Automated Brain Tumor Detection from MRI Scans using Deep Convolutional Neural Networks

The brain, as the central nervous system's most critical part, can develop abnormal growths of cells known as tumors. Cancer is the term used to describe malignant tumors. Medical imaging modalities, such as computed tomography (CT) or magnetic resonance imaging (MRI), are commonly used to detect cancerous regions in the brain. Other techniques, such as positron emission tomography (PET), cerebral arteriography, lumbar puncture, and molecular testing, are also utilized for brain tumor detection. MRI scans provide detailed information concerning delicate tissue, which aids in diagnosing brain tumors. MRI scan images are analyzed to assess the disease condition objectively. The proposed system aims to identify abnormal brain images from MRI scans accurately. The segmented mask can estimate the tumor's density, which is helpful in therapy. Deep learning techniques are employed to automatically extract features and detect abnormalities from MRI images. The proposed system utilizes a convolutional neural network (CNN), a popular deep learning technique, to analyze MRI images and identify abnormal brain scans with high accuracy. The system's training process involves feeding the CNN with large datasets of normal and abnormal MRI images to learn how to differentiate between the two. During testing, the system classifies MRI images as either normal or abnormal based on the learned features. The system's ability to accurately identify abnormal brain scans can aid medical practitioners in making informed decisions and providing better patient care. Additionally, the system's ability to estimate tumor density from the segmented mask provides additional information to guide therapy. The proposed system offers a promising solution for improving the accuracy and efficiency of brain tumor detection from MRI images, which is critical for early detection and treatment

Clinical Evaluation of Lateral Pedicle Graft Stabilized with Cyanoacrylate and Resorbable Sutures: A Randomized Controlled Trial

BACKGROUND : Gingival recession is the most common mucogingival deformity and are more likely to develop root sensitivity and root caries and pose esthetic problems. Laterally positioned pedicle graft is used to cover denuded roots that have adequate donor tissue laterally and adequate vestibular depth. A carefully planned surgery needs proper immobilization of grafted area and this can be achieved by proper wound closure technique with appropriate material such as sutures or tissue adhesives. AIM : The purpose of the present study was to evaluate the clinical outcomes of lateral pedicle graft stabilized with cyanoacrylate and resorbable sutures. MATERIALS AND METHODS : Twenty-two patients with miller’s class I and class II gingival recession were divided into two groups: Lateral pedicle graft stabilized with cyanoacrylate tissue adhesive (test) and lateral pedicle graft stabilized with resorbable sutures (control). Plaque index, gingival index, probing pocket depth, clinical attachment level, recession depth and width, and height and thickness of keratinized gingiva were evaluated at baseline, 1st month and 3rd month post-operatively. The percentage of root coverage was evaluated at the end of 3rd month post-operatively. RESULTS : The mean plaque index and gingival index at the first month and third month were found to be statistically significant and did not present any significant influence over other clinical parameters evaluated. A partial root coverage was observed in both the groups (71.97% for test group and 61.36% for control group) CONCLUSION : Cyanoacrylate tissue adhesive is clinically effective in stabilization of lateral pedicle flap and can be used as an excellent alternative to resorbable sutures

Analysis, Optimization and Molecular Characterization of PHB Positive Bacteria Isolated from Agricultural Soil Sample

Polyhydroxybutyrates are the widely studied biopolymer because of its biodegradability and non-toxic effect in the environment. This work focused on the isolation, screening, characterization and optimization of the PHB producing bacteria from agricultural soil. About eighty percentage of the isolated bacteria were identified as PHB producing bacteria after Sudan Black Blue staining. Among them three colonies were randomly selected and their morphologically and biochemically characterized. The biopolymer was extracted using sodium hypochlorite method. Potent PHB producer (WJSP1) (13.5µg/ml) was identified by crotonic acid assay. The functional groups of the produced PHB were identified by Fourier Transform Infra-Red spectroscopy (FTIR). The presence of functional groups like carbon group, C=O ester group, CH group, carbonyl group, carboxyl group which was corelated with the peak values1454.33cm-1, 1724.36 cm-1, 1274.95 cm-1, 1049.28 cm-1, 1280-1053 cm-1 indicates the presence of PHB. The potent producer (WJSP1) was optimized under different conditions to achieve the best condition for the production of PHB. The maximum production was obtained at the temperature - 37˚C, the carbon source - glucose, pH - 7 and at a time period of 72 hours. 16SrRNA sequencing was performed and the organism was identified as Bacillus cereus from the BLST (Basic Local Alignment Search Tool). A phylogenetic tree was constructed and the sequence was submitted in GENBANK (Accession No: OR192860). The isolated bacteria Bacillus cereus is an efficient producer of PHB under laboratory condition in presence of glucose as a chief carbon source. Large scale production of PHB can be done in large scale fermenters in industries. Large scale production of PHB will help the environment to get rid of the petroleum-based bioplastics

