibrational modes of chemical bonds from cells and tissues. The aim of this study is specifically to reveal the VRR characteristic spectra of different organs in mice, find the molecular alterations in the development of white matter and gray matter of mouse embryos at different ages and study the VRR spectral information of the mouse embryo head using VRR technology.

There is still a lack of reliable intraoperative tools for glioma diagnosis and to guide the maximal safe resection of glioma. We report continuing work on the optical biopsy method to detect glioma grades and assess glioma boundaries intraoperatively using the VRR-LRRTM Raman analyzer, which is based on the visible resonance Raman spectroscopy (VRR) technique. A total of 2220 VRR spectra were collected during surgeries from 63 unprocessed fresh glioma tissues using the VRR-LRRTM Raman analyzer. After the VRR spectral analysis, we found differences in the native molecules in the fingerprint region and in the high-wavenumber region, and differences between normal (control) and different grades of glioma tissues. A principal component analysis–support vector machine (PCA-SVM) machine learning method was used to distinguish glioma tissues from normal tissues and different glioma grades. The accuracy in identifying glioma from normal tissue was over 80%, compared with the gold standard of histopathology reports of glioma. The VRR-LRRTM Raman analyzer may be a new label-free, real-time optical molecular pathology tool aiding in the intraoperative detection of glioma and identification of tumor boundaries, thus helping to guide maximal safe glioma removal and adjacent healthy tissue preservation.

Machine learning algorithms were used to classify and analyze spectral data collected by visible resonance Raman spectroscopy to distinguish normal human brain tissue and glioma tumor tissues at different grades and show promising results. © OSA 2020 © 2020 The Author(s)

Recent reports pointed out that the application of optical spectroscopy in the field of liquid biopsy has aroused great interest among researchers and demonstrated the potential of its clinical application. We report a preliminary investigation on the visible resonance Raman (VRR) spectra of human brain blood liquid collected from the scalp and around the meningeal tumor during surgery and a set of venous blood samples from healthy people and glioma grade III patients using a portable VRR-LRRTM, HR800, HR-Evolution and WITec300 Raman systems in vivo and ex vivo. The biochemical fingerprints and molecular biomarkers were found. These findings indicate that if VRR spectroscopy technology is combined with polymerase chain reaction (PCR) or genetic molecular biomarker methods (VRR-PCR), it will greatly increase the possibility for its clinical application. © 2021 SPIE.

The Resonance Raman (RR) spectra of basal cell carcinoma (BCC) and normal human skin tissues were analyzed using 532nm laser excitation. RR spectral differences in vibrational fingerprints revealed skin normal and cancerous states tissues. The standard diagnosis criterion for BCC tissues are created by native RR biomarkers and its changes at peak intensity. The diagnostic algorithms for the classification of BCC and normal were generated based on SVM classifier and PCA statistical method. These statistical methods were used to analyze the RR spectral data collected from skin tissues, yielding a diagnostic sensitivity of 98.7% and specificity of 79% compared with pathological reports.

The goal of the research is to determine the prognostic molecular pathological changes in components and composition, for human brain glioma gradings in comparison with normal tissues in three-dimensional Raman imaging profiles by visible Resonance Raman (VRR) imaging. <p> </p>VRR images from twenty-five specimens including three healthy tissues, one normal control, and twenty-one glioma tissues of grades II, II-III and III-IV with histology examination were measured and investigated using WITec300R confocal micro Raman imaging system with laser excitation of 532nm. <p> </p>Two-dimensional RR spectral mappings performed in 20&mu;m x 20&mu;m generated 400 images which integrated the intensity of the specific biochemical bonds as the third dimension. The three-dimension (3D) map demonstrated the spatial distributions of three selected sets of RR spectra of molecular biomarkers, and revealed significant differences in the spectra between normal and glioma tissues of different grades due to the composition changes in key molimageecules. These RR molecular spectral fingerprints have displayed: a clear enhancement of RR vibrational modes at 1129-1131cm^-1 and 2934cm^-1 which are supposed to be arising from lipoproteins; evident decreased RR vibrational modes at 1442cm^-1 and 2854cm^-1 which are from saturated fatty acids bonds in all-grades of glioma brain tissues compared with normal tissues; and the enhanced RR spectral modes of 1129 cm^-1 and 2938cm^-1 which suggest contribution from lactate. These findings may provide a novel proof for anaerobic glycolysis metabolic process in brain glioma cancer tissues that has been explained by Warburg effects.

