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Inflammation is a complex physiological response associated with the onset and progression of various disorders, including diabetes. In this study, we synthesized a series of diclofenac acid derivatives and evaluated their potential anti-diabetic and anti-inflammatory activities. The compounds were specifically assessed for their ability to inhibit 15-lipoxygenase (15-LOX) and α-glucosidase enzymes. The structures of synthesized derivatives were confirmed through 1H nuclear magnetic resonance (NMR), 13C-NMR and high-resolution mass spectrometry (electron ionization) analysis. All these synthesized derivatives exhibited varying degrees of inhibitory activity against LOX, when compared with standard drugs, compounds 5a (half-maximal inhibitory concentration (IC50) 14 ± 1 µM), 5b (IC50 61 ± 1 µM) and 7c (IC50 67 ± 1 µM) showed good activity against the LOX enzyme. While the α-glucosidase inhibitory results revealed that most of the compounds exhibited significant activity when compared with the standard drug acarbose (376 ± 1 µM). The most potent compounds as α-glucosidase inhibitors were 7b (3 ± 1 µM), 4b (5 ± 1 µM), 7a (7 ± 1 µM) and 8b (11 ± 1 µM). All these active compounds were found to be least toxic and maintained the mononuclear cells viability at 96–97% compared with that of controls as determined by multi-transaction translator assay. Molecular docking studies further reiterated the significance of these ‘lead’ compounds with great potential against the target enzymes in the process of drug discovery.

Abstract Nod-like receptor protein 3 (NLRP-3), is an intracellular sensor that is involved in inflammasome activation, and the aberrant expression of NLRP3 is responsible for diabetes mellitus, its complications, and many other inflammatory diseases. NLRP3 is considered a promising drug target for novel drug design. Here, a pharmacophore model was generated from the most potent inhibitor, and its validation was performed by the Gunner-Henry scoring method. The validated pharmacophore was used to screen selected compounds databases. As a result, 646 compounds were mapped on the pharmacophore model. After applying Lipinski's rule of five, 391 hits were obtained. All the hits were docked into the binding pocket of target protein. Based on docking scores and interactions with binding site residues, six compounds were selected potential hits. To check the stability of these compounds, 100 ns molecular dynamic (MD) simulations were performed. The RMSD, RMSF, DCCM and hydrogen bond analysis showed that all the six compounds formed stable complex with NLRP3. The binding free energy with the MM-PBSA approach suggested that electrostatic force, and van der Waals interactions, played a significant role in the binding pattern of these compounds. Thus, the outcomes of the current study could provide insights into the identification of new potential NLRP3 inflammasome inhibitors against diabetes and its related disorders.

Abstract Lung cancer is a disease with a high mortality rate and it is the number one cause of cancer death globally. Approximately 12–14% of non-small cell lung cancers are caused by mutations in KRASG12C. The KRASG12C is one of the most prevalent mutants in lung cancer patients. KRAS was first considered undruggable. The sotorasib and adagrasib are the recently approved drugs that selectively target KRASG12C, and offer new treatment approaches to enhance patient outcomes however drug resistance frequently arises. Drug development is a challenging, expensive, and time-consuming process. Recently, machine-learning-based virtual screening are used for the development of new drugs. In this study, we performed machine-learning-based virtual screening followed by molecular docking, all atoms molecular dynamics simulation, and binding energy calculations for the identifications of new inhibitors against the KRASG12C mutant. In this study, four machine learning models including, random forest, k-nearest neighbors, Gaussian naïve Bayes, and support vector machine were used. By using an external dataset and 5-fold cross-validation, the developed models were validated. Among all the models the performance of the random forest (RF) model was best on the train/test dataset and external dataset. The random forest model was further used for the virtual screening of the ZINC15 database, in-house database, Pakistani phytochemicals, and South African Natural Products database. A total of 100 ns MD simulation was performed for the four best docking score complexes as well as the standard compound in complex with KRASG12C. Furthermore, the top four hits revealed greater stability and greater binding affinities for KRASG12C compared to the standard drug. These new hits have the potential to inhibit KRASG12C and may help to prevent KRAS-associated lung cancer. All the datasets used in this study can be freely available at ( https://github.com/Amar-Ajmal/Datasets-for-KRAS ).

The NOD-Like Receptor Protein-3 (NLRP3) inflammasome is a key therapeutic target for the treatment of epilepsy and has been reported to regulate inflammation in several neurological diseases. In this study, a machine learning-based virtual screening strategy has investigated candidate active compounds that inhibit the NLRP3 inflammasome. As machine learning-based virtual screening has the potential to accurately predict protein-ligand binding and reduce false positives outcomes compared to traditional virtual screening. Briefly, classification models were created using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) machine learning methods. To determine the most crucial features of a molecule's activity, feature selection was carried out. By utilizing 10-fold cross-validation, the created models were analyzed. Among the generated models, the RF model obtained the best results as compared to others. Therefore, the RF model was used as a screening tool against the large chemical databases. Molecular operating environment (MOE) and PyRx software's were applied for molecular docking. Also, using the Amber Tools program, molecular dynamics (MD) simulation of potent inhibitors was carried out. The results showed that the KNN, SVM, and RF accuracy was 0.911 %, 0.906 %, and 0.946 %, respectively. Moreover, the model has shown sensitivity of 0.82 %, 0.78 %, and 0.86 % and specificity of 0.95 %, 0.96 %, and 0.98 % respectively. By applying the model to the ZINC and South African databases, we identified 98 and 39 compounds, respectively, potentially possessing anti-NLRP3 activity. Also, a molecular docking analysis produced ten ZINC and seven South African compounds that has comparable binding affinities to the reference drug. Moreover, MD analysis of the two complexes revealed that the two compounds (ZINC000009601348 and SANC00225) form stable complexes with varying amounts of binding energy. The in-silico studies indicate that both compounds most likely display their inhibitory effect by inhibiting the NLRP3 protein.

