Search

There are many measures of the importance of a crop to the economy, including its area, output, and yield increase. The current study will look at the growth rates of tea acreage, output, and yield in India using training data from 1918 to 2015 and testing data from 2016 to 2018. Using the data acquired, the ARIMA model and State Space Models were used to anticipate the area, production, and yield of tea from 2021 to 2027. According to the data, tea production in India is expected to reach 607 thousand hectares by2027, reflecting a 3.93 percent increase between 2021 and 2027. India's tea production is expected to reach 1486 thousand tonnes in2027, reflecting a 10.56 percent increase between 2021 and 2027. However, the tea production in India is expected to reach 2449 kg/hectare between 2021 and2027, reflecting a 4.12% increase over the preceding five-year period. The most essential tools for increasing tea production were area expansion and yield increase. As a result, the emphasis should be on expanding the area by exploiting available land and boosting productivity through technological advancements, varietal research, and the enhancement of agricultural advisory services across India. [ABSTRACT FROM AUTHOR]

The goal of phishing assaults is to trick users into giving up personal information by making them believe they need to act quickly on critical information. The creation of efficient solutions, such as phishing attack detection systems backed by AI, is essential for the safety of users. This research suggests a two-stage hybrid strategy that uses both URL and content analysis to identify phishing assaults. In the first step of the suggested method, URL analysis is used to determine the legitimacy of suspected phishing assaults. If the site is still live, the second check uses content analysis to determine how serious the attack is. Both analysis' findings are taken into account in the decision-making procedure. As can be seen from the experiments, the hybrid system obtains an astounding 99.06% accuracy rate. This research adds to the existing body of knowledge by providing a massive dataset of over 14 million data samples that includes both legal and phishing URLs. Furthermore, when content analysis is required for phishing URL detection, the two-stage hybrid technique significantly outperforms URL analysis alone by 70.23 %. The proposed method provides better defense against phishing attempts and is practical enough for widespread use.

The paper attempts to analyze fish production and the direction of trade. Data for the analysis was taken from a period of 10 years (2011- 2021) from the Ministry of Commerce & Industry and FAO. To examine the type and extent of increase in the fish area, production, and productivity throughout the course of the year for several countries, including China, Vietnam, the United States, Norway, and India, descriptive statistics and the sustainability index were utilized. Markov chain analysis employing linear programming was then applied to determine transition probabilities in fish trade. The fish export markets were the USA, China, Japan, Thailand, Taiwan, Kuwait, Hong Kong, and others. The fish export markets were categorized as stable markets (China, USA, Taiwan, Thailand, and Hong Kong) and unstable markets (Japan and Kuwait) based on the magnitude of transition probabilities. Though the country has a good potential for export of fish. India must therefore give rising output more consideration, supported by measures that encourage exports. In addition, initiatives must be made to develop a new market and broaden the trade area to include other significant, global markets.

The study aimed to compare ARIMA and Holt's models for predicting coconut metrics in Kerala. The coconut data series was collected from the period 1957 to 2019. Of this, 80% of the data (from 1957 to 2007) is treated as training data, and the rest (20% from 2008 to 2019) is treated as testing data. Ideal models were selected based on lower AIC and BIC values. Their accuracy was evaluated through error estimation on testing data, revealing Holt's exponential, linear, and ARIMA (0,1,0) models as the bestfit choices for predicting coconut area, production, and productivity respectively. After using the testing data, we tried for the forecasting for 2020-2024 using these models, and the DM test confirmed their significant forecasting accuracy. This comprehensive analysis provides valuable insights into effective prediction models for coconut-related metrics, offering a foundation for informed decision-making and future projections.