The purpose of this study is to examine optical spatial frequency spectroscopy analysis (SFSA) combined with visible resonance Raman (VRR) spectroscopic method, for the first time, to discriminate human brain metastases of lung cancers adenocarcinoma (ADC) and squamous cell carcinoma (SCC) from normal tissues. A total of 31 label-free micrographic images of three types of brain tissues were obtained using a confocal micro-Raman spectroscopic system. VRR spectra of the corresponding samples were synchronously collected using excitation wavelength of 532[Formula: see text]nm from the same sites of the tissues. Using SFSA method, the difference in the randomness of spatial frequency structures in the micrograph images was analyzed using Gaussian function fitting. The standard deviations, [Formula: see text] calculated from the spatial frequencies of the micrograph images were then analyzed using support vector machine (SVM) classifier. The key VRR biomolecular fingerprints of carotenoids, tryptophan, amide II, lipids and proteins (methylene/methyl groups) were also analyzed using SVM classifier. All three types of brain tissues were identified with high accuracy in the two approaches with high correlation. The results show that SFSA–VRR can potentially be a dual-modal method to provide new criteria for identifying the three types of human brain tissues, which are on-site, real-time and label-free and may improve the accuracy of brain biopsy.

Alzheimer’s disease (AD) pathogenesis is widely believed to be associated with the production and deposition of the β-amyloid peptide (Aβ) and neurofibrillary tangles (NFTs) which are composed of a highly-phosphorylated form of the microtubule-associated protein tau. Based on the above hypothesis, there are currently no sufficiently effective technologies and drugs for early detection and treatment of AD. Even the most promising new drug Lecanemab that is based on an anti-amyloid monoclonal antibody therapy, has only partially slowed down the cognitive performance of patients with mild impairment caused by Alzheimer's disease. The main symptoms of AD brain tissue lesions in patients are the deposition of β-amyloid peptide and the hyperphosphorylation of tau protein, which aggregates the microtubule structure of neurons. Therefore, Aβ deposition and hyperphosphorylation of Tau are important pathological biomarkers of Alzheimer's disease. Therefore, the main targets of research for AD prevention, detection and pharmaceuticals are still Aβ and Tau protein. The aim of this study was to detect the changes of Aβ and Tau proteins in the mouse brain tissue with AD and control samples using Visible Resonance Raman (VRR) spectroscopic technology. An attempt was made to develop criteria for the detection of early AD lesions by optical spectroscopy technology. The VRR spectra of AD, the control mouse brain tissues, and Aβ and Tau proteins were recorded and analyzed. The AD and the control mouse brain tissue samples were selected from the thalamus, frontal lobe cortex and hippocampus brain areas. VRR technology with high spatial resolution and the resonance-enhanced features of certain protein molecules is first used in this study to detect and characterize the changes of Aβ and Tau proteins in AD mouse brain model. The optical spectroscopy biomarkers of AD and Control brain tissue were identified in fingerprint and the high-wavenumber regions. The Raman spectra of the secondary structure of protein in amide (I-II-III-B-A) are detected and analyzed. The results indicate that the intensity of Amide I decreased at the 1666 cm^-1 corresponding to the β-sheet structure, and the intensity of the amide III bands (1220- 1320 cm^-1) increased in all AD brain tissues. It was also observed that the Raman peaks of 1448 and 980 cm^-1 related to the abundance of proline, serine, and threonine at tau phosphorylation sites were significantly enhanced in the frontal lobe cortex and hippocampus of AD brain tissues. The intensity ratio biomarker of high phosphorylation in the high wavenumber range from 2898 to 2932 cm-1 increased in all AD brain tissues. Changes of protein secondary conformation and abnormally phosphorylated tau or tauopathies were observed. In summary, VRR is a sensitive tool for characterizing protein structural changes and monitoring the tau phosphorylation. It may potentially be used for early detection of AD.