In electrical power system engineering, the economic load dispatch (ELD) problem is a critical issue for fuel cost minimization. This ELD problem is often characterized by non-convexity and subject to multiple constraints. These constraints include valve-point loading effects (VPLEs), generator limits, emissions, and wind power integration. In this study, both emission constraints and wind power are incorporated into the ELD problem formulation, with the influence of wind power quantified using the incomplete gamma function (IGF). This study proposes a novel metaheuristic algorithm, the modified moth flame optimization (MMFO), which improves the traditional moth flame optimization (MFO) algorithm through an innovative flame selection process and adaptive adjustment of the spiral length. MMFO is a population-based technique that leverages the intelligent behavior of flames to effectively search for the global optimum, making it particularly suited for solving the ELD problem. To demonstrate the efficacy of MMFO in addressing the ELD problem, the algorithm is applied to four well-known test systems. Results show that MMFO outperforms other methods in terms of solution quality, speed, minimum fuel cost, and convergence rate. Furthermore, statistical analysis validates the reliability, robustness, and consistency of the proposed optimizer, as evidenced by the consistently low fitness values across iterations.

In the evolution of the modern “More Electric Aircrafts” (MEAs) concept, higher-pulse rectification systems using differential fork autotransformers play a pivotal role. These systems align with the MEAs concept, which aims to replace traditional hydraulic and pneumatic systems with electric alternatives. The reason is that MEAs prioritize weight reduction to enhance fuel efficiency and reduce emissions. Higher-pulse rectifiers achieve this by minimizing losses and optimizing power conversion. Additionally, they mitigate harmonics, ensuring a clean power supply to critical avionic components. These systems also regulate voltage effectively, contributing to overall system stability. Furthermore, their compliance with IEEE-519 standards ensures their safe and efficient operation. Keeping in view the above applications, a comparative study of 12-, 18-, and 24-pulse rectification systems was carried out using different autotransformer topologies. The simulation and hardware results are presented for validation. The voltage and current waveforms were meticulously analyzed for each topology, emphasizing the pivotal role played by the differential fork autotransformer design. By quantifying the total harmonic distortion (THD) levels using MATLAB simulations, intriguing insights were revealed. Notably, the 24-pulse system emerged as the clear winner in harmonic mitigation, showcasing its superior waveform quality and reduced harmonic content. However, the 18-pulse configuration also exhibited a commendable performance, surpassing the 12-pulse counterpart.

Reactive power dispatch (RPD) in electric power systems, integrated with renewable energy sources, is gaining popularity among power engineers because of its vital importance in the planning, designing, and operation of advanced power systems. The goal of RPD is to upgrade the power system performance by minimizing the transmission line losses, enhancing voltage profiles, and reducing the total operating costs by tuning the decision variables such as transformer tap setting, generator’s terminal voltages, and capacitor size. But the complex, non-linear, and dynamic characteristics of the power networks, as well as the presence of power demand uncertainties and non-stationary behavior of wind generation, pose a challenging problem that cannot be solved efficiently with traditional numerical techniques. In this study, a new fractional computing strategy, namely, fractional hybrid particle swarm optimization (FHPSO), is proposed to handle RPD issues in electric networks integrated with wind power plants (WPPs) while incorporating the power demand uncertainties. To improve the convergence characteristics of the Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), the proposed FHPSO incorporates the concepts of Shannon entropy inside the mathematical model of traditional PSOGSA. Extensive experimentation validates FHPSO effectiveness by computing the best value of objective functions, namely, voltage deviation index and line loss minimization in standard power systems. The proposed FHPSO shows an improvement in percentage of 61.62%, 85.44%, 86.51%, 93.15%, 84.37%, 67.31%, 61.64%, 61.13%, 8.44%, and 1.899%, respectively, over ALC_PSO, FAHLCPSO, OGSA, ABC, SGA, CKHA, NGBWCA, KHA, PSOGSA, and FPSOGSA in case of traditional optimal reactive power dispatch(ORPD) for IEEE 30 bus system. Furthermore, the stability, robustness, and precision of the designed FHPSO are determined using statistical interpretations such as cumulative distribution function graphs, quantile-quantile plots, boxplot illustrations, and histograms.

This research presents a novel methodology for tackling the combined thermal-wind economic load dispatch (ELD) issue in contemporary power system. The proposed approach involves hybridizing active-set algorithm (ASA), interior point algorithm (IPA) and sequential quadratic programming (SQP) into grey wolf optimization (GWO) algorithm, while effectively incorporating the intricacies associated with renewable energy sources (RES). A more accurate model is made possible by hybridization for complex systems with memory and hereditary characteristics. The GWO is used as a tool for global search while ASA, IPA and SQP methods are used for rapid local optimization mechanism. The performance evaluation of the design heuristics is carried out on 37 thermal and 3 wind power generating units and outcomes endorse the effectiveness of the proposed scheme over state-of-the-art counterparts. The worthy performance is further validated on statistical assessments in case of thermal-wind integrated ELD problem in terms of measure of central tendency and variation on cost and complexity indices.

The integration of Distributed Energy Resources (DERs) into distribution grids has become increasingly feasible and sustainable due to the development of microgrids. However, the development of an effective protection strategy remains a challenge in the implementation of microgrids. To address this challenge, this paper presents a simple and novel microgrid protection method based on superimposed components, Wigner distribution function (WDF) and alienation index-based. The proposed method develops a new fault detection index (FDI) by applying the alienation coefficient and WDF on a superimposed current signal to detect faulty events in the microgrid. The scheme is inherently phase segregated because the FDI is obtained for each phase individually. In addition, the proposed strategy introduces a new fault zone identification method based on the superimposed positive sequence reactive power (SPSQ). After obtaining the complete fault information, a relevant trip signal is generated to isolate the faulty section from the rest of the grid. The proposed methodology is evaluated through simulations using MATLAB/SIMULINK software. Various fault types, with varying parameters are simulated to validate the proposed approach. The results indicate that the proposed methodology is capable of recognizing, classifying, and locating all fault types in both grid-connected and islanded modes of operation.