Delhi's Yamuna River serves as a notable illustration of an ecologically compromised system that has undergone a transition into a conduit for sewage due to pervasive pollution and escalating anthropogenic influences. Delhi, being the primary contributor to pollution, is responsible for over 70% of the total pollutant load in the Yamuna. The city's drainage systems discharge a substantial Biological Oxygen Demand load into the river daily, resulting in severe pollution. This research utilizes pre-existing data to examine diverse factors, evaluating the quality of water at distinct observation locations along the Yamuna. The utilization of correlation analysis aids in recognizing connections among elements influencing the pollution of river water. The outcomes of the correlation analysis disclose a notable link between COD-BOD factors, whereas the connections among alternative factors like BOD-DO, BOD-pH, COD-DO, COD-pH, and DOpH range from moderate to negligible. The majority of observed parameters exceed hazardous levels deemed acceptable for river water utilization. The evaluation of Sewage Treatment Plants highlights the imperative to augment capacity in terms of treatment, storage, reactivation of closed plants, and efficient operation to meet the growing demand for fresh water. Additionally, there is a pressing need to generate demand for wastewater in diverse urban sectors.

Agriculture is the backbone of Indian Economy. Proper forecast of food crops and cash crops are necessary for the government in policy making decisions. The present paper aims to forecast Wheat and Sugarcane yield using Random Forest Regression. For the development of Random Forest models, Yield has been taken as dependent variable and variables like Gross Cropped Area, Maximum Temperature, Minimum Temperature, Rainfall, Nitrogen, Phosphorous Oxide, Potassium Oxide, Minimum Support Price and Area under Irrigation are taken as independent variables for both Wheat and Sugarcane crop. Values of R2 for Wheat and Sugarcane is 0.995 and 0.981 which indicates that the model is a good fit and other performance measures are calculated and results are satisfactory.

The present study aims to develop yield forecast models for the Sugarcane crop of the Coimbatore district in Tamilnadu using two different techniques namely Variables and Months in Discriminant function analysis. For this, the Sugarcane yield data for 57 years along with the monthly data on seven weather variables have been taken. For applying discriminant analysis, the yield data of sugarcane has been divided into two categories namely two groups and three groups. The discriminant scores from the two and three-group discriminant functions were employed as independent variables in the development of yield forecast models. The yield forecast models for both strategies were created utilizing scores and trend values as independent variables. The first 52 years of yield data (1960-2012) were used to create the model, and the last five years of data (2012-2016) were used for validation. The comparison has been made between two and three groups for both techniques. The results indicate the technique using the variable-wise method gives better results based on goodness of fit. Among the two categories in the variable-wise method, three groups performed better.

Through the use of smart sensors to monitor and regulate plant conditions, smart home gardening management systems can maximize resource utilisation and minimize human intervention. This study offers a new system that remotely controls the water supply to ensure optimal plant growth without the need for personal presence. The system uses the Blynk IoT platform to monitor soil moisture and water levels. A Raspberry Pi is used in conjunction with several sensors, such as a soil moisture sensor and a DHT11 sensor for temperature and humidity readings. The technology activates a motor to provide water to the plants automatically when the soil moisture level falls below a certain threshold. Users can remotely monitor and manage the system from their cell phones thanks to integration with the Blynk platform. The suggested method is an affordable and effective way to garden in your home, and it’s simply customizable to fit the requirements of different users.

This study offers a detailed comparative analysis of maize crop cultivation in the kharif and rabi seasons within the agricultural landscape of Gonda District. 50 respondents were carefully selected from various villages in the block, with proportional representation for Marginal, Small, and Medium-sized farmers. The research delves into the economics of maize cultivation, emphasizing factors such as the cost of cultivation, input expenses, income generation, and input-output ratios. In the kharif season, it was distinguished that the cost of cultivation of maize with the farm's size. Marginal farms spent an average of ₹ 48125.93 per hectare, small farms incurred ₹ 51002.89, and large farms invested ₹ 54295.17. Similarly, during the rabi season, the cost of cultivation increased with farm size, with marginal farms investing an average of ₹ 52397.57, small farms spending ₹ 55444.93, and large farms allocating ₹ 58604.68 per hectare. Crucially, the study found that input-output ratios remained consistent across farm sizes in both seasons, reflecting uniform agricultural practices. The findings underscore the importance of efficient management, the adoption of advanced agricultural techniques, the use of high-quality seeds, and the timely application of irrigation and plant protection practices in enhancing net income, particularly on marginal farms.

