PI3K/AKT Signaling Pathway Mediated Autophagy in Oral Carcinoma - A Comprehensive Review.

Oral cancer is the most heterogeneous cancer at clinical and histological levels. PI3K/AKT/mTOR pathway was identified as one of the most commonly modulated signals in oral cancer, which regulates major cellular and metabolic activity of the cell. Thus, various proteins of PI3K/AKT/mTOR pathway were used as therapeutic targets for oral cancer, to design more specific drugs with less off-target toxicity. This review sheds light on the regulation of PI3K/AKT/mTOR, and its role in controlling autophagy and associated apoptosis during the progression and metastasis of oral squamous type of malignancy (OSCC). In addition, we reviewed in detail the upstream activators and the downstream effectors of PI3K/AKT/mTOR signaling as potential therapeutic targets for oral cancer treatment.

A pareto strategy based on multi-objective optimal integration of distributed generation and compensation devices regarding weather and load fluctuations.

In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability.

Coordinated power management strategy for reliable hybridization of multi-source systems using hybrid MPPT algorithms.

This research discusses the solar and wind sourcesintegration in aremote location using hybrid power optimization approaches and a multi energy storage system with batteries and supercapacitors. The controllers in PV and wind turbine systems are used to efficiently operate maximum power point tracking (MPPT) algorithms, optimizing the overall system performance while minimizing stress on energy storage components. More specifically, on PV generator, the provided method integrating the Perturb & Observe (P&O) and Fuzzy Logic Control (FLC) methods. Meanwhile, for the wind turbine, the proposed approach combines the P&O and FLC methods. These hybrid MPPT strategies for photovoltaic (PV) and wind turbine aim to optimize its operation, taking advantage of the complementary features of the two methods. While the primary aim of these hybrid MPPT strategies is to optimize both PV and wind turbine, therefore minimizing stress on the storage system, they also aim to efficiently supply electricity to the load. For storage, in this isolated renewable energy system, batteries play a crucial role due to several specific benefits and reasons. Unfortunately, their energy density is still relatively lower compared to some other forms of energy storage. Moreover, they have a limited number of charge-discharge cycles before their capacity degrades significantly. Supercapacitors (SCs) provide significant advantages in certain applications, particularly those that need significant power density, quick charging and discharging, and long cycle life. However, their limitations, such as lower energy density and specific voltage requirements, make them most effective when combined with other storage technologies, as batteries. Furthermore, their advantages are enhanced, result a more dependable and cost-effective hybrid energy storage system (HESS). The paper introduces a novel algorithm for power management designed for an efficient control. Moreover, it focuses on managing storage systems to keep their state of charge (SOC) within defined range. The algorithm is simple and effective. Furthermore, it ensures the longevity of batteries and SCs while maximizing their performance. The results reveal that the suggested method successfully keeps the limits batteries and SCs state of charge (SOC). To show the significance of system design choices and the impact on the battery's SOC, which is crucial for the longevity and overall performance of the energy storage components, a comparison in of two systems have been made. A classical system with one storage (PV/wind turbine/batteries) and the proposed system with HESS (PV/wind turbine system with batteries). The results show that the suggested scenario investigated with both wind and solar resources appears to be the optimum solution for areas where the two resources are both significant and complementary. The balance between the two resources seems to contribute to less stress on storage components, potentially leading to a longer lifespan. An economical study has been made, using the Homer Pro software, to show the feasibility of the proposed system in the studied area.

Modeling of Schottky diode and optimal matching circuit design for low power RF energy harvesting.

This work designs and implements a single-stage rectifier-based RF energy harvesting device. This device integrates a receiving antenna and a rectifying circuit to convert ambient electromagnetic energy into useful DC power efficiently. The rectenna is carefully engineered with an optimal matching circuit, achieving high efficiency with a return loss of less than -10 dB. The design uses a practical model of the Schottky diode, where both RF and DC characteristics are derived through extensive experimental measurements. The results from both experiments and simulations confirm the effectiveness of the design, showing its proficiency in efficient RF energy harvesting under low-power conditions. The antenna produced operates in the wifi band with a gain close to 4 dBi and a bandwidth of 100 MHz. With a load resistance of 1600 Ω, the proposed device achieves an impressive RF-to-DC conversion efficiency of approximately 52% at a low incident power of -5 dBm. These findings highlight the potential and reliability of rectenna systems for practical and efficient RF energy harvesting applications. The study significantly contributes to our understanding of rectenna-based energy harvesting, providing valuable insights for future design considerations and applications in low-power RF systems.

