RAMAN SPECTROSCOPY OF HUMAN HEMOGLOBIN FOR DIABETES DETECTION

Glycated hemoglobin (HbA1c) has been increasingly accepted as the gold standard for diabetes monitoring. In this study, Raman spectroscopy was tentatively employed for human hemoglobin (Hb) biochemical analysis aimed at developing a simple blood test for diabetes monitoring. Raman spectroscopy measurements were performed on hemoglobin samples of patients (n = 39) with confirmed diabetes and healthy volunteers (n = 37). The tentative assignments of the measured Raman bands were performed to compare the difference between these two groups. Meanwhile, principal component analysis (PCA) combined with linear discriminant analysis (LDA) were employed to develop effective diagnostic algorithms for classification between normal controls and patients with diabetes. As a result, the spectral features of these two groups demonstrated two distinct clusters with a sensitivity and specificity of 92.3% and 73%, respectively. Then the effectiveness of the diagnostic algorithm based on PCA-LDA technique was confirmed by receiver operating characteristic (ROC) curve. The area under the ROC curve was 0.92, indicating a good diagnostic result. In summary, our preliminary results demonstrate that proposing Raman spectroscopy can provide a significant potential for the noninvasive detection of diabetes.

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In vivosubsite classification and diagnosis of oral cancers using Raman spectroscopy

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545816500176 ◽

2016 ◽

Vol 09 (05) ◽

pp. 1650017 ◽

Cited By ~ 11

Author(s):

Aditi Sahu ◽

Atul Deshmukh ◽

Arti R. Hole ◽

Pankaj Chaturvedi ◽

C. Murali Krishna

Keyword(s):

Raman Spectroscopy ◽

Buccal Mucosa ◽

Large Scale ◽

Survival Rates ◽

Delayed Diagnosis ◽

Disease Free Survival ◽

Principal Component ◽

Diagnostic Algorithm ◽

Linear Discriminant ◽

Oral Cancers

Oral cancers suffer from poor disease-free survival rates due to delayed diagnosis. Noninvasive, rapid, objective approaches as adjuncts to visual inspection can help in better management of oral cancers. Raman spectroscopy (RS) has shown potential in identification of oral premalignant and malignant conditions and also in the detection of early cancer changes like cancer-field-effects (CFE) at buccal mucosa subsite. Anatomic differences between different oral subsites have also been reported using RS. In this study, anatomical differences between subsites and their possible influence on healthy vs pathological classification were evaluated on 85 oral cancer and 72 healthy subjects. Spectra were acquired from buccal mucosa, lip and tongue in healthy, contralateral (internal healthy control), premalignant and cancer conditions using fiber-optic Raman spectrometer. Mean spectra indicate predominance of lipids in healthy buccal mucosa, contribution of both lipids and proteins in lip while major dominance of protein in tongue spectra. From healthy to tumor, changes in protein secondary-structure, DNA and heme-related features were observed. Principal component linear discriminant analysis (PC-LDA) followed by leave-one-out-cross-validation (LOOCV) was used for data analysis. Findings indicate buccal mucosa and tongue are distinct entities, while lip misclassifies with both these subsites. Additionally, the diagnostic algorithm for individual subsites gave improved classification efficiencies with respect to the pooled subsites model. However, as the pooled subsites model yielded 98% specificity and 100% sensitivity, this model may be more useful for preliminary screening applications. Large-scale validation studies are a pre-requisite before envisaging future clinical applications.

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Raman Spectroscopy for Inverted Papilloma: A Proof-of-Concept Study

Otolaryngology ◽

10.1177/0194599818776640 ◽

2018 ◽

Vol 159 (3) ◽

pp. 587-589 ◽

Cited By ~ 1

Author(s):

Marco A. Mascarella ◽

Abdulaziz Alrasheed ◽

Naif Fnais ◽

Ophelie Gourgas ◽

Ghulam Jalani ◽

...