Sugarcane is the primary agricultural industry that sustains and promotes economic growth in India. In 2018, the majority of India's sugarcane production, specifically 79.9%, was allocated for the manufacturing of white sugar. A smaller portion, 11.29%, was used to produce jaggery, while 8.80% was utilized as seed and feed components. A total of 840.16 million metric tonnes of cane sugar was shipped in the year 2019. The primary objective of this research is to determine the most suitable forecasting model for predicting the monthly export price of sugarcane in India. The input consists of a time series with 240 monthly observations of the export price of sugarcane in India, spanning from January 1993 to December 2013. The SARIMA approach was employed to predict the monthly export price of sugarcane and it is concluded that the SARIMA (0, 1, 1), (0, 0, 0)12 model is the best-fitted one by the expert modeler method. As a result, the fitted model appears to be adequate. The RMSE and MAPE statistics are used to analyze the precision of the model.

To model and forecast complex time series data, machine learning has become a major field. This machine learning study examined Moscow rainfall data's future performance. The dataset is split into 65% training and 35% test sets to build and validate the model. We compared these deep learning models using the Root Mean Square Error (RMSE) statistic. The LSTM model outperforms the BILSTM and GRU models in this data series. These three models forecast similarly. This information could aid the creation of a complete Moscow weather forecast book. This material would benefit policymakers and scholars. We also believe this study can be used to apply machine learning to complex time series data, transcending statistical approaches.

Online shopping can be done from our convenient places like home, office, etc., and the product will be delivered to the respective places. There are many factors influencing online shopping. The purpose of this study is to develop a statistical model that is used to determine the factors that influence online shopping. In this study, using factor analysis five main factors have been obtained from 15 variables that influence online shopping. These five factors have significant effects on satisfaction of customers and accounted up to 56% of total variation. Using the factor scores as independent variables, multiple regression model has been developed for predicting customers satisfaction in online shopping. Customer satisfaction has been used as dependent variable in the regression model. The five main factors that contribute online shopping are: preference of consumers towards online shopping, the risk involved in purchasing products through online, time effectiveness in online shopping, difficulties faced during online shopping and getting products from trustworthy websites.

Weather has a profound influence on crop growth, development and yield. The present study deals with the use of weather parameters for sugarcane yield forecasting. Machine learning techniques like K- Nearest Neighbors (KNN) and Random Forest model have been used for sugarcane yield forecasting. Weather parameters namely maximum temperature and minimum temperature, rainfall, relative humidity in the morning and evening, sunshine hours, evaporation along with sugarcane yield have been used as inputs variables. The performance metrics like R2, Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) have been used to select the best model for predicting the yield of the crop. Among the models, Random Forest algorithm is selected as the best fit based on the high R2 and minimum error values. The results indicate that among the weather variables, rainfall and relative humidity in the evening have significant influence on sugarcane yield.

Sugarcane is the largest crop in the world in terms of production. We use sugarcane and its byproducts more and more frequently in our daily lives, which elevates it to the status of a unique crop. As a result, the assessment of sugarcane production is critical since it has a direct impact on a wide range of lives. The yield of sugarcane is predicted using ARIMA and ANN models in this study. The models are based on sugarcane yield data collected over a period of 56 years (1951-2017). Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) have been used to analyze and compare the performance of different models to obtain the best-fit model. The results show that the RMSE and MAPE values of the ANN model are lower than those of the ARIMA model and that the ANN model matches best to this data set.

In this article, we analyzed the morphology of barchans (crescentic sand dunes) using morphometric features of seven components of this landform in the west of the Loot desert near Pashoeyeh village. The components were height, width, length of the right side, length of the left side, length of the right wing, length of the left wing, length of the back slope, length of the slip face, width of the barchan, slope of the windward, slope of the slip face, and area of the barchan. These components were calculated and analyzed for all barchans. To do the calculations, we used the SPSS software. Also, topographic maps on the scales of 1:250000 and 1:50000, aerial photos and Google earth images were used. Barchan morphometry showed that, with a decrease of the slope, the elevation of the slip face reduces too. Our study also showed that the biggest barchan was the fifth one. The reason is the position and oldness of the biggest one. The smallest barchan was formed from a tributary of the main barkhan. It was young and had an asymmetric shape. Most of the barchans in the study area, because of topographic barriers on the right and the prevailing wind direction from north and north west, had asymmetric shapes, and their left arms were greater than their right arms. In terms of correlation, the components of barchans proved to be correlated by more than 95%. This suggests that the different parts of a bachan grow in harmony with one another in line with the wind direction in the area. Also, the size of a barchan is correlated to the growth rate of its components.

