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Introduction: Surgical removal of the lower jaw is the most common cause of mandibular deviation due to benign or malignant neoplasms. The Palatal Guide Flange Prosthesis (PGFP) is a recommended corrective appliance to reduce this clinical manifestation. The primary goals of rehabilitation with PGFP are to train the mandibular muscles and restore a healthy occlusal relationship so that the patient can effectively control their mouth opening and closing movements. In the present article, the Masticatory Performance (MP) of patients who had undergone hemi-mandibulectomy surgery was evaluated both before and after receiving an interim PGFP. Aim: To clinically evaluate MP pre- and post-mandibular guidance therapy in hemi-mandibulectomy patients. Materials and Methods: A quasi-experimental study was conducted on patients selected from the Outpatient Department of the Department of Prosthodontics at Bharati Vidyapeeth Dental College and Hospital, Sangli, Maharashtra, India, from January 2021 to July 2022, and were included as the sample size for the study. A total of 10 samples were selected. The study design was quasi-experimental. All cancer patients had lost their mandibular continuity and showed mandibular deviation toward the affected side. PGFPs were fabricated using Heat Cure Acrylic Resin (DPI) for each participant (n=10). Corn flakes (Kellogg’s) were given to the participants to chew, and the samples were sieved using a US standard 8-mesh sieve and collected at three different time intervals: T0 (preprosthesis), T1 (8th week), and T2 (16th week post-prosthesis). Data were entered into Microsoft Excel Version 13 and analysed using IBM Statistical Package for the Social Sciences (SPSS) version 21.0. Descriptive and inferential statistics were used, including mean and standard deviation for continuous variables and frequency analysis for categorical variables. For categorical variables, a Chi-square proportion test was utilised, and MP was evaluated between T0-T1 and T0-T2. We utilised a 95% confidence interval and a p-value of less than 0.05. Results: When comparing the MP between T0, T1 and T2 time intervals, it was found that the difference in mean MP was statistically significant (p

Pomegranate is an important fruit crop that is capturing international attention for its rich antioxidants and nutraceutical value. The demand for the fruit is rapidly increasing in major national and international markets. Therefore, any serious pest is of great importance and here we report the severe incidence of adult beetles of Schizonycha ruficollis that resulted in potential yield loss due to its feeding on pomegranate flowers in Devanahalli, Karnataka. Usually June/July adult beetle emerge and feed on hosts like neem and Melia rubra which the farmers plant them as wind breaks along the borders. These attract the beetles into pomegranate field and the pomegranate flowers are then infested by the adult beetles. The pomegranate on an average possess 223 female flowers per tree, which when damaged fails to set fruits causing economic loss. A documentation study during 2023 was taken up to evaluate the crop loss caused by S. ruficollis in pomegranate resulted in potential seven percent yield loss accounting to ₹ 53215/acre at the farm gate price. This loss can be avoided by initiating a management practice at the first site of the beetles especially after the first monsoon rain. This study also helped us to alert pomegranate exporters and extension officials about the need of preventive measures to avoid the yield loss.

Biosensors have the potential to revolutionize healthcare by providing rapid and accurate diagnosis of diseases. Biosensors are analytical devices that convert molecular recognition of a target analyte into a measurable signal. Older diagnostic techniques, such as immunoaffinity column assays, fluorometric, and enzyme-linked immunosorbent assays, are laborious, require qualified personnel, and can be time consuming. In contrast, biosensors offer improved accuracy, sustainability, and rapidness due to their ability to detect specific biomarkers with high sensitivity and specificity. The review covers various bacterial cellulose (BC)-based biosensors, from SARS-CoV-2 detection to wearable health monitoring and interaction with human-computer interfaces. BC's integration into ionic thermoelectric hydrogels for wearable health monitoring shows its potential for real-time health tracking. Incorporating BC in biosensors for low-noise electrodes, and wearable sensors has been elaborated. The invention of a phage-immobilized BC biosensor for S. aureus detection is a significant contribution to the field, highlighting the biosafety and efficiency of BC in pathogen identification and demonstrating BC's versatility across multiple sensing platforms. Palladium nanoparticle-bacterial cellulose hybrid nanofibers show excellent electrocatalytic activity for dopamine detection, whereas Au-BC nanocomposite biosensors show efficacy in glucose detection, with potential therapeutic applications. The “lab-on-nanopaper” device, utilizing BC nanopaper, not only visually detects human serum albumin but also establishes itself as a new-generation optical biosensing platform with superiority over conventional substrates. This review contributes to the ongoing advancements in biosensor technology, highlighting the potential of BC as a versatile material for developing innovative biosensors. This is crucial for improving the accuracy, sensitivity, and efficiency of diagnostic tools in healthcare.

