Diet plays a key role to maintaining healthy life. Many natural products present in our diet, such as flavonoids, can prevent the progression of cancer. Quercetin, a distinctive bioactive flavonoid, ...is a dietary component that has attracted the attention of dietitians and medicinal chemists due to its numerous health‐promoting effects. It is an outstanding antioxidant that has a well‐documented role in reducing different human cancers. Quercetin exhibits direct proapoptotic effects on tumor cells and thus can inhibit the progress of numerous human cancers. The anticancer effect of quercetin has been documented in numerous in vitro and in vivo studies that involved several cell lines and animal models. On the other hand, the high toxic effect of quercetin against cancer cells is accompanied with little or no side effects or harm to normal cells. Accordingly, this review presents an overview of recent developments on the use of quercetin against different types of cancer along with mechanisms of action. In addition, the present review summarizes the literature pertaining to quercetin as an anticancer agent and provides an assessment of the potential utilization of this natural compound as a complimentary or alternative medicine for preventing and treating cancer.
This study empirically investigates the relationship between CO2 emission and four of its potentially contributing factors (i.e., energy consumption, income, trade openness and population) using time ...series data from 1971 to 2013 on five selected economies of South Asia. After confirming that all the series are stationary using unit root test process, the study incorporates three different and advance panel cointegration tests i.e. Pedroni- Kao- and Johansen-Fisher-panel cointegration. All the panel cointegration tests confirm that all the variables cointegrated. The long-run association between the variables is checked using FMOLS-grouped and individual cross-section country in the panel. The FMOLS grouped results show that energy consumption, trade openness and population increases environmental degradation in the panel countries with exception of income which has negative impact and sounds the existence of Environmental Kuznet curve between income and emission. The innovative accounting approach using variance decomposition test and impulse response function is applied to examine the causality amongst the underlined vectors. The results show that there is bidirectional causality between energy consumption and trade openness and uni-directional causality running from energy consumption, trade openness and population to CO2 emission. The results enumerate that the energy consumption and population density will increase in long-run and foresee further environmental degradation in the region.
A review: Mechanism of action of antiviral drugs Kausar, Shamaila; Said Khan, Fahad; Ishaq Mujeeb Ur Rehman, Muhammad ...
International journal of immunopathology and pharmacology,
2021, Letnik:
35
Journal Article
Recenzirano
Odprti dostop
Antiviral drugs are a class of medicines particularly used for the treatment of viral infections. Drugs that combat viral infections are called antiviral drugs. Viruses are among the major pathogenic ...agents that cause number of serious diseases in humans, animals and plants. Viruses cause many diseases in humans, from self resolving diseases to acute fatal diseases. Developing strategies for the antiviral drugs are focused on two different approaches: Targeting the viruses themselves or the host cell factors. Antiviral drugs that directly target the viruses include the inhibitors of virus attachment, inhibitors of virus entry, uncoating inhibitors, polymerase inhibitors, protease inhibitors, inhibitors of nucleoside and nucleotide reverse transcriptase and the inhibitors of integrase. The inhibitors of protease (ritonavir, atazanavir and darunavir), viral DNA polymerase (acyclovir, tenofovir, valganciclovir and valacyclovir) and of integrase (raltegravir) are listed among the Top 200 Drugs by sales during 2010s. Still no effective antiviral drugs are available for many viral infections. Though, there are a couple of drugs for herpesviruses, many for influenza and some new antiviral drugs for treating hepatitis C infection and HIV. Action mechanism of antiviral drugs consists of its transformation to triphosphate following the viral DNA synthesis inhibition. An analysis of the action mechanism of known antiviral drugs concluded that they can increase the cell’s resistance to a virus (interferons), suppress the virus adsorption in the cell or its diffusion into the cell and its deproteinisation process in the cell (amantadine) along with antimetabolites that causes the inhibition of nucleic acids synthesis. This review will address currently used antiviral drugs, mechanism of action and antiviral agents reported against COVID-19.
An important class of fractional differential and integral operators is given by the theory of fractional calculus with respect to functions, sometimes called Ψ‐fractional calculus. The operational ...calculus approach has proved useful for understanding and extending this topic of study. Motivated by fractional differential equations, we present an operational calculus approach for Laplace transforms with respect to functions and their relationship with fractional operators with respect to functions. This approach makes the generalised Laplace transforms much easier to analyse and to apply in practice. We prove several important properties of these generalised Laplace transforms, including an inversion formula, and apply it to solve some fractional differential equations, using the operational calculus approach for efficient solving.