Analyzing spectral or imaging data collected with various optical biopsy methods is often times difficult due to the complexity of the biological basis. Robust methods that can utilize the spectral or imaging data and detect the characteristic spectral or spatial signatures for different types of tissue is challenging but highly desired. In this study, we used various machine learning algorithms to analyze a spectral dataset acquired from human skin normal and cancerous tissue samples using resonance Raman spectroscopy with 532nm excitation. The algorithms including principal component analysis, nonnegative matrix factorization, and autoencoder artificial neural network are used to reduce dimension of the dataset and detect features. A support vector machine with a linear kernel is used to classify the normal tissue and cancerous tissue samples. The efficacies of the methods are compared.

The accurate identification of the human brain tumor boundary and the complete resection of the tumor are two essential factors for the removal of the glioma tumor in brain surgery. We present a visible resonance Raman (VRR) spectroscopy technique for differentiating the brain tumor margin and glioma grading. Eighty-seven VRR spectra from twenty-one human brain specimens of four types of brain tissues, including the control, glioma grade II, III, and IV tissues, were observed. This study focuses on observing the characteristics of new biomarkers and their changes in the four types of brain tissue. We found that two new RR peaks at 1129 cm-1 and 1338 cm-1 associated with molecular vibrational bonds in four types of brain tissues are significantly different in peak intensities of VRR spectra. These two resonance enhanced peaks may arise from lactic acid/phosphatidic acid and adenosine triphosphate (ATP)/nicotinamide adenine dinucleotide, respectively. We found that lactic acid and ATP concentrations vary with glioma gratings. The higher the grade of malignancy, the more the increase in lactic acid and ATP concentrations. These two RR peaks may be considered as new molecular biomarkers and used to evaluate glioma grades and identify the margin of gliomas from the control tissues. The metabolic process of lactic acid and ATP in glioma cells based on the VRR spectral changes may reveal the Warburg hypothesis. © 2018 Author(s).

Glioma is one of the most refractory types of brain tumor. Accurate tumor boundary identification and complete resection of the tumor are essential for glioma removal during brain surgery. We present a method based on visible resonance Raman (VRR) spectroscopy to identify glioma margins and grades. A set of diagnostic spectral biomarkers features are presented based on tissue composition changes revealed by VRR. The Raman spectra include molecular vibrational fingerprints of carotenoids, tryptophan, amide I/II/III, proteins, and lipids. These basic in situ spectral biomarkers are used to identify the tissue from the interface between brain cancer and normal tissue and to evaluate glioma grades. The VRR spectra are also analyzed using principal component analysis for dimension reduction and feature detection and support vector machine for classification. The cross-validated sensitivity, specificity, and accuracy are found to be 100%, 96.3%, and 99.6% to distinguish glioma tissues from normal brain tissues, respectively. The area under the receiver operating characteristic curve for the classification is about 1.0. The accuracies to distinguish normal, low grade (grades I and II), and high grade (grades III and IV) gliomas are found to be 96.3%, 53.7%, and 84.1% for the three groups, respectively, along with a total accuracy of 75.1%. A set of criteria for differentiating normal human brain tissues from normal control tissues is proposed and used to identify brain cancer margins, yielding a diagnostic sensitivity of 100% and specificity of 71%. Our study demonstrates the potential of VRR as a label-free optical molecular histopathology method used for in situ boundary line judgment for brain surgery in the margins.

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.

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.