The modern power generation systems are increasing their reliance on high penetrations of distributed energy resources (DERs). However, the optimal dispatching mechanisms mainly rely on central controls which receive the load demand information from the electricity utility providers and allocate the electricity production targets to participating generating units. The lack of transparency and control over the DER fuel inputs makes the physical power purchase agreements (PPAs) a cumbersome task. This research work proposes an innovative fractal moth flame optimization (FMFO) approach to tackle the problem of integrated load dispatch (ILD). The proposed methodology provides a mechanism to integrate the information of the proposed optimizer, i.e., FMFO into the smart contracts enabled by the blockchain technology. This problem entails the allocation of loads to power-generating units in a manner that minimizes the total generation cost in a decentralized manner. To improve the efficiency of dispatch operations in the presence of a substantial integration of wind energy, this study proposes a novel framework based on the principles of fractal heritage, drawing inspiration from the classical MFO method. To assess the effectiveness and adaptability of the algorithm suggested, various non-convex scenarios in the context of optimization for ILD are considered. These scenarios incorporate valve-point loading effects (VPLEs), capacity limitations, power plants with multiple fuel options, and the presence of stochastic wind (SW) power uncertainty, following a Weibull distribution. The findings demonstrate exceptional performance in terms of minimizing fuel generation costs compared to traditional algorithms.

Pistacia chinensis is locally practiced for treating diabetes, pain, inflammation, and erectile dysfunction. Therefore, the current studies subjected the crude extract/fractions and the isolated compound (2-(3,4-dihydroxyphenyl)-7,8-dihydroxy-3-methoxy-4H-chromen-4-one) to α‐glucosidase inhibitor and anti-glycation activities. The development of long-term complications associated with diabetes is primarily caused by chronic hyperglycemia. Regarding α‐glucosidase, the most significant inhibitory effect was observed with compound 1 (93.09%), followed by the methanolic extract (80.87%) with IC50 values of 45.86 and 86.32 μM. The maximum anti-glycation potential was shown by an isolated compound 1 followed by methanolic extract with effect inhibition of 90.12 and 72.09, respectively. Compound 1 is expected to have the highest gastrointestinal absorption rate, with a predicted absorption rate of 86.156%. This indicates oral suitability. The compound 1 is expected to have no harmful effects on the liver. In addition, our docking results suggest that alpha-glucosidase and isolated compounds showed strong interaction with ILE821, GLN900, and ALA901 residues, along with a −11.95 docking score.

Abstract The signal transducer and activator of transcription 3 (STAT3) plays a fundamental role in the growth and regulation of cellular life. Activation and over-expression of STAT3 have been implicated in many cancers including solid blood tumors and other diseases such as liver fibrosis and rheumatoid arthritis. Therefore, STAT3 inhibitors are be coming a growing and interesting area of pharmacological research. Consequently, the aim of this study is to design novel inhibitors of STAT3-SH3 computationally for the reduction of liver fibrosis. Herein, we performed Pharmacophore-based virtual screening of databases including more than 19,481 commercially available compounds and in-house compounds. The hits obtained from virtual screening were further docked with the STAT3 receptor. The hits were further ranked on the basis of docking score and binding interaction with the active site of STAT3. ADMET properties of the screened compounds were calculated and filtered based on drug-likeness criteria. Finally, the top five drug-like hit compounds were selected and subjected to molecular dynamic simulation. The stability of each drug-like hit in complex with STAT3 was determined by computing their RMSD, RMSF, Rg, and DCCM analyses. Among all the compounds Sa32 revealed a good docking score, interactions, and stability during the entire simulation procedure. As compared to the Reference compound, the drug-like hit compound Sa32 showed good docking scores, interaction, stability, and binding energy. Therefore, we identified Sa32 as the best small molecule potent inhibitor for STAT3 that will be helpful in the future for the treatment of liver fibrosis.

Mineral coatings have the ability to tackle the most common degradation processes (wear and corrosion) in industries at amazing ‘quality to cost’ ratio, as compared to synthetic ceramics. In the current study, concentrated natural chromite with 55 wt.% Chromium oxide (Cr2O3) is deposited on steel substrate through High Velocity Oxygen Fuel (HVOF) and Atmospheric plasma spray (APS) techniques. Effect of deposition processes and Cr2O3 enrichment on microstructure, hardness, tribological and electrochemical behavior of coatings is studied. Tribological tests were performed using pin-on-disc setup, coatings deposited with APS have shown excellent wear resistance behavior as compared to HVOF coating. Owing to the brittle nature of chromite coatings, they were worn by abrasive wear irrespective of the deposition process. Electrochemical behavior of coatings is evaluated by potentiodynamic polarization measurements and electrochemical impedance spectroscopy (EIS) in 3.5% NaCl solution. APS and HVOF coatings have exhibited very high protective efficiency of 93 and 88% respectively, against steel substrate. Equivalent electrical circuit (EEC) based on EIS results has also been conceptualized and verified with ZSimpWin analysis software. Established EEC has illustrated capacitive behavior due to the protective oxide nature of coatings. Despite having relatively higher porosities in APS coatings, they display an excellent potential for wear and corrosion resistant applications. Results revealed that hardness, compactness, tribological and corrosion performance of chromite coatings have been substantially improved due to higher Cr2O3 content.

