Head and neck cancer (HNC) is the sixth most common malignancy worldwide. Squamous cell carcinoma, the primary cause of HNC, evolves from normal epithelium through dysplasia before invading the connective tissue to form a carcinoma. However, less than 18% of suspicious oral lesions progress to cancer, with diagnosis currently relying on histopathological evaluation, which is invasive and time consuming. A non‐invasive, real‐time, point‐of‐care method could overcome these problems and facilitate regular screening. Raman spectroscopy is a non‐invasive optical technique with the ability to extract molecular level information to help determine the functional groups present in a tissue and the molecular conformations of tissue constituents. In the present study, Raman spectroscopy was assessed for its ability to discriminate between normal, dysplastic and HNC. Tissue engineered models of normal, dysplastic and HNC were constructed using normal oral keratinocytes, dysplastic and HNC cell lines, and their biochemical content predicted by interpretation of spectral characteristics. Spectral differences were evident in both the fingerprint (600/cm to 1800/cm) and high wave‐number compartments (2800/cm to 3400/cm). Visible differences were seen in peaks relating to lipid content (2881/cm), protein structure (amide I, amide III), several amino acids and nucleic acids (600/cm to 1003/cm). Multivariate data analysis algorithms successfully identified subtypes of dysplasia and cancer, suggesting that Raman spectroscopy not only has the potential to differentiate between normal, pre‐malignant and cancerous tissue models but could also be sensitive enough to detect subtypes of dysplasia or cancer on the basis of their subcellular differences. Copyright © 2016 John Wiley & Sons, Ltd.
Invasion of melanoma cells from the primary tumor involves interaction with adjacent tissues and extracellular matrix. The extent of this interaction is not fully understood. In this study Raman spectroscopy was applied to cryo-sections of established 3D models of melanoma in human skin. Principal component analysis was used to investigate differences between the tumor and normal tissue and between the peri-tumor area and the normal skin. Two human melanoma cells lines A375SM and C8161 were investigated and compared in 3D melanoma models. Changes were found in protein conformations and tryptophan configurations across the entire melanoma samples, in tyrosine orientation and in more fluid lipid packing only in tumor dense areas, and in increased glycogen content in the peri-tumor areas of melanoma. Raman spectroscopy revealed changes around the perimeter of a melanoma tumor as well as detecting differences between the tumor and the normal tissue.
Abstract:© Versita Sp. z o.o. The quantitative analysis of Tolfenamic Acid (TA) both as a pure compound and in tablet dosage form has been carried out using FT-IR and UV spectroscopy. In the FT-IR method, a number of characteristic absorption peaks were examined that could be used for analytical purpose. The analysis was carried out by preparing calibration curves of peak height/area versus TA content using two points baseline correction with fixed location, and the data was also analyzed through PLS regression model. In the UV method, ethanolic solutions of the drug were analyzed at 288 nm (λ max ) using 480 as the value of A (1%, 1 cm) at the analytical wavelength. The results have been compared statistically for recovery, precision, accuracy and linearity with the British Pharmacopoeial titration method that showed good validity of both test procedures. The two test methods exhibited good recovery of TA with an accuracy of 99.75-100.83% and 99.53-100.11% by FTIR spectrometry for peak height and area respectively and 100.21% for UV method. However, UV spectrometry was found to be more accurate and precise on the basis of statistical evaluation and hence can be employed in the quality control of TA in pharmaceuticals as an alternative to the titration method. Keywords: Tolfenamic acid • FTIR and UV spectrometry • Titration • Quantitative analysis • Clotam
Head and neck cancer (HNC) is the sixth most common malignancy worldwide. Squamous cell carcinoma, the primary cause of HNC, evolves from normal epithelium through dysplasia before invading the connective tissue to form a carcinoma. Only 5% of suspicious lesions progress to cancer and diagnosis currently relies on histopathological evaluation, which is invasive and time consuming. A non-invasive, real-time point-of-care method could overcome these problems and facilitate regular screening. Infrared (IR) and Raman spectroscopy (RS) can non-invasively provide information regarding biochemical differences between normal and abnormal tissues. In this study, RS was employed to distinguish between different tissues-engineered models. 3D tissue engineered models of normal, dysplastic and head and neck squamous cell carcinoma (HNSCC) using normal oral keratinocytes, dysplastic (D19, D20 and DOK) and HNSCC cell lines (Cal27 , SCC4 and FaDu) were constructed and their biochemical content predicted by interpretation of their spectral characteristics. Spectral features of normal tissue samples were mainly attributed to lipids, whereas, malignant tissue samples were observed to be protein dominant. Visible differences were found between the spectra of normal, dysplastic and cancerous models, specifically in the bands of amide I and III. The spectra of HNSCC models showed a broad and strong peak of amide I instead of the sharp and weak lipid peak in normal models at band centred at 1667 cm -1. A shift at 2937 cm -1 was only observed in DOK, differentiating them from the other tissue types. Principal Component Analysis (PCA) and Cluster Analysis (CA) distinguished noticeable differences between tissues.
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