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Objective:The objective of this study was to assess the surface properties (microhardness and wear resistance) of various composites and compomer materials. In addition, the methodologies used for assessing wear resistance were compared.Materials and Methods:This study was conducted using restorative material (Filtek Z250, Filtek Z350, QuiXfil, SureFil SDR, and Dyract XP) to assess wear resistance. A custom-made toothbrush simulator was employed for wear testing. Before and after wear resistance, structural, surface, and physical properties were assessed using various techniques.Results:Structural changes and mass loss were observed after treatment, whereas no significant difference in terms of microhardness was observed. The correlation between atomic force microscopy (AFM) and profilometer and between wear resistance and filler volume was highly significant. The correlation between wear resistance and microhardness were insignificant.Conclusions:The AFM presented higher precision compared to optical profilometers at a nanoscale level, but both methods can be used in tandem for a more detailed and precise roughness analysis.

We demonstrate that wax-embedded models of tissue-engineered oral mucosa can be effectively dewaxed using xylene for Raman spectroscopic analysis. Tissue sections of 20 μm thickness were cut and mounted onto glass slides. Sections were placed in xylene for increasing lengths from 2 to 45 min. Acquired Raman spectra revealed the wax contribution in the fingerprint region until 28-min treatment in xylene. Good quality and wax-free spectra were recorded at 30, 35, 40, and 45 min in xylene with no significant differences among them. It is essential to collect uncontaminated Raman spectra in order to achieve authentic results because the fingerprint region of biological tissues holds extremely vital information that is of diagnostic significance in cancer.

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.

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.