In vitro comparative evaluation of antioxidative effect of selenium alone and in combination with green tea and alpha-tocopherol on the shear bond strength of universal composite resin to enamel after in-office bleaching.

Background: Antioxidant application soon after bleaching process increases the shear bond strength (SBS) of composite resin to enamel. Aims: The aim of the study was to evaluate the antioxidant effects of selenium alone and in combination with alpha-tocopherol (αT) and green tea (GT) on SBS of composite resin to enamel following in-office bleaching with 38% hydrogen peroxide (HP). Methods: Sixty extracted human single -rooted premolar teeth were cleaned and embedded in acrylic resin blocks at the level of cementoenamel junction(CEJ) followed by bleaching with 38% hydrogen peroxide (HP) and arbitrarily divided into seven groups (n=10) for antioxidant application: Group I (negative control): intact teeth, Group II (positive control): only bleaching, Group III: 10% selenium (Se), Group IV: 10% alpha tocopherol (αT), Group V: 10% αT +10% Se, Group VI: 10% Green tea (GT), Group VII: 10%GT+10% Se. In all groups, self-etch adhesive was applied and composite restoration was done, and specimens were stored in distilled water for 24h followed by SBS evaluation. Statistical Analysis: One-way analysis of variance and post hoc Tukey's tests were used (P < 0.05). Results: The highest SBS was found in negative control Group I (intact teeth) and least in positive control Group II (bleached teeth), whereas in experimental groups, Group VII (GT + Se) showed highest followed by Groups V (αT + Se), III (Se), and VI (GT) and least in Group IV (αT). Conclusion: Combination of selenium with green tea and alpha tocopherol enhanced the SBS of composite resin following in-office bleaching.

Evaluation of Antidiabetic Effect of Luteolin in STZ Induced Diabetic Rats: Molecular Docking, Molecular Dynamics, In Vitro and In Vivo Studies.

Despite the existence of modern antidiabetic medications, diabetes still affects millions of individuals worldwide, with a high death and disability rate. There has been a concerted search for alternative natural medicinal agents; luteolin (LUT), a polyphenolic molecule, might be a good choice, both because of its efficacy and because of it having fewer side effects, compared to conventional medicines. This study aims to explore the antidiabetic potential of LUT in diabetic rats, induced by streptozotocin (STZ; 50 mg/kg b.w.), intraperitoneally. The level of blood glucose, oral glucose tolerance test (OGTT), body weight, glycated hemoglobin A1c (HbA1c), lipidemic status, antioxidant enzymes, and cytokines were assessed. Also, its action mechanism was explored through molecular docking and molecular dynamics simulations. Oral supplementation of LUT for 21 days resulted in a significant decrease in the blood glucose, oxidative stress, and proinflammatory cytokine levels, and modulated the hyperlipidemia profile. LUT also ameliorated the tested biomarkers of liver and kidney function. In addition, LUT markedly reversed the damage to the pancreas, liver, and kidney cells. Moreover, molecular docking and molecular dynamics simulations revealed excellent antidiabetic behavior of LUT. In conclusion, the current investigation revealed that LUT possesses antidiabetic activity, through the reversing of hyperlipidemia, oxidative stress, and proinflammatory status in diabetic groups. Therefore, LUT might be a good remedy for the management or treatment of diabetes.

Deep Neural Network for EEG Signal-Based Subject-Independent Imaginary Mental Task Classification.

BACKGROUND: Mental task identification using electroencephalography (EEG) signals is required for patients with limited or no motor movements. A subject-independent mental task classification framework can be applied to identify the mental task of a subject with no available training statistics. Deep learning frameworks are popular among researchers for analyzing both spatial and time series data, making them well-suited for classifying EEG signals. METHOD: In this paper, a deep neural network model is proposed for mental task classification for an imagined task from EEG signal data. Pre-computed features of EEG signals were obtained after raw EEG signals acquired from the subjects were spatially filtered by applying the Laplacian surface. To handle high-dimensional data, principal component analysis (PCA) was performed which helps in the extraction of most discriminating features from input vectors. RESULT: The proposed model is non-invasive and aims to extract mental task-specific features from EEG data acquired from a particular subject. The training was performed on the average combined Power Spectrum Density (PSD) values of all but one subject. The performance of the proposed model based on a deep neural network (DNN) was evaluated using a benchmark dataset. We achieved 77.62% accuracy. CONCLUSION: The performance and comparison analysis with the related existing works validated that the proposed cross-subject classification framework outperforms the state-of-the-art algorithm in terms of performing an accurate mental task from EEG signals.