Background: This study aimed to compare the cytochrome P450 1A1 (CYP1A1) gene expression in the cord blood of infants born from mothers living in low- and high-air polluted areas. Materials and Methods: The study was conducted in Spring 2012 in Isfahan, the second large and air-polluted city in Iran. The study comprised 60 neonates, consisting of 30 infants born from mothers residing in areas with high levels of air pollution and an equal number of infants born in areas with a lower air pollution level. The umbilical cord blood sample was taken immediately after birth. The relative gene expression levels of CYP1A1 were examined using real time-polymerase chain reaction method. Results: CYP1A1 gene expression level was 3.3-fold higher in the group living in areas with higher pollution level than in the other group (P = 0.01). No significant difference existed in the mean values of maternal age, gestational age, the newborns′ birth weight, and the gender distribution between the two groups. Conclusion: This study provides confirmatory evidence of prenatal health hazards of ambient air pollution and highlights the need for pollution prevention programs to protect women of childbearing age and their children. The clinical implications of this study finding should be confirmed in future longitudinal studies.

This study introduces an optimized hybrid deep learning approach that leverages meteorological data to improve short-term wind energy forecasting in desert regions. Over a year, various machine learning and deep learning models have been tested across different wind speed categories, with multiple performance metrics used for evaluation. Hyperparameter optimization for the LSTM and Conv-Dual Attention Long Short-Term Memory (Conv-DA-LSTM) architectures was performed. A comparison of the techniques indicates that the deep learning methods consistently outperform the classical techniques, with Conv-DA-LSTM yielding the best overall performance with a clear margin. This method obtained the lowest error rates (RMSE: 71.866) and the highest level of accuracy (R 2 : 0.93). The optimization clearly works for higher wind speeds, achieving a remarkable improvement of 22.9%. When we look at the monthly performance, all the months presented at least some level of consistent enhancement (RRMSE reductions from 1.6 to 10.2%). These findings highlight the potential of advanced deep learning techniques in enhancing wind energy forecasting accuracy, particularly in challenging desert environments. The hybrid method developed in this study presents a promising direction for improving renewable energy management. This allows for more efficient resource allocation and improves wind resource predictability., (© 2024. The Author(s).)

Forecasts are valuable to countries to make informed business decisions and develop data-driven strategies. The production of pulses is an integral part of agricultural diversification initiatives because it offers promising economic opportunities to reduce rural poverty and unemployment in developing countries. Pulses are the cheapest source of protein needed for human health. India's pulses production guidelines must be based on accurate and best forecast models. Comparing classical statistical and machine learning models based on different scientific data series is the subject of high-level research today. This study focused on the forecasting behaviour of pulses production for India, Karnataka, Madhya Pradesh, Maharashtra, Rajasthan and Uttar Pradesh. The data series was split into a training dataset (1950-2014) and a testing dataset (2015-2019) for model building and validation purposes, respectively. ARIMA, NNAR and hybrid models were used and compared on training and validation datasets based on goodness of fit (RMSE, MAE and MASE). This research demonstrates that due to the diverse agricultural conditions across different provinces in India, there is no single model that can accurately predict pulse production in all regions. This study's highest accuracy model is ARIMA. ARIMA outperforms NNAR, a machine learning model. Pulse production in India, Rajasthan, and Madhya Pradesh will expand by 26.11%, 12.62%, and 0.51% from 2020 to 2030, whereas it would decline by - 6.5%, - 6.21%, and - 6.76 per cent in Karnataka, Maharashtra, and Uttar Pradesh, respectively. The current forecast results could allow policymakers to develop more aggressive food security and sustainability plans and better Indian pulses production policies in the future., Competing Interests: Conflict of interestThe authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results., (© The Author(s), under exclusive licence to The National Academy of Sciences, India 2023.)

