Y R Sharma Spectroscopy
Doris Joo
The site is secure.
The ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.
Raman spectroscopy (RS) is widely used as a non-invasive technique in screening for the diagnosis of oral cancer. The potential of this optical technique for several biomedical applications has been proved. This work studies the efficacy of RS in detecting oral cancer using sub-site-wise differentiation. A total of 80 samples (44 tumor and 36 normal) were cryopreserved from three different sub-sites: The tongue, the buccal mucosa, and the gingiva of the oral mucosa during surgery. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to classify the samples and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. The normal and tumor tissues were differentiated under the PCA-LDA model with an accuracy of 81.25% (sensitivity: 77.27%, specificity: 86.11%). The PCA-QDA classifier model differentiated these tissues with an accuracy of 87.5% (sensitivity: 90.90%, specificity: 83.33%). The PCA-QDA classifier model outperformed the PCA-LDA-based classifier. The model studies revealed that protein, amino acid, and beta-carotene variations are the main biomolecular difference markers for detecting oral cancer.
In this study, we developed a novel quantitative analysis method to enhance the detection capability for oral cancer screening. We combined two different optical techniques, a light-based detection technique (visually enhanced lesion scope) and a vibrational spectroscopic technique (Raman spectroscopy). Materials and methods: Thirty-five oral cancer patients who went through surgery were enrolled. Thirty-five cancer lesions and thirty-five control samples with normal oral mucosa (adjacent to the cancer lesion) were analyzed. Thirty-five autofluorescence images and 70 Raman spectra were taken from 35 cancer and 35 control group cryopreserved samples. The normalized intensity and heterogeneity of the 70 regions of interest (ROIs) were calculated along with 70 averaged Raman spectra. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to differentiate the cancer and control groups (normal). The classifications rates were validated using two different validation methods, leave-one-out cross-validation (LOOCV) and k-fold cross-validation. Results: The cryopreserved normal and tumor tissues were differentiated using the PCA-LDA and PCA-QDA models. The PCA-LDA of Raman spectroscopy (RS) had 82.9% accuracy, 80% sensitivity, and 85.7% specificity, while ROIs on the autofluorescence images were differentiated with 90% accuracy, 100% sensitivity, and 80% specificity. The combination of two optical techniques differentiated cancer and normal group with 97.14% accuracy, 100% sensitivity, and 94.3% specificity. Conclusion: In this study, we combined the data of two different optical techniques. Furthermore, PCA-LDA and PCA-QDA quantitative analysis models were used to differentiate tumor and normal groups, creating a complementary pathway for efficient tumor diagnosis. The error rates of RS and VELcope analysis were 17.10% and 10%, respectively, which was reduced to 3% when the two optical techniques were combined.
Dr. Shiv Sharma is a Tenured Research Professor at the Hawaii Institute of Geophysics and Planetology, School of Ocean and Earth Science and Technology (SOEST) at the University of Hawaii at Manoa. He is one of the Co-Principal Investigators for SuperCam on the Perseverance rover.
Serving on the Graduate Faculty of both the Department of Earth Sciences, SOEST; and in the Department of Electrical Engineering (EE), College of Engineering. Since 1998, serving on the Faculty of Global Environmental Science, an Undergraduate-Degree Program administered by the Dept. of Oceanography/SOEST, and also serving as a collaborator in the NASA Astrobiology Institute at UHM since 2010.
In the late 1990s/early 2000s, Shiv demonstrated that a pulsed laser coupled with time-gating could be used to take Raman measurements in the daylight, which was revolutionary in more than one way. In addition to being resistance to ambient light and thermal emissions, fluorescence, which is intense and often interferes with Raman signals, could also be avoided. Time-gated Raman spectroscopy synchronizes the pulsed laser source with the sensitive detector so that it can detect the fast Raman signals during the short laser pulses and avoid fluorescence emissions which have longer delays. Professor Sharma and Professor Angel published the first paper on the remote pulsed Raman system in 2002, showing that mineral samples on planetary surfaces could be analyzed up to 66 meters away.4
The predecessor of the Perseverance rover, Curiosity, only has the capability of performing laser induced breakdown spectroscopy (LIBS), which gives information about the atomic composition. The team at NASA wanted to learn more about the molecular structure of the samples, information which Raman spectroscopy could afford. In 2001, Dr. Sharma met Roger Wiens, the Principal Investigator of SuperCam at the Los Alamos National Laboratory. Dr. Sharma offered some advice regarding Raman spectra measurements of minerals with a pulsed laser. The experiments took off and so did their friendship.