Breast cancer is one of the worst illnesses, with a higher fatality rate among women globally. Breast cancer detection needs accurate mammography interpretation and analysis, which is challenging for ...radiologists owing to the intricate anatomy of the breast and low image quality. Advances in deep learning-based models have significantly improved breast lesions' detection, localization, risk assessment, and categorization. This study proposes a novel deep learning-based convolutional neural network (ConvNet) that significantly reduces human error in diagnosing breast malignancy tissues. Our methodology is most effective in eliciting task-specific features, as feature learning is coupled with classification tasks to achieve higher performance in automatically classifying the suspicious regions in mammograms as benign and malignant. To evaluate the model's validity, 322 raw mammogram images from Mammographic Image Analysis Society (MIAS) and 580 from Private datasets were obtained to extract in-depth features, the intensity of information, and the high likelihood of malignancy. Both datasets are magnificently improved through preprocessing, synthetic data augmentation, and transfer learning techniques to attain the distinctive combination of breast tumors. The experimental findings indicate that the proposed approach achieved remarkable training accuracy of 0.98, test accuracy of 0.97, high sensitivity of 0.99, and an AUC of 0.99 in classifying breast masses on mammograms. The developed model achieved promising performance that helps the clinician in the speedy computation of mammography, breast masses diagnosis, treatment planning, and follow-up of disease progression. Moreover, it has the immense potential over retrospective approaches in consistency feature extraction and precise lesions classification.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The proposed work delves into the utilization of chaotic maps along with the quantum mechanisms to strengthen the security of the digital images along with addressing the limitations posed by ...conventional 1-D chaotic systems characterized by their pseudorandom and periodic attributes. Leveraging the inherent uncertainty in quantum theory, the proposed research introduces a new image encryption scheme. This method is built upon the concepts of two-dimensional quantum coding and the 1-D sine-based chaotic map (1-D SBCM). In the proposed encryption scheme, initially, a random sequence is created using 1-D SBCM by varying the seed parameters. This sequence is subsequently utilized for the purpose of scrambling. After that, a pseudorandom number generator (PRNG) is meticulously devised, drawing inspiration from the concepts of quantum coherence. This PRNG generates a confidential code stream that defies predictability and remains incongruent with the plaintext image, thereby contributing to heightened security levels. Subsequently, the research employs the novel enhanced quantum representation (NEQR) model, capitalizing on its advanced capabilities. Within this framework, the study introduces a quantum right cyclic shift operator, as well as a quantum XOR operator. These operators play a vital role in the formation of highly robust encrypted images. The application of these quantum operators not only enhances the security measures but also increases the complexity of the encryption procedure Statistical evidence derived from comprehensive experimentation corroborates the efficacy of the proposed image encryption scheme. Through rigorous analysis, it becomes evident that the system exhibits a robust performance in terms of strong security. The integration of quantum principles and quantum coding demonstrates that the proposed encryption scheme is resilient against potential threats.
The volatility and uncertainty of wind power often affect the quality of electric energy, the security of the power grid, the stability of the power system, and the fluctuation of the power market. ...In this case, the research on wind power forecasting is of great significance for ensuring the better development of wind power grids and the higher quality of electric energy. Therefore, a lot of new forecasting methods have been put forward. In this paper, a new forecasting model based on a convolution neural network and LightGBM is constructed. The procedure is shown as follows. First, we construct new feature sets by analyzing the characteristics of the raw data on the time series from the wind field and adjacent wind field. Second, the convolutional neural network (CNN) is proposed to extract information from input data, and the network parameters are adjusted by comparing the actual results. Third, in consideration of the limitations of the single-convolution model in predicting wind power, we innovatively integrated the LightGBM classification algorithm at the model to improve the forecasting accuracy and robustness. Finally, compared with the existing support vector machines, LightGBM, and CNN, the fusion model has better performance in accuracy and efficiency.
A little over a year after the official announcement from the WHO, the COVID-19 pandemic has led to dramatic consequences globally. Today, millions of doses of vaccines have already been administered ...in several countries. However, the positive effect of these vaccines will probably be seen later than expected. In these circumstances, the rapid diagnosis of COVID-19 still remains the only way to slow the spread of this virus. However, it is difficult to predict whether a person is infected or not by COVID-19 while relying only on apparent symptoms. In this context, we propose to use machine learning (ML) algorithms in order to diagnose COVID-19 infected patients more effectively. The proposed diagnosis method takes into consideration several symptoms, such as flu symptoms, throat pain, immunity status, diarrhea, voice type, body temperature, joint pain, dry cough, vomiting, breathing problems, headache, and chest pain. Based on these symptoms that are modelled as ML features, our proposed method is able to predict the probability of contamination with the COVID-19 virus. This method is evaluated using different experimental analysis metrics such as accuracy, precision, recall, and F1-score. The obtained experimental results have shown that the proposed method can predict the presence of COVID-19 with over 97% accuracy.
This paper aims to develop a numerical method for the solutions of Hadamard fractional differential equations. We introduced Hadamard fractional Legendre functions by modifying classical Legendre ...polynomials and used them to approximate the numerical solutions of linear and nonlinear Hadamard fractional differential equations. The solution is approached by approximating the appropriate terms in Hadamard fractional differential equations using Hadamard fractional Legendre functions and converting the problem to a system of algebraic equations. Quasilinearization technique is employed to linearize the nonlinear Hadamard fractional differential equations. An upper bound for approximation error is derived. This method provides reasonably accurate results for a relatively smaller order of Hadamard fractional Legendre functions.
This paper introduces a numerical approach by generalizing Legendre wavelets for solving nonlinear Caputo–Hadamard fractional differential equations. The methodology involves the extension of ...classical Legendre wavelets, namely the generalized Legendre wavelets (gLWs), along with the development of operational matrices for Hadamard fractional integration and Caputo–Hadamard fractional differentiation. The proposed method combines the gLWs with the Adomian decomposition method to address the nonlinearities inherent in fractional equations through Adomian polynomials. A detailed methodology is presented for applying the proposed method to nonlinear Caputo–Hadamard fractional differential equations, accompanied by error analysis and numerical simulations to demonstrate its reliability and accuracy.
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