The aim of this paper was to determine the effect of stabilized curcumin nanoemulsions (CUNE) as a food additive capable of directionally acting to inhibit molecules involved in dairy products' quality and digestibility, especially cheese. The objects were cheeses made from the milk of higher grades with addition of a CUNE and a control sample. The cheeses were studied using a scanning electron microscope (SEM) in terms of organoleptic properties, such as appearance, taste, and aroma. The results show that the addition of CUNEs improved the organoleptic properties compared to the control cheese by 150% and improved its shelf life. The SEM study shows that formulation with CUNE promotes the uniform distribution of porosity. The CUNE-based cheese shows a better sensory evaluation compared to the emulsion without curcumin. CUNE-processed cheese provided better antioxidant and antimicrobial analysis than the control sample and offers added value to the dairy sector.

We considered in this work the linear operator equation and used the Landweber iterative method as an iterative solver. After that, we used the multigrid method as an optimization method for obtaining an approximation solution with a highly accurate and fast process. A new parallel algorithm for the multigrid process has been developed. The proposed algorithm is based on a V-cycle mixed with the two-grid method. This modification of the V-cycle provides for parallel computing for each level. A coarse grid operator with a residual right-hand side vector for each coarse grid is provided. This parallel algorithm is used to accelerate and enhance computation for the solution of the iteration method in solving the inverse ill-posed problems. The necessary cost-time computation for all stages and processes for the parallel V-cycle algorithm has been done. A numerical experiment on solving the IVP (initial value problem) for the heat equation showed that the new parallel algorithm is much more efficient than the sequential method.•The study of iteration algorithms and mathematical experiments reveals a slow rate of convergence.•The Multigrid method is often used to speed up the rate of convergence of iterative methods, which is an effective method of solving large systems of linear algebra equations.•The approximation solution for the linear algebra equations was found by using the parallel method with the multigrid method., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

The purpose of this article is to provide an overview of possible solutions to improve the performance of measurement and control processes in maritime engineering applications. This improvement can be basically provided by adopting techniques to enhance the reliability of measurement/control systems based on the 4-20 mA analogue standard. This aspect will be discussed through a Simscape Simulink model illustrating methods of noise and ground loops elimination for pressure measurement of a 4-20 mA current loop in the tank level measurement system on a bulk carrier commercial ship. Alternatively, improved measurement and control processes can be rendered by utilizing smart transmitters based on wired hybrid analogue-digital (Highway Addressable Remote Transducer (HART)), wired digital (Foundation Fieldbus (FF)) or wireless (wireless HART) communication protocols. A brief theoretical description of these protocols will be presented in this article. As an example of using smart transmitters, a simulation-based case study will analyze the possible options to implement non-intrinsically safe as well as intrinsically safe FF models for the tank level measurement system on a bulk carrier commercial ship. Conclusions obtained through analysis of the simulation results will characterize the behavior of FF segments in safe as well as explosive hazardous areas, highlighting the characteristics of field barriers and segment protectors used in conjunction with the HPTC (High-Power Trunk Concept) intrinsically safe model.

Pulses are staple protein-rich food for Indian vegetarians, and India is one of the largest producers in the world. The present investigation is an attempt to study the trend in the production of total pulses in India using the autoregressive integrated moving average (ARIMA) method. For stochastic trend estimation, yearly data were used for the period from 1961 to 2019. On the basis of the performance of several goodness of model fit criteria, the most suitable ARIMA model is chosen to capture the trend of pulse production. Forecasting for the 10 years from 2020 to 2029 is done, and it is observed that India has the highest forecast value (31.03302 million tonnes) in 2029. This study will play an important role in determining the gap between production of and demand for pulses in the future., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors.)