Abstract Cancer immunotherapy has significantly contributed to the treatment of various types of cancers mainly by targeting immune checkpoint inhibitors (ICI). Among them, V-domain immunoglobulin suppressor of T cell activation (VISTA) has been explored as a promising therapeutic target. Besides, histone deacetylase 6 (HDAC6) has been demonstrated to be efficacious target for several cancers. The current theoretical work was performed to explore the virtual repurposing of the FDA-approved drugs as inhibitors against these two (VISTA and HDAC6) cancers therapeutic targets. The crystal structure of the two proteins were downloaded from PDB and subjected to virtual screening by DrugRep webserver while using FDA-approved drugs library as ligands database. Our study revealed that Oxymorphone and Bexarotene are the top-ranked inhibitors of VISTA and HDAC6, respectively. The docking score of Bexarotene was predicted as − 10 kcal/mol while the docking score of Oxymorphone was predicted as − 6.2 kcal/mol. Furthermore, a total of 100 ns MD simulation revealed that the two drugs Oxymorphone and Bexarotene formed stable complexes with VISTA and HDAC6 drug targets. As compared to the standard drug the two drugs Oxymorphone and Bexarotene revealed great stability during the whole 100 ns MD simulation. The binding free energy calculation further supported the Root Mean Square Deviation (RMSD) result which stated that as compared to the ref/HDAC6 (− 18.0253 ± 2.6218) the binding free energy score of the Bexarotene/HDAC6 was good (− 51.9698 ± 3.1572 kcal/mol). The binding free energy score of Oxymorphone/VISTA and Ref/VISTA were calculated as − 36.8323 ± 3.4565, and − 21.5611 ± 4.8581 respectively. In conclusion, the two drugs deserve further consideration as cancer treatment option.

This article introduces a novel optimization approach known as fractional order whale optimization algorithm (FWOA). The proposed optimizer incorporates the idea of fractional calculus (FC) into the mathematical structure of the conventional whale optimization algorithm (WOA). To validate the efficiency of the proposed FWOA, it is applied to address the challenges associated with the economic load dispatch (ELD) problem, which is a nonconvex, nonlinear, and non-smooth optimization problem. The objectives associated with ELD such as fuel cost and wind power generation cost minimization are achieved by taking into consideration different practical constraints like valve point loading effect (VPLE), transmission line losses, generator constraints, and stochastically variation of renewable energy sources (RES) integration. RES, particularly wind energy, has garnered more attention in recent times due to a range of environmental and economic factors. Stochastic wind (SW) power is also included in the ELD problem formulation. The incomplete gamma function (IGF) quantifies the influence of wind power. To assess its efficacy, the suggested approach is applied to a range of power systems including 3 generating units, 13 generating units and 40 generating units, consisting of 37 thermal units and 3 wind power units. To further strengthen the performance of the optimizer, the FWOA is hybridized with the interior point algorithm (IPA) to further refine the outcomes of the FWOA. The FWOA and IPA are used to address the problem of ELD while including the unpredictable nature of wind power. The simulation results of the suggested technique are compared with the most advanced heuristic optimization methods available, and it has been observed that the proposed optimizer obtained a superior and refined solution when compared to other state of the art optimization techniques. Furthermore, the efficacy of the suggested strategy in enhancing the solution of the ELD issue is validated through statistical analysis in terms of minimum fitness value.

This paper addresses the enhancement of multiple Unmanned Aerial Vehicle (UAV) swarm formation control in challenging terrains through the novel fractional memetic computing approach known as fractional-order velocity-pausing particle swarm optimization (FO-VPPSO). Existing particle swarm optimization (PSO) algorithms often suffer from premature convergence and an imbalanced exploration–exploitation trade-off, which limits their effectiveness in complex optimization problems such as UAV swarm control in rugged terrains. To overcome these limitations, FO-VPPSO introduces an adaptive fractional order β and a velocity pausing mechanism, which collectively enhance the algorithm’s adaptability and robustness. This study leverages the advantages of a meta-heuristic computing approach; specifically, fractional-order velocity-pausing particle swarm optimization is utilized to optimize the flying path length, mitigate the mountain terrain costs, and prevent collisions within the UAV swarm. Leveraging fractional-order dynamics, the proposed hybrid algorithm exhibits accelerated convergence rates and improved solution optimality compared to traditional PSO methods. The methodology involves integrating terrain considerations and diverse UAV control parameters. Simulations under varying conditions, including complex terrains and dynamic threats, substantiate the effectiveness of the approach, resulting in superior fitness functions for multi-UAV swarms. To validate the performance and efficiency of the proposed optimizer, it was also applied to 13 benchmark functions, including uni- and multimodal functions in terms of the mean average fitness value over 100 independent trials, and furthermore, an improvement at percentages of 29.05% and 2.26% is also obtained against PSO and VPPSO in the case of the minimum flight length, as well as 16.46% and 1.60% in mountain terrain costs and 55.88% and 31.63% in collision avoidance. This study contributes valuable insights to the optimization challenges in UAV swarm-formation control, particularly in demanding terrains. The FO-VPPSO algorithm showcases potential advancements in swarm intelligence for real-world applications.

