Raman spectroscopy of normal oral buccal mucosa tissues: study on intact and incised biopsies

In vivo, ex vivo and in vitro assessment of buccal permeation of drugs from delivery systems

Introduction: Buccal mucosa has been described as an attractive site for local and systemic drug delivery, owing its accessibility, safety, and excellent blood supply. The absorption of drugs through buccal mucosa has been assessed by in vivo, ex vivo and in vitro permeability studies, using animal and cell-based models with close resemblance to the human buccal mucosa.Areas covered: This paper focuses on the current in vivo, ex vivo and in vitro permeability studies to analyze the absorption of compounds of interest through buccal mucosa, as well as their advantages and limitations in the preclinical studies of the drugs absorption profiles. The techniques for preparation and preservation of the animal buccal tissue are also discussed to evaluate their interference in the integrity and permeability of the tissues.Expert opinion: Overall, the permeability studies have been useful to evaluate the drugs absorption and to clarify the mechanism of transport of drugs across human buccal mucosa, as well as to explain the enhancement of permeability provided by certain dosage forms. Currently, several researchers have demonstrated particular interest in ex vivo permeability studies, due to their effectiveness in the evaluation of drug absorption and low costs in the acquisition of buccal mucosa samples.

Deep convolutional neural networks for tongue squamous cell carcinoma classification using Raman spectroscopy

With deep convolutional neural networks and fiber optic Raman spectroscopy, this study presents a novel classification method that discriminates tongue squamous cell carcinoma (TSCC) from non-tumorous tissue. To achieve this purpose, 24 tissues spectral data were first collected from 12 patients who had undergone a surgical resection due to the tongue squamous cell carcinomas. Then 6 blocks with each block including 1 convolutional layer and 1 max-pooling layer are used to extract the nonlinear feature representations from Raman spectra. The derived features form a representative vector, which is fed into a fully-connected network for performing classification task. Experimental results demonstrated that the proposed method achieved high sensitivity (99.31%) and specificity (94.44%). To show the superiority for the ConvNets classifier, comparison results with the state-of-the-art methods show it had a competitive classification accuracy. Moreover, these promising results may pave the way to apply the deep ConvNets model in the fiber optic Raman instrument for intra-operative evaluation of TSCC resection margins and improve patient survival.

Raman spectroscopic analysis of the molecular composition of oral cavity squamous cell carcinoma and healthy tongue tissue

A Raman tissue spectrum is a quantitative representation of the overall molecular composition of that tissue. Raman spectra are often used as tissue fingerprints without further interpretation of the specific information that they contain about the tissue's molecular composition. In this study, we analyzed the differences in molecular composition between oral cavity squamous cell carcinoma (OCSCC) and healthy tissue structures in tongue, based on their Raman spectra. A total of 1087 histopathologically annotated spectra (142 OCSCC, 202 surface squamous epithelium, 61 muscle, 65 adipose tissue, 581 connective tissue, 26 gland, and 10 nerve) were obtained from Raman maps of 44 tongue samples from 21 patients. A characteristic, average spectrum of each tissue structure was fitted with a set of 55 pure-compound reference spectra, to define the best library of fit-spectra. Reference spectra represented proteins, lipids, nucleic acids, carbohydrates, amino acids and other miscellaneous molecules. A non-negative least-squares algorithm was used for fitting. Individual spectra per histopathological annotation were then fitted with this selected library in order to determine the molecular composition per tissue structure. The spectral contribution per chemical class was calculated. The results show that all characteristic tissue-type spectra could be fitted with a low residual of <4.82%. The content of carbohydrates, proteins and amino acids was the strongest discriminator between OCSCC and healthy tissue. The combination of carbohydrates, proteins and amino acids was used for a classification model of 'tumor' versus 'healthy tissue'. Validation of this model on an independent dataset showed a specificity of 93% at a sensitivity of 100%.