Nimbolide: promising agent for prevention and treatment of chronic diseases (recent update).

Background: Nimbolide, a bioactive compound derived from the neem tree, has garnered attention as a potential breakthrough in the prevention and treatment of chronic diseases. Recent updates in research highlight its multifaceted pharmacological properties, demonstrating anti-inflammatory, antioxidant, and anticancer effects. With a rich history in traditional medicine, nimbolide efficacy in addressing the molecular complexities of conditions such as cardiovascular diseases, diabetes, and cancer positions it as a promising candidate for further exploration. As studies progress, the recent update underscores the growing optimism surrounding nimbolide as a valuable tool in the ongoing pursuit of innovative therapeutic strategies for chronic diseases. Methods: The comprehensive search of the literature was done until September 2020 on the MEDLINE, Embase, Scopus and Web of Knowledge databases. Results: Most studies have shown the Nimbolide is one of the most potent limonoids derived from the flowers and leaves of neem (Azadirachta indica), which is widely used to treat a variety of human diseases. In chronic diseases, nimbolide reported to modulate the key signaling pathways, such as Mitogen-activated protein kinases (MAPKs), Wingless-related integration site-ß (Wnt-ß)/catenin, NF-κB, PI3K/AKT, and signaling molecules, such as transforming growth factor (TGF-ß), Matrix metalloproteinases (MMPs), Vascular Endothelial Growth Factor (VEGF), inflammatory cytokines, and epithelial-mesenchymal transition (EMT) proteins. Nimbolide has anti-inflammatory, anti-microbial, and anti-cancer properties, which make it an intriguing compound for research. Nimbolide demonstrated therapeutic potential for osteoarthritis, rheumatoid arthritis, cardiovascular, inflammation and cancer. Conclusion: The current review mainly focused on understanding the molecular mechanisms underlying the therapecutic effects of nimbolide in chronic diseases.

Neural networks and particle swarm for transformer oil diagnosis by dissolved gas analysis.

The lifetime of power transformers is closely related to the insulating oil performance. This latter can degrade according to overheating, electric arcs, low or high energy discharges, etc. Such degradation can lead to transformer failures or breakdowns. Early detection of these problems is one of the most important steps to avoid such failures. More efficient diagnostic systems, such as artificial intelligence techniques, are recommended to overcome the limitations of the classical methods. This work deals with diagnosing the power transformer insulating oil by analysis of dissolved gases using new techniques. For this, we have proposed intelligent techniques based on Multilayer artificial neural networks (ANN). Thus, a multi-layer ANN-based model for fault detection is presented. To improve its classification rate, this one was optimized by a meta-heuristic technique as the particle swarm optimization (PSO) technique. Optimized ANNs have never been used in transformer insulating oil diagnostics so far. The robustness and effectiveness of the proposed model is demonstrated, and high accuracy is obtained.

Geraniol attenuates oxidative stress and neuroinflammation-mediated cognitive impairment in D galactose-induced mouse aging model.

D-galactose (D-gal) administration was proven to induce cognitive impairment and aging in rodents' models. Geraniol (GNL) belongs to the acyclic isoprenoid monoterpenes. GNL reduces inflammation by changing important signaling pathways and cytokines, and thus it is plausible to be used as a medicine for treating disorders linked to inflammation. Herein, we examined the therapeutic effects of GNL on D-gal-induced oxidative stress and neuroinflammation-mediated memory loss in mice. The study was conducted using six groups of mice (6 mice per group). The first group received normal saline, then D-gal (150 mg/wt) dissolved in normal saline solution (0.9%, w/v) was given orally for 9 weeks to the second group. In the III group, from the second week until the 10th week, mice were treated orally (without anesthesia) with D-gal (150 mg/kg body wt) and GNL weekly twice (40 mg/kg body wt) four hours later. Mice in Group IV were treated with GNL from the second week up until the end of the experiment. For comparison of young versus elderly mice, 4 month old (Group V) and 16-month-old (Group VI) control mice were used. We evaluated the changes in antioxidant levels, PI3K/Akt levels, and Nrf2 levels. We also examined how D-gal and GNL treated pathological aging changes. Administration of GNL induced a significant increase in spatial learning and memory with spontaneously altered behavior. Enhancing anti-oxidant and anti-inflammatory effects and activating PI3K/Akt were the mechanisms that mediated this effect. Further, GNL treatment upregulated Nrf2 and HO-1 to reduce oxidative stress and apoptosis. This was confirmed using 99mTc-HMPAO brain flow gamma bioassays. Thus, our data suggested GNL as a promising agent for treating neuroinflammation-induced cognitive impairment.