Professor Stanley Michael Angel is a Carolina Trustee Professor and Fred M. Weissman Palmetto Chair in Chemical Ecology, Department of Chemistry and Biochemistry at the University of South Carolina. He currently works on the SuperCam team as a Scientific Research Collaborator and Scientific Payload Download Leader (sPDL).
Roger Wiens is the Principal Investigator of SuperCam and one of the co-investigators of SHERLOC. In 2016, the government of France knighted Wiens for his contribution in forging strong bonds between the French and American scientific communities.
Sanford Asher, a Distinguished Professor in the Department of Chemistry at the University of Pittsburgh, is involved with the development of the Raman spectrometer and the ultraviolet laser in SHERLOC.
Although we endeavor to make our web sites work with a wide variety of browsers, we can only support browsers that provide sufficiently modern support for web standards. Thus, this site requires the use of reasonably up-to-date versions of Google Chrome, FireFox, Internet Explorer (IE 9 or greater), or Safari (5 or greater). If you are experiencing trouble with the web site, please try one of these alternative browsers. If you need further assistance, you may write to he...@aps.org.
The exotic correlated phases that emerge in the low-dispersing moir bands of twisted van der Waals structures have opened new exciting opportunities in condensed-matter physics. These systems exhibit a rich phase diagram of novel physical phenomena and exotic correlated phases that emerge in the low-dispersing bands. Despite its popularity, the resulting mini-bands in the conduction band of MoS2 moir superlattices remained elusive so far. In this work, we resolve these spin-valley mini-bands via transport spectroscopy in micron-scale devices. The theoretical energy scales exhibit an astounding agreement with our experimental observations, which together with the behavior under thermal activation, suggest an electronic phase transition. These intriguing observations highlight the potential of twisted MoS2 structures as a playground to explore correlated electron states and associated phenomena.
(a) Schematics of the sample configuration with the moir superlattices encapsulated in hBN. (b) Cartoon showing the atomic arrangement of a twisted bilayer MoS2, with AB stacked domains marked in pink circles. (c) False-colored SEM image of the device with the two MoS2 flakes outlined in green and pink for clarity. The two contacts (source and drain) used for the measurement are marked with black arrows (see Appendix pp1 for more details). (d) A level diagram illustrating transport via discrete moir mini-bands. The inset shows an equivalent circuit diagram with the Schottky contact represented as a diode. (e) Energy dispersion and (f), band diagrams illustrating the band hybridization near the magic angle or flat-band condition (left) and the spontaneous spin-valley symmetry breaking that emerges with interactions (right).
We believe this unmet need can be addressed by combining advancements in vibrational spectroscopy (Raman Spectroscopy, RS; Near Infra-Red Spectroscopy, NIRS; and mid infrared spectroscopy, MIRS) with computational methods (machine learning). These technologies have potential to provide non-perturbative, rapid and label-free tissue assessment of morphology and metabolism [4,5,6,7] at the molecular level [8, 9]. In the cardiovascular space, studies from animal or preserved tissue [4, 7, 10,11,12,13,14,15,16] demonstrate that these techniques can quantify myocardial fibrosis by exploring collagen subtypes [17,18,19,20], cross linking [21, 22] and distribution [20, 23]. The techniques are complementary, with RS techniques being more sensitive to metabolic data at a molecular level [24], and infrared-based techniques (such as MIRS, NIRS) better equipped to assess morphological changes [24]. Advances in machine learning have significantly reduced the processing time for analysing these data, and bear potential for real-time diagnoses. Clinical translation is hindered by current studies being restricted to one modality of spectroscopy (RS, Mid IR or NIRS) and have not yet used point-of-care instruments on human tissue.
In this report, we combine RS and NIRS scans of cardiac tissue to obtain non-invasive multimodal spectroscopic signatures (MSS) and use machine learning (ML) to compare its accuracy to conventional histopathology. These scans, which can be performed in 3 s, are iterative steps to generating a point-of-care and non-invasive morphological and metabolic diagnoses in heart disease.
c80f0f1006