Exploring the effect of vitamin E in cancer chemotherapy-A biochemical and biophysical insight

Many oncologists contend that patient undergoing chemotherapy must avoid antioxidant supplementation as it may interfere with the activity of the drug. In the present investigation, we have explored the influence of vitamin E, a well-known antioxidant on Camptothecin (CPT), a potent anti-cancer drug induced cell apoptosis and death of cervical cancer cells. HeLa cells were treated with different concentrations of CPT in presence and absence of 100 μm vitamin E. Treated cells were subjected to cytotoxicity studies, catalase assay, DNA fragmentation assay, clonogenic assay and flow cytometry based apoptosis detection. Also, Raman spectroscopy a label free technique which provides global information, in conjunction with multivariate tools like PCA, PCLDA and FDA, was investigated to explore vitamin E supplementation induced alterations. Our data based on biochemical and biophysical experimental analysis reveals that CPT causes DNA damage along with protein and lipid alteration culminating in cell death. Importantly, Raman spectroscopic analysis could uniquely differentiate the cluster of control and vitamin E control from CPT and CPT + Vit E treated cells. We conclusively prove that presence of vitamin E at 100 μM concentration shows promising antioxidant activity and displays no modulatory role on CPT induced effect, thereby causing no possible hindrance with the efficacy of the drug. Vitamin E may prove beneficial to alleviate chemotherapy associated side effects in patients during clinical settings which may open the doors further for subsequent exploration in in vivo preclinical studies.

New insights of Raman spectroscopy for oral clinical applications

Oral injuries are currently diagnosed by histopathological analysis of biopsy, which is an invasive procedure and does not give immediate results.

Water Concentration Analysis by Raman Spectroscopy to Determine the Location of the Tumor Border in Oral Cancer Surgery

Adequate resection of oral cavity squamous cell carcinoma (OCSCC) means complete tumor removal with a clear margin of more than 5 mm. For OCSCC, 85% of the surgical resections appear inadequate. Raman spectroscopy is an objective and fast tool that can provide real-time information about the molecular composition of tissue and has the potential to provide an objective and fast intraoperative assessment of the entire resection surface. A previous study demonstrated that OCSCC can be discriminated from healthy surrounding tissue based on the higher water concentration in tumor. In this study, we investigated how the water concentration changes across the tumor border toward the healthy surrounding tissue on freshly excised specimens from the oral cavity. Experiments were performed on tissue sections from 20 patients undergoing surgery for OCSCC. A transition from a high to a lower water concentration, from tumor (76% ± 8% of water) toward healthy surrounding tissue (54% ± 24% of water), takes place over a distance of about 4 to 6 mm across the tumor border. This was accompanied by an increase of the heterogeneity of the water concentration in the surrounding healthy tissue. The water concentration distributions between the regions were significantly different (P < 0.0001). This new finding highlights the potential of Raman spectroscopy for objective intraoperative assessment of the resection margins. Cancer Res; 76(20); 5945-53. ©2016 AACR.

Development and validation of Raman spectroscopic classification models to discriminate tongue squamous cell carcinoma from non-tumorous tissue

BACKGROUND

Currently, up to 85% of the oral resection specimens have inadequate resection margins, of which the majority is located in the deeper soft tissue layers. The prognosis of patients with oral cavity squamous cell carcinoma (OCSCC) of the tongue is negatively affected by these inadequate surgical resections. Raman spectroscopy, an optical technique, can potentially be used for intra-operative evaluation of resection margins.

OBJECTIVE

To develop in vitro Raman spectroscopy-based tissue classification models that discriminate OCSCC of the tongue from (subepithelial) non-tumorous tissue.

MATERIALS AND METHODS

Tissue classification models were developed using Principal Components Analysis (PCA) followed by (hierarchical) Linear Discriminant Analysis ((h)LDA). The models were based on a training set of 720 histopathologically annotated Raman spectra, obtained from 25 tongue samples (11 OCSCC and 14 normal) of 10 patients, and were validated by means of an independent validation set of 367 spectra, obtained from 19 tongue samples (6 OCSCC and 13 normal) of 11 patients.