The disease severity of COVID-19, especially in the elderly and patients with co-morbidities, is characterized by hypercytokinemia, an exaggerated immune response associated with an uncontrolled and excessive release of proinflammatory cytokine mediators (cytokine storm). Flavonoids, important secondary metabolites of plants, have long been studied as therapeutic interventions in inflammatory diseases due to their cytokine-modulatory effects. In this review, we discuss the potential role of flavonoids in the modulation of signaling pathways that are crucial for COVID-19 disease, particularly those related to inflammation and immunity. The immunomodulatory ability of flavonoids, carried out by the regulation of inflammatory mediators, the inhibition of endothelial activation, NLRP3 inflammasome, toll-like receptors (TLRs) or bromodomain containing protein 4 (BRD4), and the activation of the nuclear factor erythroid-derived 2-related factor 2 (Nrf2), might be beneficial in regulating the cytokine storm during SARS-CoV-2 infection. Moreover, the ability of flavonoids to inhibit dipeptidyl peptidase 4 (DPP4), neutralize 3-chymotrypsin-like protease (3CL pro ) or to affect gut microbiota to maintain immune response, and the dual action of angiotensin-converting enzyme 2 (ACE-2) may potentially also be applied to the exaggerated inflammatory responses induced by SARS-CoV-2. Based on the previously proven effects of flavonoids in other diseases or on the basis of newly published studies associated with COVID-19 (bioinformatics, molecular docking), it is reasonable to assume positive effects of flavonoids on inflammatory changes associated with COVID-19. This review highlights the current state of knowledge of the utility of flavonoids in the management of COVID-19 and also points to the multiple biological effects of flavonoids on signaling pathways associated with the inflammation processes that are deregulated in the pathology induced by SARS-CoV-2. The identification of agents, including naturally occurring substances such as flavonoids, represents great approach potentially utilizable in the management of COVID-19. Although not clinically investigated yet, the applicability of flavonoids against COVID-19 could be a promising strategy due to a broad spectrum of their biological activities., (Copyright © 2021 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Metastasis represents a major obstacle in cancer treatment and the leading cause of cancer-related deaths. Therefore, the identification of compounds targeting the multi-step and complex process of metastasis could improve outcomes in the management of cancer patients. Carotenoids are naturally occurring pigments with a plethora of biological activities. Carotenoids exert a potent anti-cancer capacity in various cancer models in vitro and in vivo, mediated by the modulation of signaling pathways involved in the migration and invasion of cancer cells and metastatic progression, including key regulators of the epithelial-mesenchymal transition and regulatory molecules, such as matrix metalloproteinases (MMPs), tissue inhibitors of metalloproteinases (TIMPs), urokinase plasminogen activator (uPA) and its receptor (uPAR), hypoxia-inducible factor-1α (HIF-1α), and others. Moreover, carotenoids modulate the expression of genes associated with cancer progression and inflammatory processes as key mediators of the complex process involved in metastasis. Nevertheless, due to the predominantly preclinical nature of the known anti-tumor effects of carotenoids, and unclear results from certain carotenoids in specific cancer types and/or specific parts of the population, a precise analysis of the anti-cancer effects of carotenoids is essential. The identification of carotenoids as effective compounds targeting the complex process of cancer progression could improve the outcomes of advanced cancer patients.

Purpose: The formation of new blood vessels from previous ones, angiogenesis, is critical in tissue repair, expansion or remodeling in physiological processes and in various pathologies including cancer. Despite that, the development of anti-angiogenic drugs has great potential as the treatment of cancer faces many problems such as development of the resistance to treatment or an improperly selected therapy approach. An evaluation of predictive markers in personalized medicine could significantly improve treatment outcomes in many patients., Methods: This comprehensive review emphasizes the anticancer potential of flavonoids mediated by their anti-angiogenic efficacy evaluated in current preclinical and clinical cancer research., Results and Conclusion: Flavonoids are important groups of phytochemicals present in common diet. Flavonoids show significant anticancer effects. The anti-angiogenic effects of flavonoids are currently a widely discussed topic of preclinical cancer research. Flavonoids are able to regulate the process of tumor angiogenesis through modulation of signaling molecules such as VEGF, MMPs, ILs, HIF or others. However, the evaluation of the anti-angiogenic potential of flavonoids within the clinical studies is not frequently discussed and is still of significant scientific interest.