Epigenetic modulation of long noncoding RNA H19 in oral squamous cell carcinoma-A narrative review.

Oral squamous cell carcinoma (OSCC) showed a seemingly increasing incidence in the last decade. In India, despite the use of tobacco decreased rapidly, in the past five years, the incidence pattern of OSCC over gender and age showed a drastic shift. About 51 % of the head and neck cancers are not associated with habits. Studies exploring various contributing factors in the incidence of this malignancy have documented. Recently, the epigenetic factors associated with the induction and progression of OSCC were explored. More than 90 % of the human genome is made up of non-coding transcriptome, which believed to be noises. However, these non-coding RNAs were identified to be the major epigenetic modulators, which raises concern over incidence of carcinoma in non-habit patients. H19 is a long non coding RNA which proved to be an effective biomarker in various carcinoma. Its role in oral squamous cell cancer was not investigated in depth. This review discusses in detail the various epigenetic role of H19 in inducing oral carcinogenesis.

Design and performances improvement of an UWB antenna with DGS structure using a grey wolf optimization algorithm.

In this article, we propose the design of a rectangular-shaped patch antenna suitable for ultra-wideband (UWB) applications and short and long-range Millimeter-Wave Communications. We begin with the design of a high-gain UWB rectangular patch antenna featuring a partial ground plane and operating within the 3.1-10.6 GHz bandwidth. Complementary Split Ring Resonators (CSRRs) are integrated on both sides of the structure to meet desired specifications. The resulting UWB antenna boasts an extended frequency bandwidth, covering 2.38-22.5 GHz (twice that of the original antenna), with a peak gain of 6.5 dBi and an 88% radiation efficiency. The grey wolf optimization technique (GWO) determines optimal structural dimensions. Validation of the antenna's performance is demonstrated through the strong agreement between measurement and simulation.

An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques.

Integration renewable energy sources into current power generation systems necessitates accurate forecasting to optimize and preserve supply-demand restrictions in the electrical grids. Due to the highly random nature of environmental conditions, accurate prediction of PV power has limitations, particularly on long and short periods. Thus, this research provides a new hybrid model for forecasting short PV power based on the fusing of multi-frequency information of different decomposition techniques that will allow a forecaster to provide reliable forecasts. We evaluate and provide insights into the performance of five multi-scale decomposition algorithms combined with a deep convolution neural network (CNN). Additionally, we compare the suggested combination approach's performance to that of existing forecast models. An exhaustive assessment is carried out using three grid-connected PV power plants in Algeria with a total installed capacity of 73.1 MW. The developed fusing strategy displayed an outstanding forecasting performance. The comparative analysis of the proposed combination method with the stand-alone forecast model and other hybridization techniques proves its superiority in terms of forecasting precision, with an RMSE varying in the range of [0.454-1.54] for the three studied PV stations.

The Inhibitory Effect of Geraniol on CCL4-induced Hepatorenal Toxicity in Pregnant Mice through the PI3K/AKT Signaling Pathway.

Background: Hepatotoxicity caused by CCL4 is well known. Geraniol (GNL) has high antioxidant effect that can induces liver regeneration. However, the protective effect of GNL effect on CCL4-induced hepatorenal toxicity in pregnant mice has not yet been studied. Objective: To investigate whether GNL could protect against oxidative stress induced by CCL4 via the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, which is regulated by phosphatidylinositol 3 kinase/protein kinase B (PI3K/AKT), and has been found to have protective effects on renal and hepatic tissues. Materials and Methods: Forty-eight female albino mice weighing 25-30 g were randomly allocated to 4 groups: Group I served as a control; Group II received a toxicity-inducing single dose of 15 µL of CCL4 on the 4th day after mating; Group III received 40 mg/kg GNL + CCL4 (with GNL from the 1st day of assimilation to delivery); and Group IV received GNL alone from the 1st day of assimilation to the end of the delivery period. GNL was evaluated for its protective effects on hepatotoxicity in CCL4-treated pregnant mice. Litter size, weight, survival rate, and resorption were recorded. In addition, H & E staining was done for liver and kidney pathology as well as biochemical markers and oxidative markers malondialdehyde, superoxide dismutase, and catalase were analyzed. Results: CCL4 significantly reduced survival rate and increased resorption after exposure. Alanine transaminase and aspartate aminotransferase concentrations in the serum, tissue MDA, blood urea nitrogen, and creatinine were increased after CCL4 exposure. GNL improved enzyme and antioxidant levels and prevented CCL4-induced hepatic injury in mice. Caspase-3 cleavage was decreased by GNL, which increased PI3K, phosphorylated AKT, Nrf2, and B-cell lymphoma 2. Conclusion: GNL demonstrates a protective effect against CCl4-induced hepatorenal toxicity, mediated through the activation of the PI3K/AKT signaling pathway and the upregulation of Nrf2. These findings highlight the potential therapeutic implications of GNL in mitigating oxidative stress and inflammation in liver and kidney tissues.

