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Garlic ( Allium sativum L.) possesses numerous pharmacological potential, including antibacterial, antiarthritic, antithrombotic, anticancer, hypoglycemic, and hypolipidemic effects. The anti-cancer action of garlic is likely the best researched of the many advantageous pharmacological effects, and its use offers significant protection against the risk of developing cancer. A few active metabolites of garlic have been reported to be essential in the destruction of malignant cells due to their multi-targeted activities and lack of significant toxicity. The bioactive compounds in garlic having anticancer properties include diallyl trisulfide, allicin, allyl mercaptan diallyl disulfide, and diallyl sulphide. Different garlic-derived constituents and their nanoformulations have been tested for their effects against various cancers including skin, ovarian, prostate, gastric, breast, and lung, colorectal, liver, oral, and pancreatic cancer. The objective of this review is to summarize the antitumor activity and associated mechanisms of the organosulfur compounds of garlic in breast carcinoma. Breast cancer continues to have a significant impact on the total number of cancer deaths worldwide. Global measures are required to reduce its growing burden, particularly in developing nations where incidence is increasing quickly and fatality rates are still high. It has been demonstrated that garlic extract, its bioactive compounds, and their use in nanoformulations can prevent breast cancer in all of its stages, including initiation, promotion, and progression. Additionally, these bioactive compounds affect cell signaling for cell cycle arrest and survival along with lipid peroxidation, nitric oxide synthase activity, epidermal growth factor receptor, nuclear factor kappa B (NF-κB), and protein kinase C in breast carcinoma. Hence, this review deciphers the anticancer potential of garlic components and its nanoformulations against several breast cancer thereby projecting it as a potent drug candidate for efficient breast cancer management.

This paper investigates the impact of life expectancy on carbon emission, in Saudi Arabia. Additionally, we examined the role of governance to achieve carbon neutrality status. We used the novel dynamic ARDL technique for estimations. This is one of the pioneer studies that analyze the role of life expectancy to control carbon emissions. The coefficients of life expectancy, education, and political stability are significantly negative. On contrary, governance effectiveness is an obstacle to achieving carbon neutrality. Empirical findings of life expectancy and governance effectiveness are quite surprising. In terms of Vision 2030 estimations, the coefficient of corruption control is significant and negative, indicating that the Saudi government has prioritized corruption control. While governance effectiveness remains positive, the Saudi government still requires governance reforms in order to achieve carbon neutrality goals.

SARS-CoV-2 is a positive-stranded RNA virus that bundles its genomic material as messenger-sense RNA in infectious virions and replicates these genomes through RNA intermediates. Several virus-encoded nonstructural proteins play a key role during the viral life cycle. Endoribonuclease NSP15 is vital for the replication and life cycle of the virus, and is thus considered a compelling druggable target. Here, we performed a combination of multiscoring virtual screening and molecular docking of a library of 1624 natural compounds (Nuclei of Bioassays, Ecophysiology and Biosynthesis of Natural Products (NuBBE) database) on the active sites of NSP15 (PDB:6VWW). After sequential high-throughput screening by LibDock and GOLD, docking optimization by CDOCKER, and final scoring by calculating binding energies, top-ranked compounds NuBBE-1970 and NuBBE-242 were further investigated via an indepth molecular-docking and molecular-dynamics simulation of 60 ns, which revealed that the binding of these two compounds with active site residues of NSP15 was sufficiently strong and stable. The findings strongly suggest that further optimization and clinical investigations of these potent compounds may lead to effective SARS-CoV-2 treatment.

Considering the urgency of the COVID-19 pandemic, we developed a receptor-based pharmacophore model for identifying FDA-approved drugs and hits from natural products. The COVID-19 main protease (Mpro) was selected for the development of the pharmacophore model. The model consisted of a hydrogen bond acceptor, donor, and hydrophobic features. These features demonstrated good corroboration with a previously reported model that was used to validate the present model, showing an RMSD value of 0.32. The virtual screening was carried out using the ZINC database. A set of 208,000 hits was extracted and filtered using the ligand pharmacophore mapping, applying the lead-like properties. Lipinski’s filter and the fit value filter were used to minimize hits to the top 2000. Simultaneous docking was carried out for 200 hits for natural drugs belonging to the FDA-approved drug database. The top 28 hits from these experiments, with promising predicted pharmacodynamic and pharmacokinetic properties, are reported here. To optimize these hits as Mpro inhibitors and potential treatment options for COVID-19, bench work investigations are needed.