OdorTAM: Technology Acceptance Model for Biometric Authentication System Using Human Body Odor.

Body odor is a biometric feature unique to each individual, and it can be used for authentication. However, decision makers must learn about the users' level of acceptance of this technology, as well as their thoughts on the system's features and procedures. In this study, a technology acceptance model (TAM) for body-odor-based biometric techniques named OdorTAM was proposed and validated. An English language questionnaire was developed in a web-based, easy-to-read format on Google Forms. The survey consisted of 19 questions, and 150 responses were received. Statistical analysis of the responses was carried out, and it was found that all the hypotheses were supported. Therefore, the OdorTAM model appears to be satisfactory. To this end, we posit that a body-odor-based biometric technique can be one of the alternatives for authentication, and it can also be used along with some other techniques for improved security. The study contributes to the literature on consumers' understanding of biometric technologies, in particular odor detection, which has received relatively less attention in extant research.

Recent developments made in the assessment of the antidiabetic potential of gymnema species - From 2016 to 2020.

BACKGROUND: In traditional herbal medicine, the Gymnema species has been well known for various therapeutic activities such as anti-diabetic, anti-inflammatory, anti-bacterial, anti-arthritic, anti-hyperlipidemic, cytotoxic, and immunostimulatory activities. This review is an effort to analyse all the recent studies done to explore the anti-diabetic potential of traditional Gymnema species. Gymnema sylvestre (Retz.) R.Br. ex Sm. is an important member of the Apocynaceae family that has been used to treat a variety of diseases, the most studied of which is diabetes. This action is mostly due to the pharmacologically active phytoconstituents present in its extract, which include gymnemic acids, triterpenoid saponin glycosides, and so on. Numerous other Gymnema species have also demonstrated a similar pharmacological action. INTRODUCTION: The goal of this study is to give a critical overview of the available data on Gymnema species that are used to treat diabetes. The major goal of this study is to give up-to-date knowledge on ethnopharmacology, botany, pharmacology, and structure-activity relationships of Gymnemaspecies from 2016 to 2020, as well as potential future research. The potential of using medicinal plants for alleviating symptoms of diabetes is recently being recognized. This review aims to summarize the available data and highlight both the potential and shortcomings of using Gymnema therapeutically. This knowledge can further be used to develop more therapeutically effective drugs derived from Gymnema. MATERIALS AND METHODS: Data for Gymnema species was obtained using a mix of several search terms from online databases such as PubMed, SCOPUS, and Europe PMC. Other literature surveys relevant to traditional knowledge, phytochemistry, pharmacology, or structure-activity relationship activity were also used as reference. Several methods by which Gymnema species extracts exert their effects have been investigated, and a summary of the newly discovered chemicals isolated from the plant in the previous five years has been provided. RESULTS: SAR based evaluation has been carried out for a total of 27 pharmacologically active compounds belonging to three species of Gymnema genus (Gymnema sylvestre, Gymnema latifolium, and Gymnema inodorum).These compounds demonstrated the critical significance of plant medicines for diabetes management. Numerous heterocyclic compounds have anti-diabetic action and may serve as a starting point for the design and identification of new diabetes inhibitors. CONCLUSIONS: This study aims to provide researchers with a better understanding of the antidiabetic potential Gymnema species, as well as an outline of prospective future developments. It was concluded after studying the evaluation done in the last 5 years that although extracts of Gymnema have shown good antidiabetic potential, further modifications in the structures could result in the development of more potent and safer compounds.