In the present work, polycarbazole (PCz)/magnesium cobalt oxide (MgCo2O4)/reduced graphene oxide (RGO) based ternary nanocomposite was prepared through in-situ polymerization, and utilized it as an active electrodes for electrochemical energy storage supercapacitor applications. The electrochemical behaviour of PCz and its nanocomposites were investigated by measuring specific capacitance using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and Galvanostatic charge–discharge (GCD) analysis. The PCz/MgCo2O4/RGO hybrids exhibited higher capacitance (548.54 F/g) than that of PCz (117.65 F/g) and PCz/MgCo2O4 (482.92 F/g) at the scan rate of 50 mV/s, as determined by CV method. The enhanced supercapacitance indicates high power and energy storage capabilities of the ternary metal oxide-graphene based polycarbazole nanocomposites. Electrochemical impedance spectroscopy confirmed low solution resistance of PCz/MgCo2O4/RGO. Thermogravimetric analysis affirmed the increased thermal stability of PCz/MgCo2O4/RGO composite compared to that of pure polycarbazole and PCz/MgCo2O4 nanocomposite. The scanning electron micrographs of nanocomposite confirmed the successful incorporation of nanofillers into the PCz matrix. On the basis of the research findings, PCz/MgCo2O4/RGO can be expected to be a promising electrode active material for high performance energy storage supercapacitors.

Introduction: Surgical removal of the lower jaw is the most common cause of mandibular deviation due to benign or malignant neoplasms. The Palatal Guide Flange Prosthesis (PGFP) is a recommended corrective appliance to reduce this clinical manifestation. The primary goals of rehabilitation with PGFP are to train the mandibular muscles and restore a healthy occlusal relationship so that the patient can effectively control their mouth opening and closing movements. In the present article, the Masticatory Performance (MP) of patients who had undergone hemi-mandibulectomy surgery was evaluated both before and after receiving an interim PGFP. Aim: To clinically evaluate MP pre- and post-mandibular guidance therapy in hemi-mandibulectomy patients. Materials and Methods: A quasi-experimental study was conducted on patients selected from the Outpatient Department of the Department of Prosthodontics at Bharati Vidyapeeth Dental College and Hospital, Sangli, Maharashtra, India, from January 2021 to July 2022, and were included as the sample size for the study. A total of 10 samples were selected. The study design was quasi-experimental. All cancer patients had lost their mandibular continuity and showed mandibular deviation toward the affected side. PGFPs were fabricated using Heat Cure Acrylic Resin (DPI) for each participant (n=10). Corn flakes (Kellogg's) were given to the participants to chew, and the samples were sieved using a US standard 8-mesh sieve and collected at three different time intervals: T0 (preprosthesis), T1 (8th week), and T2 (16th week post-prosthesis). Data were entered into Microsoft Excel Version 13 and analysed using IBM Statistical Package for the Social Sciences (SPSS) version 21.0. Descriptive and inferential statistics were used, including mean and standard deviation for continuous variables and frequency analysis for categorical variables. For categorical variables, a Chi-square proportion test was utilised, and MP was evaluated between T0-T1 and T0-T2. We utilised a 95% confidence interval and a p-value of less than 0.05. Results: When comparing the MP between T0, T1 and T2 time intervals, it was found that the difference in mean MP was statistically significant (p<0.05), with MP increasing from 26.2% to 43% to 61.2%, respectively. Conclusion: The occlusion restored with the guide flange prosthesis, along with continuous use over a longer period, helped correct the deviated mandible and, in turn, improved the MP. [ABSTRACT FROM AUTHOR]