RESULTS

A PCA-LDA tissue classification model 'tumor' versus 'non-tumorous tissue' (i.e. surface squamous epithelium, connective tissue, muscle, adipose tissue, gland and nerve) showed an accuracy of 86% (sensitivity: 100%, specificity: 66%). A two-step PCA-hLDA tissue classification model 'tumor' versus 'non-tumorous tissue' showed an accuracy of 91% (sensitivity: 100%, specificity: 78%).

CONCLUSION

An accurate PCA-hLDA Raman spectroscopy-based tissue classification model for discrimination between OCSCC and (especially the subepithelial) non-tumorous tongue tissue was developed and validated. This model with high sensitivity and specificity may prove to be very helpful to detect tumor in the resection margins.

SERS and integrative imaging upon internalization of quantum dots into human oral epithelial cells

CdTe quantum dots (QDs) are widely used in bio-applications due to their size and highly efficient optical properties. However internalization mechanisms thereof for the variety of freshly extracted, not cultivated human cells and their specific molecular interactions remains an open topic for discussion. In this study, we assess the internalization mechanism of CdTe quantum dots (3.3 nm) capped with thioglycolic acid using non cultivated oral epithelial cells obtained from healthy donors. Naked gold nanoparticles (40 nm) were successfully used as nanosensors for surface-enhanced Raman spectroscopy to efficiently identify characteristic Raman peaks, providing new evidence indicating that the first interactions of these QDs with epithelial cells occurred preferentially with aromatic rings and amine groups of amino acid residues and glycans from trans-membrane proteins and cytoskeleton. Using an integrative combination of advanced imaging techniques, including ultra-high resolution SEM, high resolution STEM coupled with EDX spectroscopy together with the results obtained by Raman spectroscopy, it was determined that thioglycolic acid capped CdTe QDs are efficiently internalized into freshly extracted oral epithelial cells only by facilitated diffusion, distributed into cytoplasm and even within the cell nucleus in three minutes.

Investigation of the potential of Raman spectroscopy for oral cancer detection in surgical margins

The poor prognosis of oral cavity squamous cell carcinoma (OCSCC) patients is associated with residual tumor after surgery. Raman spectroscopy has the potential to provide an objective intra-operative evaluation of the surgical margins. Our aim was to understand the discriminatory basis of Raman spectroscopy at a histological level. In total, 127 pseudo-color Raman images were generated from unstained thin tissue sections of 25 samples (11 OCSCC and 14 healthy) of 10 patients. These images were clearly linked to the histopathological evaluation of the same sections after hematoxylin and eosin-staining. In this way, Raman spectra were annotated as OCSCC or as a surrounding healthy tissue structure (i.e., squamous epithelium, connective tissue (CT), adipose tissue, muscle, gland, or nerve). These annotated spectra were used as input for linear discriminant analysis (LDA) models to discriminate between OCSCC spectra and healthy tissue spectra. A database was acquired with 88 spectra of OCSCC and 632 spectra of healthy tissue. The LDA models could distinguish OCSCC spectra from the spectra of adipose tissue, nerve, muscle, gland, CT, and squamous epithelium in 100%, 100%, 97%, 94%, 93%, and 75% of the cases, respectively. More specifically, the structures that were most often confused with OCSCC were dysplastic epithelium, basal layers of epithelium, inflammation- and capillary-rich CT, and connective and glandular tissue close to OCSCC. Our study shows how well Raman spectroscopy enables discrimination between OCSCC and surrounding healthy tissue structures. This knowledge supports the development of robust and reliable classification algorithms for future implementation of Raman spectroscopy in clinical practice.