Large interconnected power systems are usually subjected to natural oscillation (NO) and forced oscillation (FO). NO occurs due to system transient response and is characterized by several oscillation modes, while FO occurs due to external perturbations driving generation sources. Compared to NO, FO is considered a more severe threat to the safe and reliable operation of power systems. Therefore, it is important to locate the source of FO so corrective actions can be taken to ensure stable power system operation. In this paper, a novel approach based on two-step signal processing is proposed to characterize FO in terms of its frequency components, duration, nature, and the location of the source. Data recorded by the Phasor Measurement Units (PMUs) in a Wide Area Monitoring System (WAMS) is utilized for analysis. As PMU data usually contains white noise and appears as multi-frequency oscillatory signal, the first step is to de-noise the raw PMU data by decomposing it into a series of intrinsic mode functions (IMF) using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) technique. The most appropriate IMF containing the vital information is selected using the correlation technique. The second step involves various signal processing and statistical analysis tools such as segmented Power Spectrum Density (PSD), excess kurtosis, cross PSD etc. to achieve the desired objectives. The analysis performed on the simulated two-area four-machine system, reduced WECC-179 bus 29 machine system, and the real-time power system PMU data set from ISO New England, demonstrates the accuracy of the proposed method. The proposed approach is independent of complex network topologies and their characteristics, and is also robust against measurement noise usually contained in PMU data.

In this study, the antioxidant and anti-inflammatory effects of Zingiber officinale roscoe and Allium subhirsutum aqueous extracts were examined in a carrageenan-induced acute inflammation model. Some markers of inflammation such as hematological parameters, fibrinogen and C-reactive protein were measured. Variables reflecting oxidative stress included thiobarbituric acid reactive substances (TBARS), advanced oxidation of protein products (AOPP), superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx) and glutathione were determined in both inflamed foci and erythrocytes. The in silico molecular docking simulation showed that the main components of Zingiber officinale roscoe and Allium subhirsutum bound to toll-like receptor 6 (TLR6) with high affinities. Moreover, histological examinations of paw edema were carried out. Both Zingiber officinale roscoe and Allium subhirsutum ameliorated the induced inflammation and oxidative stress status as outlined by anti-edematous, antioxidant and anti-inflammatory activities. Our investigation lends pharmacological support to the medical uses of these spices in the management of inflammatory disorders and oxidative damage. The results of the in silico assay satisfactory explain the in vivo effects as compared with indomethacin.

Worldwide, since ages and nowadays, traditional medicine is well known, owing to its biodiversity, which immensely contributed to the advancement and development of complementary and alternative medicines. There is a wide range of spices, herbs, and trees known for their medicinal uses. Chilli peppers, a vegetable cum spice crop, are bestowed with natural bioactive compounds, flavonoids, capsaicinoids, phytochemicals, phytonutrients, and pharmacologically active compounds with potential health benefits. Such compounds manifest their functionality over solo-treatment by operating in synergy and consortium. Co-action of these compounds and nutrients make them potentially effective against coagulation, obesity, diabetes, inflammation, dreadful diseases, such as cancer, and microbial diseases, alongside having good anti-oxidants with scavenging ability to free radicals and oxygen. In recent times, capsaicinoids especially capsaicin can ameliorate important viral diseases, such as SARS-CoV-2. In addition, capsaicin provides an ability to chilli peppers to ramify as topical agents in pain-relief and also benefitting man as a potential effective anesthetic agent. Such phytochemicals involved not only make them useful and a much economical substitute to wonder/artificial drugs but can be exploited as obscene drugs for the production of novel stuffs. The responsibility of the TRPV1 receptor in association with capsaicin in mitigating chronic diseases has also been justified in this study. Nonetheless, medicinal studies pertaining to consumption of chilli peppers are limited and demand confirmation of the findings from animal studies. In this artifact, an effort has been made to address in an accessible format the nutritional and biomedical perspectives of chilli pepper, which could precisely upgrade and enrich our pharmaceutical industries towards human well-being.