Keyword(s):

Principal Component Analysis ◽

Raman Spectroscopy ◽

Chronic Rhinosinusitis ◽

Principal Component ◽

Inverted Papilloma ◽

Proof Of Concept ◽

Linear Discriminant ◽

Acceptable Accuracy ◽

Sinonasal Mucosa ◽

Inverted Papillomas

Inverted papillomas are tumors of the sinonasal tract with a propensity to recur. Raman spectroscopy can potentially identify inverted papillomas from other tissue based on biochemical signatures. A pilot study comparing Raman spectroscopy to histopathology for 3 types of sinonasal tissue was performed. Spectral data of biopsies from patients with normal sinonasal mucosa, chronic rhinosinusitis, and inverted papillomas are compared to histopathology using principal component analysis and linear discriminant analysis after data preprocessing. A total of 18 normal, 15 chronic rhinosinusitis, and 18 inverted papilloma specimens were evaluated. The model distinguished normal sinonasal mucosa, chronic rhinosinusitis, and inverted papilloma tissue with an overall accuracy of 90.2% (95% confidence interval, 0.86-0.94). In conclusion, Raman spectroscopy can distinguish inverted papilloma, normal sinonasal mucosa, and chronically rhinosinusitis tissue with acceptable accuracy.

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Raman Spectroscopy Analysis for Optical Diagnosis of Oral Cancer Detection

Journal of Clinical Medicine ◽

10.3390/jcm8091313 ◽

2019 ◽

Vol 8 (9) ◽

pp. 1313 ◽

Cited By ~ 9

Author(s):

Ming-Jer Jeng ◽

Mukta Sharma ◽

Lokesh Sharma ◽

Ting-Yu Chao ◽

Shiang-Fu Huang ◽

...

Keyword(s):

Raman Spectroscopy ◽

Discriminant Analysis ◽

Oral Cancer ◽

Cross Validation ◽

Principal Component ◽

Beta Carotene ◽

Invasive Technique ◽

Protein Amino Acid ◽

Linear Discriminant ◽

Model Studies

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.

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A biospectroscopic analysis of human prostate tissue obtained from different time periods points to a trans-generational alteration in spectral phenotype

Scientific Reports ◽

10.1038/srep13465 ◽

2015 ◽

Vol 5 (1) ◽

Cited By ~ 11

Author(s):

Georgios Theophilou ◽

Kássio M. G. Lima ◽

Matthew Briggs ◽

Pierre L. Martin-Hirsch ◽

Helen F. Stringfellow ◽

...

Keyword(s):

Raman Spectroscopy ◽

Principal Component ◽

Attenuated Total Reflection ◽

Prostate Tissue ◽

Projection Algorithm ◽

Human Prostate ◽

Phenotypic Change ◽

Linear Discriminant ◽

Dna And Rna ◽

Immunohistochemical Studies

Abstract Prostate cancer is the most commonly-diagnosed malignancy in males worldwide; however, there is marked geographic variation in incidence that may be associated with a Westernised lifestyle. We set out to determine whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) or Raman spectroscopy combined with principal component analysis-linear discriminant analysis or variable selection techniques employing genetic algorithm or successive projection algorithm could be utilised to explore differences between prostate tissues from differing years. In total, 156 prostate tissues from transurethral resection of the prostate procedures for benign prostatic hyperplasia from 1983 to 2013 were collected. These were distributed to form seven categories: 1983–1984 (n = 20), 1988–1989 (n = 25), 1993–1994 (n = 21), 1998–1999 (n = 21), 2003–2004 (n = 21), 2008–2009 (n = 20) and 2012–2013 (n = 21). Ten-μm-thick tissue sections were floated onto Low-E (IR-reflective) slides for ATR-FTIR or Raman spectroscopy. The prostate tissue spectral phenotype altered in a temporal fashion. Examination of the two categories that are at least one generation (30 years) apart indicated highly-significant segregation, especially in spectral regions containing DNA and RNA bands (≈1,000–1,490 cm−1). This may point towards alterations that have occurred through genotoxicity or through epigenetic modifications. Immunohistochemical studies for global DNA methylation supported this. This study points to a trans-generational phenotypic change in human prostate.