Current concepts and future of noninvasive procedures for diagnosing oral squamous cell carcinoma--a systematic review

Background

Oral squamous cell carcinoma (OSCC) has a remarkably high incidence worldwide, and a fairly serious prognosis, encouraging further research into advanced technologies for noninvasive methods of making early diagnoses, ideally in primary care settings.

Objectives

Our purpose was to examine the validity of using advanced noninvasive technologies in diagnosis of OSCC by identifying and evaluating relevant published reports.

Data source

MEDLINE, EMBASE, and CINAHL were searched to identify clinical trials and other information published between 1990 and 10 June 2014; the searches of MEDLINE and EMBASE were updated to November 2014. Study selection: Studies of noninvasive methods of diagnosing OSCC, including oral brush biopsy, optical biopsy, saliva-based oral cancer diagnosis, and others were included.

Data extraction

Data were abstracted and evaluated in duplicate for possible relevance on two occasions at an interval of 2 months before being included or excluded.

Data synthesis

This study identified 163 studies of noninvasive methods for diagnosing OSCC that met the inclusion criteria. These included six studies of oral brush biopsy, 42 of saliva-based oral diagnosis, and 115 of optical biopsy. Sixty nine of these studies were assessed by the modified version of the QUADAS instrument. Saliva-based oral cancer diagnosis and optical biopsy were found to be promising noninvasive methods for diagnosing OSCC.

Limitation

The strength of evidence was rated low for accuracy outcomes because the studies did not report important details required to assess the risk for bias.

Conclusions

It is clear that screening for and early detection of cancer and pre-cancerous lesions have the potential to reduce the morbidity and mortality of this disease. Advances in technologies for saliva-based oral diagnosis and optical biopsy are promising pathways for the future development of more effective noninvasive methods for diagnosing OSCC that are easy to perform clinically in primary care settings.

In vivo Raman spectroscopy for detection of oral neoplasia: a pilot clinical study

We report a pilot study carried out to evaluate the applicability of in vivo Raman spectroscopy for differential diagnosis of malignant and potentially malignant lesions of human oral cavity in a clinical setting. The study involved 28 healthy volunteers and 171 patients having various lesions of oral cavity. The Raman spectra, measured from multiple sites of normal oral mucosa and of lesions belonging to three histopathological categories, viz. oral squamous cell carcinoma (OSCC), oral submucous fibrosis (OSMF) and leukoplakia (OLK), were subjected to a probability based multivariate statistical algorithm capable of direct multi-class classification. With respect to histology as the gold standard, the diagnostic algorithm was found to provide an accuracy of 85%, 89%, 85% and 82% in classifying the oral tissue spectra into the four tissue categories based on leave-one-subject-out cross validation. When employed for binary classification, the algorithm resulted in a sensitivity and specificity of 94% in discriminating normal from the rest of the abnormal spectra of OSCC, OSMF and OLK tissue sites pooled together.

In vivo Raman spectroscopy of oral buccal mucosa: a study on malignancy associated changes (MAC)/cancer field effects (CFE)

Occurrence of metachronous and synchronous secondary tumors in oral cavities has been associated with poor prognosis and decreased 5-year disease-free survival rates. The origin of secondary tumors in the oral cavity has been primarily attributed to cancer field effects (CFE) or malignancy-associated changes (MAC) in uninvolved areas. Classification of normal, cancerous and pre-cancerous oral lesions by in vivo Raman spectroscopy (RS) has already been demonstrated. In the present study, MAC/CFE in oral buccal mucosa were explored. In vivo Raman spectra from 84 subjects (722 spectra) under five categories - cancer and contralateral normal (opposite side of tumor), healthy controls (no tobacco habit, no cancer), habitués healthy controls (tobacco habit, no cancer) and non-habitués contralateral normal (no tobacco habit with cancer) were acquired. Mean and difference spectra suggest that loss of lipids and additional features representing proteins and DNA are characteristics of all pathological conditions, with respect to healthy controls. Spectral data were analyzed by PC-LDA followed by leave-one-out cross-validation. Results suggest that Raman characteristics of mucosa of healthy controls are exclusive, while those of habitués healthy controls are similar to those of contralateral normal mucosa. It was observed that the cluster of non-habitués contralateral normal mucosa is different from habitués healthy controls, suggesting that malignancy associated changes can be identified and also indicating that transformation of uninvolved oral mucosa due to tobacco habit or malignancy is different. The findings of the study demonstrate the potential of RS in identifying early transformation changes in oral mucosa and the efficacy of this approach in oral cancer applications.