The Warburg effect is characterised by increased glucose uptake and lactate secretion in cancer cells resulting from metabolic transformation in tumour tissue. The corresponding molecular pathways switch from oxidative phosphorylation to aerobic glycolysis, due to changes in glucose degradation mechanisms known as the 'Warburg reprogramming' of cancer cells. Key glycolytic enzymes, glucose transporters and transcription factors involved in the Warburg transformation are frequently dysregulated during carcinogenesis considered as promising diagnostic and prognostic markers as well as treatment targets. Flavonoids are molecules with pleiotropic activities. The metabolism-regulating anticancer effects of flavonoids are broadly demonstrated in preclinical studies. Flavonoids modulate key pathways involved in the Warburg phenotype including but not limited to PKM2, HK2, GLUT1 and HIF-1. The corresponding molecular mechanisms and clinical relevance of 'anti-Warburg' effects of flavonoids are discussed in this review article. The most prominent examples are provided for the potential application of targeted 'anti-Warburg' measures in cancer management. Individualised profiling and patient stratification are presented as powerful tools for implementing targeted 'anti-Warburg' measures in the context of predictive, preventive and personalised medicine., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s) 2020.)

Metastasis represents a serious complication in the treatment of cancer. Flavonoids are plant secondary metabolites exerting various health beneficiary effects. The effects of flavonoids against cancer are associated not only with early stages of the cancer process, but also with cancer progression and spread into distant sites. Flavonoids showed potent anti-cancer effects against various cancer models in vitro and in vivo, mediated via regulation of key signaling pathways involved in the migration and invasion of cancer cells and metastatic progression, including key regulators of epithelial-mesenchymal transition or regulatory molecules such as MMPs, uPA/uPAR, TGF-β and other contributors of the complex process of metastatic spread. Moreover, flavonoids modulated also the expression of genes associated with the progression of cancer and improved inflammatory status, a part of the complex process involved in the development of metastasis. Flavonoids also documented clear potential to improve the anti-cancer effectiveness of conventional chemotherapeutic agents. Most importantly, flavonoids represent environmentally-friendly and cost-effective substances; moreover, a wide spectrum of different flavonoids demonstrated safety and minimal side effects during long-termed administration. In addition, the bioavailability of flavonoids can be improved by their conjugation with metal ions or structural modifications by radiation. In conclusion, anti-cancer effects of flavonoids, targeting all phases of carcinogenesis including metastatic progression, should be implemented into clinical cancer research in order to strengthen their potential use in the future targeted prevention and therapy of cancer in high-risk individuals or patients with aggressive cancer disease with metastatic potential.

Globally, cancer is the second leading cause of death. Different conventional approaches to treat cancer include chemotherapy or radiotherapy. However, these are usually associated with various deleterious effects and numerous disadvantages in clinical practice. In addition, there are increasing concerns about drug resistance. In the continuous search for safer and more effective treatments, plant-derived natural compounds are of major interest. Plant phenolics are secondary metabolites that have gained importance as potential anti-cancer compounds. Phenolics display a great prospective as cytotoxic anti-cancer agents promoting apoptosis, reducing proliferation, and targeting various aspects of cancer (angiogenesis, growth and differentiation, and metastasis). Phenolic acids are a subclass of plant phenolics, furtherly divided into benzoic and cinnamic acids, that are associated with potent anticancer abilities in various in vitro and in vivo studies. Moreover, the therapeutic activities of phenolic acids are reinforced by their role as epigenetic regulators as well as supporters of adverse events or resistance associated with conventional anticancer therapy. Encapsulation of phyto-substances into nanocarrier systems is a challenging aspect concerning the efficiency of natural substances used in cancer treatment. A summary of phenolic acids and their effectiveness as well as phenolic-associated advances in cancer treatment will be discussed in this review., Competing Interests: “The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the article”.