Optimization and intelligent power management control for an autonomous hybrid wind turbine photovoltaic diesel generator with batteries.

In this paper, a critical issue related to power management control in autonomous hybrid systems is presented. Specifically, challenges in optimizing the performance of energy sources and backup systems are proposed, especially under conditions of heavy loads or low renewable energy output. The problem lies in the need for an efficient control mechanism that can enhance power availability while protecting and extending the lifespan of the various power sources in the system. Furthermore, it is necessary to adapt the system's operations to variations in climatic conditions for sustained effectiveness. To address the identified problem. It is proposed the use of an intelligent power management control (IPMC) system employing fuzzy logic control (FLC). The IPMC is designed to optimize the performance of energy sources and backup systems. It aims to predict and adjust the system's operating processes based on variations in climatic conditions, providing a dynamic and adaptive control strategy. The integration of FLC is specifically emphasized for its effectiveness in balancing multiple power sources and ensuring a steady and secure operation of the system. The proposed IPMC with FLC offers several advantages over existing strategies. Firstly, it showcases enhanced power availability, particularly under challenging conditions such as heavy loads or low renewable energy output. Secondly, the system protects and extends the lifespan of the power sources, contributing to long-term sustainability. The dynamic adaptation to climatic variations adds a layer of resilience to the system, making it well-suited for diverse geographical and climatic conditions. The use of realistic data and simulations in MATLAB/Simulink, along with real-time findings from the RT-LAB simulator, indicates the reliability and practical applicability of the proposed IPMC strategy. Efficient load supply and preserved batteries further underscore the benefits of the fuzzy logic-based control strategy in achieving a well-balanced and secure system operation.

Design and Fabrication of a Novel Corona-Shaped Metamaterial Biosensor for Cancer Cell Detection.

The early detection and diagnosis of cancer presents significant challenges in today's healthcare. So, this research, suggests an original experimental biosensor for cell cancer detection using a corona-shaped metamaterial resonator. This resonator is designed to detect cancer markers with high sensitivity, selectivity, and linearity properties. By exploiting the unique properties of the corona metamaterial structure in the GHz regime, the resonator provides enhanced interaction of electromagnetic waves and improved detection skills. Through careful experimental, simulation, and optimization studies, we accurately demonstrate the resonator's ability to detect cancer. The proposed detection system is capable of real-time non-invasive cancer detection, allowing for rapid intervention and better patient outcomes. The sensitivity value was confirmed through simulation, estimated at 0.1825 GHz/RIU. The results of two different simulation methods are used: the simulation software CST Studio Suite (version 2017) based on the finite element method (FEM), and the simulation software ADS (version 2019) based on the equivalent circuit method, thereby increasing confidence in the convergence of simulation and measurement results. This work opens new avenues for developing advanced detection technologies in the field of oncology, and paves the way for more effective cancer diagnosis. The experimental study verified that this realized sensor has very small frequency shifts, significantly small electrical dimension and miniaturization, high sensitivity, and good linearity. The suggested configurations showed a capacity for sensing cancer cells in the GHz regime.

Agrobacterium-Mediated Genetic Transformation of Withania coagulans (Dunal) with rol A Genes and Its Antioxidant Potential.