ANTIOXIDANT AND ANTIMICROBIAL ACTIVITY OF SELECTED MEDICINAL PLANTS AGAINST HUMAN ORAL PATHOGENS

Objective: The aim of the study was focused on determining the phytochemicals, antibacterial, antiadherence, antifungal and antioxidant activities of Glycyrrhiza glabra, Matricaria chamomilla and Eclipta alba and also their mechanism of action towards human oral pathogens.Methods: Qualitative analysis and quantitative estimation of phenols and flavonoids were performed in methanolic extracts. Antibacterial, anti adherence, antifungal assays were performed by plate assays. Antioxidant assays were done by ABTS and DPPH methods. SEM, TEM and flow cytometry analysis were executed to find out the mechanism of action of plant extract.Results:  The total phenol contents were 0.85, 1.24, 0.64 GAE/g and the total flavonoid contents were 356, 231.34 and 88 µg QE/mg for G. glabra, M. chamomilla and E. alba respectively.Matricaria chamomilla possesses highest antioxidant activity (DPPH and ABTS assays) among the all extracts tested. E. alba showed a highest zone of inhibition against S. aureus (21.6 mm) whereas G. glabra and M. chamomilla revealed the better result of 21 mm and 19.8 mm respectively against S. mutans. Glycyrrhiza glabra showed antifungal activity against Candida parapsilosis whereas Pseudomonas aeruginosa, Candida krusei, Candida tropicalis and Candida albicans showed resistance towards all the extracts tested. The MIC, MBC and antiadherence tests were also performed. Sorbitol assay confirmed that G. glabra has no impact on the fungal cell wall. To confirm the mode of action SEM, TEM and flow cytometric analysis were performed which showed the cell elongation and damage in cytoplasmic membrane resulting in oozing of cellular constituents. Conclusion: This work concluded that all the plant extracts showed potent activities among the various tests. Oral care product can be developed if the active constituents responsible for the activities were analysed.Â

SiRNA Mediated Gene Silencing: a Mini Review

- RNA interference (RNAi) technology has become a novel tool for silencing gene expression in cells or organisms. RNA interference is the process that double-stranded RNA induces the homology-dependent degradation of cognate mRNA mediated by 21-23 nucleotide short interfering RNA (siRNA). RNA interference is a powerful mechanism of gene silencing that underlies many aspects of eukaryotic biology. On the molecular level, RNAi is mediated by a family of ribonucleoprotein (RNP) complexes called RNA-Induced Silencing Complexes (RISCs), which can be programmed to target virtually any nucleic acid sequence for silencing. The ability of RISC to locate target RNAs been co-opted by evolution many times to generate a broad spectrum of gene silencing pathways. The study about the Silencing of gene expression by siRNA is rapidly becoming a powerful tool for genetic analysis and represents a potential strategy for therapeutic product development. In this study, the applications of siRNA expressing recombinant adenovirus system in plants, animals and in cancer gene therapy are given importance with its modification

Evaluation of Geometrical Nonlinear Behaviour of FRP Composite Plate Using Finite Element Method

The work presents the prediction of nonlinear behavior of a square plate made of composite material under uniform transvers pressure using 3-D finite element analysis. Transverse deflection, Normal stresses and Shear stresses are evaluated for different values of load and by varying the number of layers. The effect of loading and variation of stresses in both the analyses for different lay-up of composite laminated plate are determined. The study also includes the effect of the layer thickness for different lay-up responses of laminate under the clamped boundary conditions. The commercial Finite element analysis software ANSYS has been successfully executed for both linear Static analysis and geometric nonlinear static analysis

Estimation of morphological and molecular genetic diversity in blackgram [Vigna mungo (L.) Hepper] under YMV hotspot regime

A phenotypic and molecular diversity study was conducted using seven traits and 19 SSR markers in a collection of 26 black gram genotypes. Phenotypic characterization was based on seven yield and yield related variable. The  field experiment  was  laid  out  at  Panboli village (YMV hotspot)  of Tirunelveli District in Tamilnadu during summer 2017. Genetic divergence was estimated on the basis of D2 values and 26 genotypes under study were grouped into six clusters by Tocher’s method. Seed yield per plant followed by Plant height and number of pods per plant contributed to the genetic divergence. The genetic distance announced using DICE dissimilarity co-efficient indicated highest divergence of 1.0 between VBN 8 and AUBG 17 and between VBN 8 and AUBG 19. The dendogram constructed using the DICE dissimilarity co-efficient between genotypes showed four apparent clusters based on marker allele distribution. Divergence was noted between the dissimilarity matrices based on the molecular and phenotypic diversity based on agronomic data.&nbsp

A Hankel Matrix Based Reduced Order Model for Stability Analysis of Hybrid Power System Using PSO-GSA Optimized Cascade PI-PD Controller for Automatic Load Frequency Control

This paper presents the automatic load frequency control (ALFC) of two-area multisource hybrid power system (HPS). The interconnected HPS model consists of conventional and renewable energy sources operating in disparate combinations to balance the generation and load demand of the system. In the proffered work, the stability analysis of nonlinear dynamic HPS model was analyzed using the Hankel method of model order reduction. Also, an attempt was made to apply cascade proportional integral - proportional derivative (PI-PD) control for HPS. The gains of the controller were optimized by minimizing the integral absolute error (IAE) of area control error using particle swarm optimization-gravitational search algorithm (PSO-GSA) optimization technique. The performance of cascade control was compared with other classical controllers and the efficiency of this approach was studied for various cases of HPS model. The result shows that the cascade control produced better transient and steady state performances than those of the other classical controllers. The robustness analysis also reveals that the system overshoots/undershoots in frequency response pertaining to random change in wind power generation and load perturbations were significantly reduced by the proposed cascade control. In addition, the sensitivity analysis of the system was performed, with the variation in step load perturbation (SLP) of 1% to 5%, system loading and inertia of the system by ±25% of nominal values to prove the efficiency of the controller. Furthermore, to prove the efficiency of PSO-GSA tuned cascade control, the results were compared with other artificial intelligence (AI) methods presented in the literature. Further, the stability of the system was analyzed in frequency domain for different operating cases