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Raman Spectroscopy in Colorectal Cancer Diagnostics: Comparison of PCA-LDA and PLS-DA Models

Journal of Spectroscopy ◽

10.1155/2016/1603609 ◽

2016 ◽

Vol 2016 ◽

pp. 1-6 ◽

Cited By ~ 14

Author(s):

Wenjing Liu ◽

Zhaotian Sun ◽

Jinyu Chen ◽

Chuanbo Jing

Keyword(s):

Colorectal Cancer ◽

Raman Spectroscopy ◽

Discriminant Analysis ◽

Diagnostic Accuracy ◽

Raman Spectra ◽

Principal Component ◽

Cancer Diagnostics ◽

Analysis Model ◽

Tissue Samples ◽

Linear Discriminant

Raman spectra of human colorectal tissue samples were employed to diagnose colorectal cancer. High-quality Raman spectra were acquired from normal and cancerous colorectal tissues from 81 patients. Subtle Raman variations, such as for peaks at 1134 cm−1 (protein, C-C/C-N stretching) and 1297 cm−1 (lipid, C-H2 twisting), were observed between normal and cancerous colorectal tissues. The average peak intensity at 1134 and 1297 cm−1 was increased from approximately 235 and 72 in the normal group, respectively, to 315 and 273 in the cancer group. The variations of Raman spectra reflected the changes of cell molecules during canceration. The multivariate statistical methods of principal component analysis-linear discriminant analysis (PCA-LDA) and partial least-squares-discriminant analysis (PLS-DA), together with leave-one-patient-out cross-validation, were employed to build the discrimination model. PCA-LDA was used to evaluate the capability of this approach for classifying colorectal cancer, resulting in a diagnostic accuracy of 79.2%. Further PLS-DA modeling yielded a diagnostic accuracy of 84.3% for colorectal cancer detection. Thus, the PLS-DA model is preferable between the two to discriminate cancerous from normal tissues. Our results demonstrate that Raman spectroscopy can be used with an optimized multivariate data analysis model as a sensitive diagnostic alternative to identify pathological changes in the colon at the molecular level.

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RAMAN SPECTROSCOPIC STUDY ON PREDICTION OF TREATMENT RESPONSE IN CERVICAL CANCERS

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545813500144 ◽

2013 ◽

Vol 06 (02) ◽

pp. 1350014 ◽

Cited By ~ 8

Author(s):

S. RUBINA ◽

M. S. VIDYASAGAR ◽

C. MURALI KRISHNA

Keyword(s):

Raman Spectroscopy ◽

Tumor Response ◽

Locally Advanced ◽

Principal Component ◽

Diagnostic Tools ◽

Spectroscopic Techniques ◽

Predictive Tool ◽

Linear Discriminant ◽

Cervical Cancers

Concurrent chemoradiotherapy (CCRT) is the choice of treatment for locally advanced cervical cancers; however, tumors exhibit diverse response to treatment. Early prediction of tumor response leads to individualizing treatment regimen. Response evaluation criteria in solid tumors (RECIST), the current modality of tumor response assessment, is often subjective and carried out at the first visit after treatment, which is about four months. Hence, there is a need for better predictive tool for radioresponse. Optical spectroscopic techniques, sensitive to molecular alteration, are being pursued as potential diagnostic tools. Present pilot study aims to explore the fiber-optic-based Raman spectroscopy approach in prediction of tumor response to CCRT, before taking up extensive in vivo studies. Ex vivo Raman spectra were acquired from biopsies collected from 11 normal (148 spectra), 16 tumor (201 spectra) and 13 complete response (151 CR spectra), one partial response (8 PR spectra) and one nonresponder (8 NR spectra) subjects. Data was analyzed using principal component linear discriminant analysis (PC-LDA) followed by leave-one-out cross-validation (LOO-CV). Findings suggest that normal tissues can be efficiently classified from both pre- and post-treated tumor biopsies, while there is an overlap between pre- and post-CCRT tumor tissues. Spectra of CR, PR and NR tissues were subjected to principal component analysis (PCA) and a tendency of classification was observed, corroborating previous studies. Thus, this study further supports the feasibility of Raman spectroscopy in prediction of tumor radioresponse and prospective noninvasive in vivo applications.