In ancient times, Withania coagulans Dunal was used as a therapeutic plant for the treatment of several diseases. This report aims to examine the effect of Agrobacterium tumefactions-mediated transformation of W. coagulans with the rolA gene to enhance secondary metabolite production, antioxidant activity, and anticancer activity of transformed tissues. Before transgenic plant production, the authors designed an efficient methodology for in vitro transformation. In this study, leaf explants were cultured on Murashage and Skoog (MS) media containing different amounts of naphthalene acetic acid (NAA) and benzyl adenine (BA). The best performance for inducing embryogenic callus was in MS medium containing 4 µM NAA and 6.0 µM BA, while the best results for shooting (100%) were obtained at 8 µM benzyl adenine. On the other hand, direct shooting was attained by subculturing leaves on MS medium supplemented with 8 µM benzyl adenine. Prolonged shoots showed excellent in vitro rooting results (80%) with 12 µM indole-3-butyric acid (IBA). The samples were precultivated for 3 days and were followed by 48 h infection with A. tumefaciens strain GV3101 having pCV002. Then, a vector expressed the rol A gene of strain Agrobacterium rhizogenes. Furthermore, three independent transgenic shoot lines and one callus line (T2) were produced and exhibited stable integration of transgene rol A genes, as revealed by PCR analysis. Transgenic strains showed a significant increase in antioxidant potential as compared to untransformed plants. Additionally, LC-MS analysis showed that the transformed strains have a higher withanolide content as compared to untransformed ones. Moreover, the reduced proliferation of prostate cancer cells was observed after treatment with extracts of transgenic plants. Furthermore, these transformed plants exhibited superior antioxidant capability and higher withanolide content than untransformed ones. In conclusion, the reported data can be used to select withanolide-rich germplasm from transformed cell cultures.

Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil.

This study investigates the application of the Gaussian Radial Basis Function Neural Network (GRNN), Gaussian Process Regression (GPR), and Multilayer Perceptron Optimized by Particle Swarm Optimization (MLP-PSO) models in analyzing the relationship between rainfall and runoff and in predicting runoff discharge. These models utilize autoregressive input vectors based on daily-observed TRMM rainfall and TMR inflow data. The performance evaluation of each model is conducted using statistical measures to compare their effectiveness in capturing the complex relationships between input and output variables. The results consistently demonstrate that the MLP-PSO model outperforms the GRNN and GPR models, achieving the lowest root mean square error (RMSE) across multiple input combinations. Furthermore, the study explores the application of the Empirical Mode Decomposition-Hilbert-Huang Transform (EMD-HHT) in conjunction with the GPR and MLP-PSO models. This combination yields promising results in streamflow prediction, with the MLP-PSO-EMD model exhibiting superior accuracy compared to the GPR-EMD model. The incorporation of different components into the MLP-PSO-EMD model significantly improves its accuracy. Among the presented scenarios, Model M4, which incorporates the simplest components, emerges as the most favorable choice due to its lowest RMSE values. Comparisons with other models reported in the literature further underscore the effectiveness of the MLP-PSO-EMD model in streamflow prediction. This study offers valuable insights into the selection and performance of different models for rainfall-runoff analysis and prediction.

Assessing coastal vulnerability and land use to sea level rise in Jeddah province, Kingdom of Saudi Arabia.

Sea level rise is one of the most serious outcomes of increasing temperatures, leading to coastal flooding, beach erosion, freshwater contamination, loss of coastal habitats, increased soil salinity, and risk of damage to coastal infrastructures. This study estimates the vulnerability to inundation for 2100 in coastal zones in Jeddah Province, Kingdom of Saudi Arabia, under various sea level rise (SLR) scenarios of 1, 2, 5, and 10 m. The predicted flooding was estimated using a combination of factors, including SLR, the bathtub model, digital elevation model, climate scenarios, and land use and land cover. The climate scenarios used were Representative Concentration Pathway (RCP) scenarios 1.9, 2.6, 4.5, and 8.5. The results of the SLR scenarios of 1, 2, 5, and 10 m revealed that 1.6, 4.7, 14.9, and 30.6% (or 88, 214, 679, 1398 km2) of the study area's coast could be classified as inundated areas. The various SLR scenarios can inundate 3.3 to 34% of the road area/length. The inundated built-up and road areas were estimated to range between 0.31 and 0.79 km2, accounting respectively for 1.18 to 3.01% of the total class areas for 1-meter and 2-meter SLR scenarios. In contrast, the inundated area will be significant in the situation of 5 and 10 m SLR scenarios. Regarding the case of a 10-meter SLR scenario, the inundation will negatively impact the built-up and road infrastructure areas, inundating 8.9 km2, with industrial infrastructures affected by inundation estimated at 0.21 km2, followed by green space infrastructures at 0.013 km2. The spatial information based on various SLR scenario impact mapping for Jeddah Province can be highly valuable for decision-makers to better plan future civil engineering structures within the framework of sustainable development.

