4 research outputs found

    Evaluation of non-cross linked xeno-free hyaluronic acid solutions as visco elastic biomaterials

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    Background: Hyaluronic acid (HA) is a naturally occurring biodegradable, high molecular weight, non-sulfated glycosaminoglycan (GAG) polymer known for its excellent biocompatibility. HA-based products are widely used as viscosupplements, dermal fillers, and ophthalmic lubricants in clinical settings. Although animal and bacterial-derived HA are commonly reported, plant-sourced HA is not frequently reported. In this study, we have evaluated various viscoelastic properties of one such plant-based HA solution and propose them as an alternative to existing animal/bacteria-sourced HA. Materials and Methods: The viscoelastic properties of plant-sourced HA solution of various concentrations (0.1%, 0.5%, 1%, and 2% in PBS) were studied using a rheometer at 37°C. Flow curves, amplitude sweep studies, and frequency sweep studies were performed and compared for all HA solutions. Results: The HA solutions displayed shear-thinning behavior, which is an important characteristic of an injectable biomaterial. The 0.1 and 0.5% HA were found to have viscoelastic properties appropriate for eye lubricants, while 1 and 2% HA solutions showed properties suitable for soft tissue fillers. Frequency sweep studies indicated that all the samples are typically viscoelastic liquids with a loss modulus (G″) higher than the storage modulus (G′). This indicated that the samples needed further processing like crosslinking of HA or using higher molecular weight HA to be suitable as viscosupplements. However, the frequency sweep studies also indicated that these solutions can be used as soft tissue fillers of any type based on the G′ and tan δ values. Conclusion: The plant-sourced HA solutions are found to exhibit good shear-thinning properties with viscoelastic properties suitable for eye lubricants and soft tissue fillers. However, to be used as viscosupplements, the viscoelastic properties of HA solutions have to be further modified through non-toxic crosslinking strategies, and hydrophobic derivatives as well as by using high molecular weight HAs

    Nifedipine-induced gingival enlargement: Correlation with dose and oral hygiene

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    Gingival enlargement is a common finding in a routine dental practice. Though it has many etiological factors, enlargement due to drugs which are administered to treat the systemic problems is of major concern which needs attention. Here, we present a case of nifedipine-induced gingival enlargement and discuss the reason for the enlargement

    In vitro cytotoxicity evaluation of flowable hyaluronic acid–acellular stromal vascular fraction (HA–aSVF) mixture for tissue engineering applications

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    Background: The stromal vascular fraction (SVF) is an aqueous fraction isolated from the adipose tissue that constitutes different kinds of cells and extracellular matrix components. Hyaluronic acid (HA) is a linear polysaccharide in vertebrate tissues and is considered a potential tissue engineering scaffold due to its biocompatible nature. In this study, we have evaluated the cytotoxicity of xenofree HA in combination with an acellular component of adipose SVF (HA–aSVF) to propose it as a candidate biomaterial for future applications. Materials and Methods: 3-(4,5-dimethyl thiazolyl-2)-2,5-diphenyltetrazolium bromide assay of L-929 cells treated with HA–aSVF was used in our study. Data were normalized to cell control (untreated) and extracts of copper and ultra-high molecular weight polyethylene were used as positive (PC) and negative controls (NC). Results: Fibroblast cells retained the morphology after 24 h of treatment with HA–aSVF mixture and exhibited a similar percentage of cell activity compared to NC. PC showed a positive cytotoxic response as expected. The cells incubated with HA–aSVF showed a linear increase in cell activity indicating proliferation. Conclusion: The mixture of HA and acellular SVF in its flowable form is non-cytotoxic and showed improved cell proliferation. Hence the mixture can be proposed as a biomaterial and can be further explored for specific tissue engineering applications

    A smart tele-cytology point-of-care platform for oral cancer screening.

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    Early detection of oral cancer necessitates a minimally invasive, tissue-specific diagnostic tool that facilitates screening/surveillance. Brush biopsy, though minimally invasive, demands skilled cyto-pathologist expertise. In this study, we explored the clinical utility/efficacy of a tele-cytology system in combination with Artificial Neural Network (ANN) based risk-stratification model for early detection of oral potentially malignant (OPML)/malignant lesion. A portable, automated tablet-based tele-cytology platform capable of digitization of cytology slides was evaluated for its efficacy in the detection of OPML/malignant lesions (n = 82) in comparison with conventional cytology and histology. Then, an image pre-processing algorithm was established to segregate cells, ANN was trained with images (n = 11,981) and a risk-stratification model developed. The specificity, sensitivity and accuracy of platform/ stratification model were computed, and agreement was examined using Kappa statistics. The tele-cytology platform, Cellscope, showed an overall accuracy of 84-86% with no difference between tele-cytology and conventional cytology in detection of oral lesions (kappa, 0.67-0.72). However, OPML could be detected with low sensitivity (18%) in accordance with the limitations of conventional cytology. The integration of image processing and development of an ANN-based risk stratification model improved the detection sensitivity of malignant lesions (93%) and high grade OPML (73%), thereby increasing the overall accuracy by 30%. Tele-cytology integrated with the risk stratification model, a novel strategy established in this study, can be an invaluable Point-of-Care (PoC) tool for early detection/screening in oral cancer. This study hence establishes the applicability of tele-cytology for accurate, remote diagnosis and use of automated ANN-based analysis in improving its efficacy
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