Diabetes mellitus (DM) is a prevailing global health metabolic disorder, with an alarming incidence rate and a huge burden on health care providers. DM is characterized by the elevation of blood glucose due either to a defect in insulin synthesis, secretion, binding to receptor, or an increase of insulin resistance. The internal and external factors such as obesity, urbanizations, and genetic mutations could increase the risk of developing DM. Flavonoids are phenolic compounds existing as secondary metabolites in fruits and vegetables as well as fungi. Their structure consists of 15 carbon skeletons and two aromatic rings (A and B) connected by three carbon chains. Flavonoids are furtherly classified into 6 subclasses: flavonols, flavones, flavanones, isoflavones, flavanols, and anthocyanidins. Naturally occurring flavonoids possess anti-diabetic effects. As in vitro and animal model's studies demonstrate, they have the ability to prevent diabetes and its complications. The aim of this review is to summarize the current knowledge addressing the antidiabetic effects of dietary flavonoids and their underlying molecular mechanisms on selected pathways: Glucose transporter, hepatic enzymes, tyrosine kinase inhibitor, AMPK, PPAR, and NF-κB. Flavonoids improve the pathogenesis of diabetes and its complications through the regulation of glucose metabolism, hepatic enzymes activities, and a lipid profile. Most studies illustrate a positive role of specific dietary flavonoids on diabetes, but the mechanisms of action and the side effects need more clarification. Overall, more research is needed to provide a better understanding of the mechanisms of diabetes treatment using flavonoids., Competing Interests: The authors declare no conflicts of interest.

Cancer is the second leading cause of death globally. Although, there are many different approaches to cancer treatment, they are often painful due to adverse side effects and are sometimes ineffective due to increasing resistance to classical anti-cancer drugs or radiation therapy. Targeting delayed/inhibited apoptosis is a major approach in cancer treatment and a highly active area of research. Plant derived natural compounds are of major interest due to their high bioavailability, safety, minimal side effects and, most importantly, cost effectiveness. Flavonoids have gained importance as anti-cancer agents and have shown great potential as cytotoxic anti-cancer agents promoting apoptosis in cancer cells. In this review, a summary of flavonoids and their effectiveness in cancer treatment targeting apoptosis has been discussed.

Among the different types of breast cancers, triple-negative breast cancers (TNBCs) are highly aggressive, do not respond to conventional hormonal/human epidermal growth factor receptor 2 (HER2)-targeted interventions due to the lack of the respective receptor targets, have chances of early recurrence, metastasize, tend to be more invasive in nature, and develop drug resistance. The global burden of TNBCs is increasing regardless of the number of cytotoxic drugs being introduced into the market each year as they have only moderate efficacy and/or unforeseen side effects. Therefore, the demand for more efficient therapeutic interventions, with reduced side effects, for the treatment of TNBCs is rising. While some plant metabolites/derivatives actually induce the risk of cancers, many plant-derived active principles have gained attention as efficient anticancer agents against TNBCs, with fewer adverse side effects. Here we discuss the possible oncogenic molecular pathways in TNBCs and how the purified plant-derived natural compounds specifically target and modulate the genes and/or proteins involved in these aberrant pathways to exhibit their anticancer potential. We have linked the anticancer potential of plant-derived natural compounds (luteolin, chalcones, piperine, deguelin, quercetin, rutin, fisetin, curcumin, resveratrol, and others) to their ability to target multiple dysregulated signaling pathways (such as the Wnt/β-catenin, Notch, NF-κB, PI3K/Akt/mammalian target of rapamycin (mTOR), mitogen-activated protein kinase (MAPK) and Hedgehog) leading to suppression of cell growth, proliferation, migration, inflammation, angiogenesis, epithelial-mesenchymal transition (EMT) and metastasis, and activation of apoptosis in TNBCs. Plant-derived compounds in combination with classical chemotherapeutic agents were more efficient in the treatment of TNBCs, possibly with lesser side effects.

Breast cancer is the second greatest cause of death among women worldwide; it comprises a group of heterogeneous diseases that evolves due to uncontrolled cellular growth and differentiation and the loss of normal programmed cell death. There are different molecular sub-types of breast cancer; therefore, various options are selected for treatment of different forms of metastatic breast cancer. However, the use of chemotherapeutic drugs is usually accompanied by deleterious side effects and the development of drug resistance when applied for a longer period. This review offers a classification of these chemotherapeutic agents according to their modes of action and therefore improves the understanding of molecular targets that are affected during treatment. Overall, it will allow the clinician to identify more specific targets to increase the effectiveness of a drug and to reduce general toxicity, resistance and other side effects., (Copyright © 2018 Elsevier Masson SAS. All rights reserved.)