In this work, we investigated Diospyros kaki extract and an isolated compound for their potential as xanthine oxidase (XO) inhibitors, a target enzyme involved in inflammatory disorders. The prepared extract was subjected to column chromatography, and dinaphthodiospyrol S was isolated. Then XO inhibitory properties were assessed using a spectrophotometry microplate reader. DMSO was taken as a negative control, and allopurinol was used as a standard drug. The molecular docking study of the isolated compound to the XO active site was performed, followed by visualization and protein–ligand interaction. The defatted chloroform extract showed the highest inhibitory effect, followed by the chloroform extract and the isolated compound. The isolated compound exhibited significant inhibitory activity against XO with an IC50 value of 1.09 µM. Molecular docking studies showed that the compound strongly interacts with XO, forming hydrogen bond interactions with Arg149 and Cys113 and H-pi interactions with Cys116 and Leu147. The binding score of −7.678 kcal/mol further supported the potential of the isolated compound as an XO inhibitor. The quantum chemical procedures were used to study the electronic behavior of dinaphthodiospyrol S isolated from D. kaki. Frontier molecular orbital (FMO) analysis was performed to understand the distribution of electronic density, highest occupied molecular orbital HOMO, lowest unoccupied molecular orbital LUMO, and energy gaps. The values of HOMO, LUMO, and energy gap were found to be −6.39, −3.51 and 2.88 eV respectively. The FMO results indicated the intramolecular charge transfer. Moreover, reactivity descriptors were also determined to confirm the stability of the compound. The molecular electrostatic potential (MEP) investigation was done to analyze the electrophilic and nucleophilic sites within a molecule. The oxygen atoms in the compound exhibited negative potential, indicating that they are favorable sites for electrophilic attacks. The results indicate its potential as a therapeutic agent for related disorders. Further studies are needed to investigate this compound's in vivo efficacy and safety as a potential drug candidate.

In the current studies two naproxen derivatives (NPD) were evaluated for analgesic and anti-inflammatory properties. The acetic acid and hot plate animal models were used to screen the compounds for analgesic potential. While the anti-inflammatory potential was evaluated through animal paw edema, induced by several inflammatory mediators (carrageenan, bradykinin, and prostaglandin E2), the xylene-induced ear edema was also used as an inflammatory model. Both NPDs showed significant (p

Inhibiting α-glucosidase is a reliable method for reducing blood sugar levels in diabetic individuals. Bis(dimethylamino)benzophenone derivatives 1–27 were synthesized from bis(dimethylamino)benzophenone via two-step reaction. Different spectroscopic techniques, including EI-MS and 1H NMR, were employed to characterize all synthetic derivatives. The elemental composition of synthetic compounds was confirmed by elemental analysis and results were found in agreement with the calculated values. The synthetic compounds 1–27 were evaluated for α-glucosidase inhibitory activity, except five compounds all derivatives showed good to moderate inhibitory potential in the range of IC50 = 0.28 ± 2.65 - 0.94 ± 2.20 μM. Among them, the most active compounds were 5, 8, 9, and 12 with IC50 values of 0.29 ± 4.63, 0.29 ± 0.93, 0.28 ± 3.65, and 0.28 ± 2.65, respectively. Furthermore, all these compounds were found to be non-toxic on human fibroblast cell lines (BJ cell lines). Kinetics study of compounds 8 and 9 revealed competitive type of inhibition with Ki values 2.79 ± 0.011 and 3.64 ± 0.012 μM, respectively. The binding interactions of synthetic compounds were also confirmed through molecular docking studies that indicated that compounds fit well in the active site of enzyme. Furthermore, a total of 30ns MD simulation was carried out for the most potent complexes of the series. The molecular dynamics study revealed that compound-8 and compound-12 were stable during the MD simulation.

The proper coordination of directional overcurrent relays (DOCRs) is crucial in electrical power systems. The coordination of DOCRs in a multi-loop power system is expressed as an optimization problem. The aim of this study focuses on improving the protection system’s performance by minimizing the total operating time of DOCRs via effective coordination with main and backup DOCRs while keeping the coordination constraints within allowable limits. The coordination problem of DOCRs is solved by developing a new application strategy called Fractional Order Derivative Moth Flame Optimizer (FODMFO). This approach involves incorporating the ideas of fractional calculus (FC) into the mathematical model of the conventional moth flame algorithm to improve the characteristics of the optimizer. The FODMFO approach is then tested on the coordination problem of DOCRs in standard power systems, specifically the IEEE 3, 8, and 15 bus systems as well as in 11 benchmark functions including uni- and multimodal functions. The results obtained from the proposed method, as well as its comparison with other recently developed algorithms, demonstrate that the combination of FOD and MFO improves the overall efficiency of the optimizer by utilizing the individual strengths of these tools and identifying the globally optimal solution and minimize the total operating time of DOCRs up to an optimal value. The reliability, strength, and dependability of FODMFO are supported by a thorough statistics study using the box-plot, histograms, empirical cumulative distribution function demonstrations, and the minimal fitness evolution seen in each distinct simulation. Based on these data, it is evident that FODMFO outperforms other modern nature-inspired and conventional algorithms.

This research utilizes the innovative fractional-order Archimedean spiral moth–flame optimization (FO-AMFO) technique to address the issues of the optimal reactive power dispatch (ORPD) problem. The formulated fitness function aims to minimize power losses and determine the ideal flow of reactive power for the IEEE 30- and 57-bus test systems. The extensive functions of the fractional evolutionary computing strategy are utilized to address the minimization problem of ORPD. This involves determining the control variables, such as VAR compensators, bus voltages, and the tap setting of the transformers. The effective incorporation of reactive compensation devices into traditional power grids has greatly reduced power losses; however, it has resulted in an increase in the complexity of optimization problems. A comparison of the findings indicates that swarming fractional intelligence using FO-AMFO surpassed the state-of-the-art competitors in terms of minimizing power losses.

The effects of chemical reaction and Hall current on the unsteady magneto hydrodynamic flow along a vertical plate with variable temperature and mass diffusion in the presence of a porous medium is studied hare. The fluid flow model of this paper contains governing partial differential equations of the motion, energy, and diffusion equation. To simplify the analysis, the governing equations are transformed into dimensionless form using non-dimensional variables. The Laplace-transform technique is employed to obtain an exact solution for the flow equations of the MHD model. The results of the analysis are presented using graphical representations of the velocity profiles. With the help of graphs, we discussed the behavior of fluid velocities with different parameters, including the chemical reaction parameter, Hall cur-rents parameter, accelerated parameter, magnetic field parameter, and permeability parameter, and the numerical values of Sherwood number have been tabulated. We found that the values obtained for velocity, concentration and temperature are in concurrence with the actual flow of the fluid.