Non-enzymatic glycation is the addition of free carbonyl group of reducing sugar to the free amino groups of proteins, resulting in the formation of a Schiff base and an Amadori product. Dihydroxyacetone (DHA) is one of the carbonyl species which reacts rapidly with the free amino groups of proteins to form advanced glycation end products (AGEs). The highly reactive dihydroxyacetone phosphate is a derivative of dihydroxyacetone (DHA), and a product of glycolysis, having potential glycating effects to form AGEs. The formation of AGEs results in the generation of free radicals which play an important role in the pathophysiology of aging and diabetic complications. While the formation of DHA-AGEs has been demonstrated previously, no extensive studies have been performed to assess the inhibition of AGE inhibitors at all the three stages of glycation (early, intermediate and late) using metformin (MF) and pyridoxamine (PM) as a novel inhibitor. In this study we report glycation of human serum albumin (HSA) & its characterization by various spectroscopic techniques. Furthermore, inhibition of glycation products at all the stages of glycation was also studied. Spectroscopic analysis suggests structural perturbations in the HSA as a result of modification which might be due to generation of free radicals and formation of AGEs. The inhibition in the formation of glycation reaction reveals that Pyridoxamine is a better antiglycating agent than Metformin at all stages of the glycation (early, intermediate and late stages).

It is yet unknown what causes cardiovascular disease (CVD), but we do know that it is associated with a high risk of death, as well as severe morbidity and disability. There is an urgent need for AI-based technologies that are able to promptly and reliably predict the future outcomes of individuals who have cardiovascular disease. The Internet of Things (IoT) is serving as a driving force behind the development of CVD prediction. In order to analyse and make predictions based on the data that IoT devices receive, machine learning (ML) is used. Traditional machine learning algorithms are unable to take differences in the data into account and have a low level of accuracy in their model predictions. This research presents a collection of machine learning models that can be used to address this problem. These models take into account the data observation mechanisms and training procedures of a number of different algorithms. In order to verify the efficacy of our strategy, we combined the Heart Dataset with other classification models. The proposed method provides nearly 96 percent of accuracy result than other existing methods and the complete analysis over several metrics has been analysed and provided. Research in the field of deep learning will benefit from additional data from a large number of medical institutions, which may be used for the development of artificial neural network structures.

Inhalable microparticle-based anti TB drug delivery systems are being investigated extensively for Tuberculosis [TB] treatment as they offer efficient and deep lung deposition with several advantages over conventional routes. It can reduce the drug dose, treatment duration and toxic effects and optimize the drug bioavailability. Yeast derived β-glucan is a β-[1–3/1–6] linked biocompatible polymer and used as carrier for various biomolecules. Due to presence of glucan chains, particulate glucans act as PAMP and thereby gets internalized via receptor mediated phagocytosis by the macrophages. In this study, β-glucan microparticles were prepared by adding l-leucine as excipient, and exhibited 70% drug [Rifabutin] loading efficiency. Further, the sizing and SEM data of particles revealed a size of 2–4 µm with spherical dimensions. The FTIR and HPLC data confirmed the β-glucan composition and drug encapsulations efficiency of the particles. The mass median aerodynamic diameter [MMAD] and geometric standard deviation [GSD] data indicated that these particles are inhalable in nature and have better thermal stability as per DSC thermogram. These particles were found to be non-toxic upto a concentration of 80 µg/ml and were found to be readily phagocytosed by human macrophage cells in-vitro as well as in-vivo by lung alveolar macrophage. This study provides a framework for future design of inhalable β-glucan particle based host-directed drug delivery system against pulmonary TB.