In this task, the improvement on the electrochemical performances, particularly, on the cyclability and capacity retention of cathode material, LiMn2-x-yCexCuyO4 is reported using the dual doping method. This way of substitution is performed to investigate the effect of simultaneous substitution of mono and trivalent cations Ce+ and Cu3+ respectively for Mn3+/4+ into LiMn2O4 cathode by making the average oxidation state Mn to be around +3.5 and above. Thus, the synthesis of a new series of spinel LiMn2-x-yCexCuyO4 (0.01 ≤ x ≤ 0.05 and y = x) cathode materials are performed. An investigation of the electrical and electrochemical properties of the synthesized materials is performed. [ABSTRACT FROM AUTHOR]

Background: The production of inflammatory factors in periodontium is increased by LPS, particularly from P. gingivalis, and the damage to periodontal tissues is exacerbated. Exosomes from periodontal ligament stem cells change regeneration and repair brought on by bacterial LPS. MiRNAs are carried by exosomes to recipient cells to affect epigenetic functions. Thus, this study aims to utilize deep learning algorithms to uncover novel micro-RNA biomarkers in bacterial LPS-exposed PDLSC stem cells to understand the activation pathway. Methods: Using NCBI GEO DATA SET GSE163489, the most differentially expressed micro RNAs were found to differ between healthy and LPS-induced PDLSC cells. Deep learning analysis, employing a Random Forest, Artificial Neural Network c, a Support Vector Machine (SVM), and a Linear Regression model implemented within the orange data mining toolkit, identified novel microRNA biomarkers. The orange data mining toolkit was utilized for deep learning analysis of microRNA expression data, providing a user-friendly environment for machine learning tasks like classification, regression, and clustering. Results: Random Forest emerged as the superior model, achieving the highest R 2 score (.985) and the lowest RMSE (0.189) compared to Neural Networks ( R 2 = .952, RMSE = 0.332), Linear Regression ( R 2 = .949, RMSE = 0.343), and SVM ( R 2 = .931, RMSE = 0.398). This suggests its superior ability to capture the underlying patterns in the microRNA expression data. Given its robust performance, Random Forest holds promise for identifying novel biomarkers, developing more accurate diagnostic tools, and potentially guiding the stratification of patients for targeted therapeutic interventions in periodontal disease. Conclusion: The current study utilizes deep learning analysis of microRNA expression data to identify novel biomarkers associated with inflammasome activation and anti-apoptotic pathways. These findings hold promise for guiding the development of novel therapeutic strategies for periodontal disease. However, future studies are warranted to validate these biomarkers using independent datasets and experimental methods.

Aim: The Insilco study uses deep learning algorithms to predict the protein-coding pg m RNA sequences. Material and methods: The NCBI GEO DATA SET GSE218606’s GEO R tool discovered P.G’s outer membrane vesicles’ most differentially expressed mRNA. Genemania analyzed differentially expressed gene networks. Transcriptomics data were collected and labeled on P. gingivalis protein-coding mRNA sequence and pseudogene, lincRNA, and bidirectional promoter lincRNA. Orange, a machine learning tool, analyzed and predicted data after preprocessing. Naïve Bayes, neural networks, and gradient descent partition data into training and testing sets, yielding accurate results. Cross-validation, model accuracy, and ROC curve were evaluated after model validation. Results: Three models, Neural Networks, Naive Bayes, and Gradient Boosting, were evaluated using metrics like Area Under the Curve (AUC), Classification Accuracy (CA), F 1 Score, Precision, Recall, and Specificity. Gradient Boosting achieved a balanced performance (AUC: 0.72, CA: 0.41, F 1: 0.32) compared to Neural Networks (AUC: 0.721, CA: 0.391, F1: 0.314) and Naive Bayes (AUC: 0.701, CA: 0.172, F1: 0.114). While statistical tests revealed no significant differences between the models, Gradient Boosting exhibited a more balanced precision-recall relationship. Conclusion: In silico analysis using machine learning techniques successfully predicted protein-coding mRNA sequences within Porphyromonas gingivalis OMVs. Gradient Boosting outperformed other models (Neural Networks, Naive Bayes) by achieving a balanced performance across metrics like AUC, classification accuracy, and precision-recall, suggests its potential as a reliable tool for protein-coding mRNA prediction in P. gingivalis OMVs.