In this paper, a real-time adaptive clustering-based distributed voltage regulation scheme is presented for the voltage regulation of unbalanced distribution systems. In the proposed methodology, first, adaptive clusters for the distributed control are formulated based on voltage to injected power sensitivity, and a threshold criterion is designed that limits the size of the clusters to strongly controlled nodes only. Having performed the clustering, a multi-objective optimization problem is solved for power factor optimization within clusters for voltage regulation of all the control zones of the distribution system. Voltage quality factor and voltage unbalance indices are utilized to show the improvements in voltage profile and reduction in voltage unbalance, respectively. Furthermore, for optimally finding the tap positions for multiple OLTCs, another optimization problem is designed. An IEEE 123-node test feeder with multiple wind and solar DGs of varying capacities is used for the simulations, and two test cases with different scenarios are simulated to test the proposed scheme. All the results verify that the proposed scheme effectively regulates the voltage of the unbalanced system without massive remote data requirements and active power curtailment which ensures the stability of the system’s voltage profile and prevents OLTC-hunting even in highly dynamic operational scenarios.

Abstract One of the challenging issues in the realization of medium‐voltage DC (MVDC) distribution system is high‐speed protection scheme design against quickly rising short circuit fault‐DC current. This paper proposes a high‐speed protection scheme based on the processing of energy signals through mathematical morphology (MM). The voltage and current signals measured at a distribution line section (DLS) are used to obtain the energy signal. The proposed scheme develops a modified MM‐based filter to extract the transient information in the energy signal rapidly and distinctly in the form of positive/negative polarity. The polarity detection of any disturbance in the analysed energy signal indicates the occurrence of a sudden disturbance in the system. The sudden disturbance occurrence confirmation triggers the faulty DLS identification and classification algorithms of the proposed scheme. The faulty DLS identification and classification algorithms are based on the detected polarities of the energy signal. The protection scheme requires low bandwidth communication. The proposed protection scheme is evaluated by using a ±2.5 kV radial distribution network containing 4 feeders under different fault conditions in MATLAB/SIMULINK software. Simulation results verify that the proposed scheme can isolate the faulty portion within a few milliseconds after fault inception under a variety of fault cases.

In the current research work, an amide based metal carboxylate chemical ([((5-((5-(2-hydroxyethyl)-4-methylthiazol-3-ium-3-yl)methyl)-2-methylpyrimidin-4-yl)amino)bis((4-((4-methoxy-2-nitrophenyl)amino)-4-oxobutanoyl)oxy)zinc]) was identified as anti-diabetic analgesic and anti-inflammatory. The identified chemical(MT-1) was tested for acute toxicity (the MT-1 was fund safe), antidiabetic analgesic, and anti-inflammatory potentials. The in-vitro study was conducted for antidiabetic enzyme inhibition (α-amylase and α-glucosidase) and the in-vivo studies included analgesic (acetic acid-induced writing and hot plate model) and anti-inflammatory (carrageenan etc induced edema) effects. The tested compound showed 88.63% (IC50 = 3.23 μg/ml) and 89.10%(IC50 = 5.10 μg/ml) againstα-amylase and α-glucosidase respectively. A significant (p

Vibrio vulnificus is a rod shape, Gram-negative bacterium that causes sepsis (with a greater than 50% mortality rate), necrotizing fasciitis, gastroenteritis, skin, and soft tissue infection, wound infection, peritonitis, meningitis, pneumonia, keratitis, and arthritis. Based on pathogenicity V. vulnificus is categorized into three biotypes. Type 1 and type 3 cause diseases in humans while biotype 2 causes diseases in eel and fish. Due to indiscriminate use of antibiotics V. vulnificus has developed resistance to many antibiotics so curing is dramatically a challenge. V. vulnificus is resistant to cefazolin, streptomycin, tetracycline, aztreonam, tobramycin, cefepime, and gentamycin. Subtractive genome analysis is the most effective method for drug target identification. The method is based on the subtraction of homologous proteins from both pathogen and host. By this process set of proteins present only in the pathogen and perform essential functions in the pathogen can be identified. The entire proteome of Vibrio vulnificus strain ATCC 27562 was reduced step by step to a single protein predicted as the drug target. AlphaFold2 is one of the applications of deep learning algorithms in biomedicine and is correctly considered the game changer in the field of structural biology. Accuracy and speed are the major strength of AlphaFold2. In the PDB database, the crystal structure of the predicted drug target was not present, therefore the Colab notebook was used to predict the 3D structure by the AlphaFold2, and subsequently, the predicted model was validated. Potent inhibitors against the new target were predicted by virtual screening and molecular docking study. The most stable compound ZINC01318774 tightly attaches to the binding pocket of bisphosphoglycerate-independent phosphoglycerate mutase. The time-dependent molecular dynamics simulation revealed compound ZINC01318774 was superior as compared to the standard drug tetracycline in terms of stability. The availability of V. vulnificus strain ATCC 27562 has allowed in silico identification of drug target which will provide a base for the discovery of specific therapeutic targets against Vibrio vulnificus.

This review focuses on the therapeutic effects of ions when released in physiological environments. Recent studies have shown that metallic ions like Ag+, Sr2+, Mg2+, Mn2+, Cu2+, Ca2+, P+5, etc., have shown promising results in drug delivery systems and regenerative medicine. These metallic ions can be loaded in nanoparticles, mesoporous bioactive glass nanoparticles (MBGNs), hydroxyapatite (HA), calcium phosphates, polymeric coatings, and salt solutions. The metallic ions can exhibit different functions in the physiological environment such as antibacterial, antiviral, anticancer, bioactive, biocompatible, and angiogenic effects. Furthermore, the metals/metalloid ions can be loaded into scaffolds to improve osteoblast proliferation, differentiation, bone development, fibroblast growth, and improved wound healing efficacy. Moreover, different ions possess different therapeutic limits. Therefore, further mechanisms need to be developed for the highly controlled and sustained release of these ions. This review paper summarizes the recent progress in the use of metallic/metalloid ions in regenerative medicine and encourages further study of ions as a solution to cure diseases.