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Characterization of the COPD Salivary Fingerprint through Surface Enhanced Raman Spectroscopy: A Pilot Study

Diagnostics ◽

10.3390/diagnostics11030508 ◽

2021 ◽

Vol 11 (3) ◽

pp. 508

Author(s):

Cristiano Carlomagno ◽

Alice Gualerzi ◽

Silvia Picciolini ◽

Francesca Rodà ◽

Paolo Innocente Banfi ◽

...

Keyword(s):

Raman Spectroscopy ◽

Pilot Study ◽

Principal Component ◽

Surface Enhanced Raman Spectroscopy ◽

Biological Species ◽

Classification Model ◽

Chronic Obstructive ◽

Linear Discriminant ◽

Surface Enhanced ◽

Surface Enhanced Raman

Chronic Obstructive Pulmonary Disease (COPD) is a debilitating pathology characterized by reduced lung function, breathlessness and rapid and unrelenting decrease in quality of life. The severity rate and the therapy selection are strictly dependent on various parameters verifiable after years of clinical observations, missing a direct biomarker associated with COPD. In this work, we report the methodological application of Surface Enhanced Raman Spectroscopy combined with Multivariate statistics for the analysis of saliva samples collected from 15 patients affected by COPD and 15 related healthy subjects in a pilot study. The comparative Raman analysis allowed to determine a specific signature of the pathological saliva, highlighting differences in determined biological species, already studied and characterized in COPD onset, compared to the Raman signature of healthy samples. The unsupervised principal component analysis and hierarchical clustering revealed a sharp data dispersion between the two experimental groups. Using the linear discriminant analysis, we created a classification model able to discriminate the collected signals with accuracies, specificities, and sensitivities of more than 98%. The results of this preliminary study are promising for further applications of Raman spectroscopy in the COPD clinical field.

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Biochemical Profiles of In Vivo Oral Mucosa by Using a Portable Raman Spectroscopy System

Optics ◽

10.3390/opt2030013 ◽

2021 ◽

Vol 2 (3) ◽

pp. 134-147

Author(s):

Marcelo Saito Nogueira ◽

Victoria Ribeiro ◽

Marianna Pires ◽

Felipe Peralta ◽

Luis Felipe das Chagas e Silva de Carvalho

Keyword(s):

Raman Spectroscopy ◽

Biochemical Characterization ◽

Principal Component ◽

Histopathological Analysis ◽

Label Free ◽

Healthy Individuals ◽

Normal Tissues ◽

The Difference

Most oral injuries are diagnosed by histopathological analysis of invasive and time-consuming biopsies. This analysis and conventional clinical observation cannot identify biochemically altered tissues predisposed to malignancy if no microstructural changes are detectable. With this in mind, detailed biochemical characterization of normal tissues and their differentiation features on healthy individuals is important in order to recognize biomolecular changes associated with early tissue predisposition to malignant transformation. Raman spectroscopy is a label-free method for characterization of tissue structure and specific composition. In this study, we used Raman spectroscopy to characterize the biochemistry of in vivo oral tissues of healthy individuals. We investigated this biochemistry based on the vibrational modes related to Raman spectra of four oral subsites (buccal, gingiva, lip and tongue) of ten volunteers as well as with principal component (PC) loadings for the difference between the four types of oral subsites. Therefore, we determined the biochemical characteristics of each type of healthy oral subsite and those corresponding to differentiation of the four types of subsites. In addition, we developed a spectral reference of oral healthy tissues of individuals in the Brazilian population for future diagnosis of early pathological conditions using real-time, noninvasive and label-free techniques such as Raman spectroscopy.