Predicting California bearing ratio of HARHA-treated expansive soils using Gaussian process regression.

The California bearing ratio (CBR) is one of the basic subgrade strength characterization properties in road pavement design for evaluating the bearing capacity of pavement subgrade materials. In this research, a new model based on the Gaussian process regression (GPR) computing technique was trained and developed to predict CBR value of hydrated lime-activated rice husk ash (HARHA) treated soil. An experimental database containing 121 data points have been used. The dataset contains input parameters namely HARHA-a hybrid geometrical binder, liquid limit, plastic limit, plastic index, optimum moisture content, activity and maximum dry density while the output parameter for the model is CBR. The performance of the GPR model is assessed using statistical parameters, including the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), Relative Root Mean Square Error (RRMSE), and performance indicator (ρ). The obtained results through GPR model yield higher accuracy as compare to recently establish artificial neural network (ANN) and gene expression programming (GEP) models in the literature. The analysis of the R2 together with MAE, RMSE, RRMSE, and ρ values for the CBR demonstrates that the GPR achieved a better prediction performance in training phase with (R2 = 0.9999, MAE = 0.0920, RMSE = 0.13907, RRMSE = 0.0078 and ρ = 0.00391) succeeded by the ANN model with (R2 = 0.9998, MAE = 0.0962, RMSE = 4.98, RRMSE = 0.20, and ρ = 0.100) and GEP model with (R2 = 0.9972, MAE = 0.5, RMSE = 4.94, RRMSE = 0.202, and ρ = 0.101). Furthermore, the sensitivity analysis result shows that HARHA was the key parameter affecting the CBR.

Salivaomics to decode non-coding RNAs in oral cancer. A narrative review.

Oral cancer is the most debilitating disease which affects the orderly life of a human. With so much advancement in research and technology, the average life expectancy of an individual with oral cancer appears to be about 5 years. The changing trend in incidence of oral cancer among young individuals and women without tobacco habits are ascending. Non habit related oral cancer are taking centre stage and multiple factors which induce complex biology are associated in such scenarios. To decipher the aetiology and to understand the process, these cancerous conditions are to be studied at molecular level. Saliva, the most non-invasively obtained body fluid are assessed for biomarkers exclusively in liquid biopsy. This fluid gives a huge platform to study number of molecules associated with oral cancer. Non coding RNAs are transcripts with no protein coding function. They are gaining more importance in recent times. Long noncoding RNA, microRNA are major types of noncoding transcriptome that influences in progression of oral cancer. They seem to play an important role in health and disease. Apart from these, circulating tumour cells, exosomes, extracellular vesicles, antigens and other proteins can be studied from saliva. This review is aimed to update the knowledge on current biomarkers in saliva associated with oral cancer and their epigenetic role in disease progression as well recent advances in detecting these markers to identify the stage of the disease, which will help in deciding the treatment protocol.

Polyphenols Mediate Neuroprotection in Cerebral Ischemic Stroke-An Update.

Stroke is one of the main causes of mortality and disability, and it is due to be included in monetary implications on wellbeing frameworks around the world. Ischemic stroke is caused by interference in cerebral blood flow, leading to a deficit in the supply of oxygen to the affected region. It accounts for nearly 80-85% of all cases of stroke. Oxidative stress has a significant impact on the pathophysiologic cascade in brain damage leading to stroke. In the acute phase, oxidative stress mediates severe toxicity, and it initiates and contributes to late-stage apoptosis and inflammation. Oxidative stress conditions occur when the antioxidant defense in the body is unable to counteract the production and aggregation of reactive oxygen species (ROS). The previous literature has shown that phytochemicals and other natural products not only scavenge oxygen free radicals but also improve the expressions of cellular antioxidant enzymes and molecules. Consequently, these products protect against ROS-mediated cellular injury. This review aims to give an overview of the most relevant data reported in the literature on polyphenolic compounds, namely, gallic acid, resveratrol, quercetin, kaempferol, mangiferin, epigallocatechin, and pinocembrin, in terms of their antioxidant effects and potential protective activity against ischemic stroke.