IntroductionMonkeypox is a zoonotic disease caused by brick-shaped enveloped monkeypox (Mpox) virus that belongs to the family of ancient viruses known as Poxviridae. Subsequently, the viruses have been reported in various countries. The virus is transmitted by respiratory droplets, skin lesions, and infected body fluids. The infected patients experience fluid-filled blisters, maculopapular rash, myalgia, and fever. Due to the lack of effective drugs or vaccines, there is a need to identify the most potent and effective drugs to reduce the spread of monkeypox. The current study aimed to use computational methods to quickly identify potentially effective drugs against the Mpox virus.MethodsIn our study, the Mpox protein thymidylate kinase (A48R) was targeted because it is a unique drug target. We screened a library of 9000 FDA-approved compounds of the DrugBank database by using various in silico approaches, such as molecular docking and molecular dynamic (MD) simulation.ResultsBased on docking score and interaction analysis, compounds DB12380, DB13276, DB13276, DB11740, DB14675, DB11978, DB08526, DB06573, DB15796, DB08223, DB11736, DB16250, and DB16335 were predicted as the most potent. To examine the dynamic behavior and stability of the docked complexes, three compounds—DB16335, DB15796, and DB16250 —along with the Apo state were simulated for 300ns. The results revealed that compound DB16335 revealed the best docking score (-9.57 kcal/mol) against the Mpox protein thymidylate kinase.DiscussionAdditionally, during the 300 ns MD simulation period, thymidylate kinase DB16335 showed great stability. Further, in vitro and in vivo study is recommended for the final predicted compounds.

Objective: Medicinal plants are the mainstay of various chemical constituents responsible for curing or mitigating different pathological and non-pathological health issues. In this regard, the current research work was subjected to finding the best effective, new and safe leishmanicidal molecules. Pistacia integerrima is an important medicinal plant growing in Asia with various ethnopharmacological uses such as skin, respiratory and hepatic disorders. The folklore of this plant in skin problems encouraged us to evaluate the isolated compounds for leishmanicidal activity. Methods: In the current study, whole ethyl acetate extract was subjected to chromatographic analysis affording two flavonoid constituents, namely naringenin (1) and 3,5,4′-trihydroxy-7-methoxy-flavanone (2). The extract and isolated constituents were tested for leishmanicidal activity. Both compounds were docked into the target binding site (PDB ID: 5HJ9) using MOE 2016. Arginase was taken as the targeted enzyme which is an important enzyme for leishmania species’ survival. Arginase up-regulates amastin that impacts amastigote replication and survival. This approach aimed to explore the binding modes of the two isolated flavonoids and to predict their inhibitory activity against arginase. Results: The two flavonoid compounds isolated from P. integerrima were evaluated for their anti-leishmanial activity. Compounds 1 and 2 were tested at a concentration of 5 µg/mL against the selected Leishmanial species. The percent inhibition was 70.43 and 74.76 regarding compounds 1 and 2, respectively with IC50 of 29.43 and 16.43 μM. The reference standard drug used, amphotericin-B, displayed the highest leishmanicidal effect (IC50 of 0.22 μM) with 84.09 % inhibition at half concentration compared to compounds 1 and 2. The molecular docking studies of these two compounds and the standard drug were performed in MOE software. According to the findings, compound 1, naringenin, docked well in the binding site, with a docking score of −9.28 followed by compound 2, 3,5,4′-trihydroxy-7-methoxy-flavanone with a docking score of −8.37. Conclusion: Both of the tested samples demonstrated excellent leishmanicidal effects. This pharmacological inhibitory profile provides a strong scientific profile to the folklore of Pistacia integerrima in the treatment of skin disorders especially leishmanial. A further mechanistic study is recommended to find the extract target responsible for the said activity.

The new coronavirus SARS-COV-2, which emerged in late 2019 from Wuhan city of China was regarded as causing agent of the COVID-19 pandemic. The primary protease which is also known by various synonymous i.e., main protease, 3-Chymotrypsin-like protease (3CLPRO) has a vital role in the replication of the virus, which can be used as a potential drug target. The current study aimed to identify novel phytochemical therapeutics for 3CLPRO by machine learning-based virtual screening. A total of 4,000 phytochemicals were collected from deep literature surveys and various other sources. The 2D structures of these phytochemicals were retrieved from the PubChem database, and with the use of a molecular operating environment, 2D descriptors were calculated. Machine learning-based virtual screening was performed to predict the active phytochemicals against the SARS-CoV-2 3CLPRO. Random forest achieved 98% accuracy on the train and test set among the different machine learning algorithms. Random forest model was used to screen 4,000 phytochemicals which leads to the identification of 26 inhibitors against the 3CLPRO. These hits were then docked into the active site of 3CLPRO. Based on docking scores and protein-ligand interactions, MD simulations have been performed using 100 ns for the top 5 novel inhibitors, ivermectin, and the APO state of 3CLPRO. The post-dynamic analysis i.e,. Root means square deviation (RMSD), Root mean square fluctuation analysis (RMSF), and MM-GBSA analysis reveal that our newly identified phytochemicals form significant interactions in the binding pocket of 3CLPRO and form stable complexes, indicating that these phytochemicals could be used as potential antagonists for SARS-COV-2.