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P–057 Preliminary evaluation of laser confocal Raman spectroscopy as a noninvasive method for detecting sperm chromosome aneuploidy

Human Reproduction ◽

10.1093/humrep/deab130.056 ◽

2021 ◽

Vol 36 (Supplement_1) ◽

Author(s):

M Li ◽

L Hu ◽

Y Ji

Keyword(s):

Raman Spectroscopy ◽

Roc Curve ◽

Dna Content ◽

Principal Component ◽

Confocal Raman Spectroscopy ◽

Roc Curve Analysis ◽

Standard Principal Component Analysis ◽

Sperm Aneuploidy ◽

Sperm Dna

Abstract Study question To evaluate the efficiency and accuracy of Raman microspectra in detecting sperm chromosome balance state by DNA content difference. Summary answer Raman spectroscopy can identify the difference of X and Y sperm DNA content, but the accuracy still needed to be improved for clinical application. What is known already Aneuploid sperm fertilization affects embryo quality and leads to the waste of oocytes in Assisted Reproductive Technology (ART). Raman spectroscopy can identify substances and observe molecular changes through specific spectral patterns with high specificity and has become a new hot spot in ART. Previous research has used this technology to detect embryo culture medium to evaluate the aneuploidy of embryos. The DNA content of X and Y in sperm was different, which may serve as a marker for sperm aneuploidy detection by Raman spectroscopy. Study design, size, duration The significant difference in the morphology of the sex chromosomes of X and Y spermatozoa leads to a substantial difference in the DNA content. We perform Raman spectroscopy to identify the spectral differences of the sperms, especially the differences in sperm DNA content. We further verified the accuracy with fluorescence in situ hybridization (FISH). Participants/materials, setting, methods Spermatozoa were provided by healthy donors with normal aneuploidy, and analysis parameters met the current World Health Organization (WHO, 2010) standards. Sperm heads were detected by laser confocal Raman spectroscopy and obtained the corresponding spectra. The sperm chromosome information was classified by Standard principal component analysis (PCA) and identified by fluorescence in situ hybridization (FISH). Student’s t-test and Receiver operating characteristic (ROC) curve analysis was performed for further analysis. Main results and the role of chance Standard principal component analysis (PCA) after unqualified quality control divided spermatozoa into two groups according to the calculation and calibration results, 22 cases in group A and 31 cases in group B. Then, we conducted frequency distribution histogram statistics on the above data, and the results showed that there were differences in frequency distribution at I785 = 23,750 and Area714 –1162 = 3,250,000. The FISH analysis identified sex chromosomes of 59 spermatozoa, which was not exactly one-to-one correspondence with the results of PCA analysis. Then we further analyzed the sperm of 59 cases by statistical analysis. The results showed that there were significant differences between X sperm (n = 39) and Y sperm (n = 20) at 714–1162 cm–1 and 785 (P < 0.05). ROC curve analysis was used to evaluate the sensitivity of correlation between sperm DNA content and Raman spectra. The results showed that the corresponding thresholds of I785 = 24,986.5 and Area714–1162 cm–1 = 3,748,990 were the best for distinguishing the two kinds of sperm. When the sperm’s peak value of 785 or 714–1162cm–1 exceeds the above thresholds, X-sperm’s possibility greatly increased. The AUC of the ROC curve in both cases was 0.662 and 0.696, respectively. Limitations, reasons for caution Current Raman spectroscopy requires spermatozoa elution and fixation, which damage the sperms. Furthermore, current Raman spectral data are not obtained from the whole sperm head, limiting the accuracy of this technique. Wider implications of the findings: Our results indicated that Raman spectroscopy had potential application value for sperm aneuploidy detection and could be used as a noninvasive selector for normal haploid sperms in the ART. Trial registration number LL-SC–2018–038

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