Real-time Raman spectroscopy for in vivo, online gastric cancer diagnosis during clinical endoscopic examination

Optical spectroscopic techniques including reflectance, fluorescence and Raman spectroscopy have shown promising potential for in vivo precancer and cancer diagnostics in a variety of organs. However, data-analysis has mostly been limited to post-processing and off-line algorithm development. In this work, we develop a fully automated on-line Raman spectral diagnostics framework integrated with a multimodal image-guided Raman technique for real-time in vivo cancer detection at endoscopy. A total of 2748 in vivo gastric tissue spectra (2465 normal and 283 cancer) were acquired from 305 patients recruited to construct a spectral database for diagnostic algorithms development. The novel diagnostic scheme developed implements on-line preprocessing, outlier detection based on principal component analysis statistics (i.e., Hotelling's T2 and Q-residuals) for tissue Raman spectra verification as well as for organ specific probabilistic diagnostics using different diagnostic algorithms. Free-running optical diagnosis and processing time of < 0.5 s can be achieved, which is critical to realizing real-time in vivo tissue diagnostics during clinical endoscopic examination. The optimized partial least squares-discriminant analysis (PLS-DA) models based on the randomly resampled training database (80% for learning and 20% for testing) provide the diagnostic accuracy of 85.6% [95% confidence interval (CI): 82.9% to 88.2%] [sensitivity of 80.5% (95% CI: 71.4% to 89.6%) and specificity of 86.2% (95% CI: 83.6% to 88.7%)] for the detection of gastric cancer. The PLS-DA algorithms are further applied prospectively on 10 gastric patients at gastroscopy, achieving the predictive accuracy of 80.0% (60/75) [sensitivity of 90.0% (27/30) and specificity of 73.3% (33/45)] for in vivo diagnosis of gastric cancer. The receiver operating characteristics curves further confirmed the efficacy of Raman endoscopy together with PLS-DA algorithms for in vivo prospective diagnosis of gastric cancer. This work successfully moves biomedical Raman spectroscopic technique into real-time, on-line clinical cancer diagnosis, especially in routine endoscopic diagnostic applications.

Fourier transform infrared photoacoustic spectroscopy study of physicochemical interaction between human dentin and etch-&-rinse adhesives in a simulated moist bond technique

The purpose of this study was to provide the physicochemical interactions at the interfaces between two commercial etch-&-rinse adhesives and human dentin in a simulated moist bond technique. Six dentin specimens were divided into two groups (n=3) according to the use of two different adhesive systems: (a) 2-hydroxyethylmethacrylate (HEMA) and 4-methacryloxyethyl trimellitate anhydrate (4-META), and (b) HEMA. The Fourier transform infrared photoacoustic spectroscopy was performed before and after dentin treatment with 37% phosphoric acid, with adhesive systems and also for the adhesive systems alone. Acid-conditioning resulted in a decalcification pattern. Adhesive treated spectra subtraction suggested the occurrence of chemical bonding to dentin expressed through modifications of the OH stretching peak (3340 cm(-1)) and symmetric CH stretching (2900 cm(-1)) for both adhesives spectra; a decrease of orthophosphate absorption band (1040 to 970 cm(-1)) for adhesive A and a better resolved complex band formation (1270 to 970 cm(-1)) for adhesive B were observed. These results suggested the occurrence of chemical bonding between sound human dentin and etch-&-rinse adhesives through a clinical typical condition.