Diospyros kaki (Japanese persimmon) is cultivated specious of the Diospyros genus. D. kaki is a multi-medicinal application in the folk system for the cure of ischemic stroke, angina, atherosclerosis, muscle relaxation, internal hemorrhage, hypertension, high cough, and infectious disease. The main objective of this study was the isolated bioactive metabolites from chloroform fractions of D. kaki. The extract and fractions were then tested for various in-vitro (antioxidant and lipoxygenase) and in-vivo (muscle relaxant) activities. The repeated chromatographic separation of chloroform extract afforded compound 1. Compound 1, n-hexane, and chloroform fractions were evaluated for in vitro antioxidant, lipoxygenase inhibitory, and in vivo muscle relaxant potency. The chloroform extract has 79.54% interaction with DPPH at higher concentrations (100 μg/ml) while the compound exhibited a maximum effect of 95.09% at 100 μg/ml. Compound 1 exhibited significant lipoxygenase inhibitory activity with an IC50 value of 36.98 μM followed by a chloroform extract of 57.09 μM. Similarly, compound 1 and chloroform extract showed excellent muscle relaxant effects at a higher dose. From this investigation, it is concluded that extracts and pure compounds exhibited promising antioxidant, lipoxygenase inhibitory, and muscle relaxant activity. This study excellently rationalizes the traditional usage of D. kaki in curing various diseases. Furthermore, the docking results indicate, that the isolated compound fits well into the active site of the lipoxygenase, and makes strong interactions with the target protein.

Copper(II) complexes with a general formula [Cu2(3,4-F2C6H3CH2COO)4(L)2], where L = 2-methylpyridine (1) and 3-methylpyridine (2), are reported here. The FTIR spectra of the complexes confirmed the bridging bidentate coordination mode of the carboxylate ligand. The low (475 and 449 cm−1) and strong (727 & 725 cm−1) intensity bands in the FTIR spectra, due to Cu-N stretches and pyridyl ring vibrations, confirmed coordination of the 2-/3-methyl pyridine co-ligands in complexes 1 and 2, respectively. A binuclear paddlewheel structural arrangement with a square pyramidal geometry was confirmed for copper atoms in the complexes via single-crystal X-ray analysis. The DPPH, •OH radical, and α-amylase enzyme inhibition assays showed higher activities for the complexes than for the free ligand acid. The binding constant (Kb = 1.32 × 105 for 1 and 5.33 × 105 for 2) calculated via UV-VIS absorption measurements and docking scores (−6.59 for 1 and −7.43 for 2) calculated via molecular docking showed higher SS-DNA binding potential for 2 compared to 1. Viscosity measurement also reflected higher DNA binding ability for 2 than 1. Both complexes 1 and 2 (docking scores of −7.43 and −6.95, respectively) were found to be more active inhibitors than the free ligand acid (docking score of −5.5159) against the target α-amylase protein. This in silico study has shown that the herein reported compounds follow the rules of drug-likeness and exhibit good potential for bioavailability.

With the growing cyber-infrastructure of smart grids, the threat of cyber-attacks has intensified, posing an increased risk of compromised communication links. Of particular concern is the false data injection (FDI) attack, which has emerged as a highly dangerous cyber-attack targeting smart grids. This paper addresses the limitations of the variable dummy value model proposed in the authors previous work and presents a novel defense methodology called the nonlinear function-based variable dummy value model for the AC power flow network. The proposed model is evaluated using the IEEE 14-bus test system, demonstrating its effectiveness in detecting FDI attacks. It has been shown that previous detection techniques are unable to detect FDI attacks, whereas the proposed method is shown to be successful in the detection of such attacks, guaranteeing the security of the smart grid’s measurement infrastructure.

With the increase in energy demand, renewable energy has become a need of almost every country. Solar Energy is an important constituent of it and contributes a large portion in it. Forecasting the output power of a Photovoltaic (PV) system has always been a challenging problem in the power sector from the last few decades. The output power of a PV system depends upon several environmental factors such as irradiance (G), temperature (T), humidity (H), wind speed (W), provided the tilt angle is kept constant, among which the vital role is played by irradiance. Researchers have utilized several techniques to accurately predict the output power of PV module but every method has various pros and cons. In this paper, an experimental measurement dataset of 28296 samples with all the environmental parameters mentioned above are taken as the inputs and power as its output, of a Poly-Silicon (Poly-Si) PV module, is trained through Artificial Neural Network (ANN), to predict the output power accurately. The proposed ANN contains a layer size of 15 and training algorithm used is Levenberg-Marquardt. A detailed analysis and preprocessing of the data is carried out through Pearson’s correlation method prior to training. The hyperparameters of Neural Network tuning are selected through heuristic method. The data division is done randomly with 70% dataset used for training, 15% dataset used for each validation and testing. The statistical results show that ANN accurately predicted the power output of PV module. The regression analysis values acquired are 98% and the MSE of all the three phases is 0.0604.

Inflammation is the immune system's adaptive response to tissue dysfunction or homeostatic imbalance, inducing fever, pain, physiological and biochemical changes via the cyclooxygenase (COX) and lipoxygenase (LOX) pathways. NSAIDs (non-steroidal anti-inflammatory drugs), such as diclofenac acid and naproxen, are the most common inhibitors of the COX pathway. These drugs, however, are currently being studied as LOX inhibitors as well. Therefore, in the present study, a novel series of diclofenac acid and naproxen-bearing hydrazones 7(a-r) were designed, synthesized, and characterized by different spectroscopic methods like 1H NMR, 13C NMR, IR and HRMS (EI) analysis. All these synthesized compounds were evaluated for their in vitro inhibitory potential against the Soybean 15-lipoxygenase (15-LOX) enzyme. These compounds exhibited varying degrees of inhibitory potential ranging from IC50 4.61 ± 3.21 μM to 193.62 ± 4.68 μM in comparison to standard inhibitors quercetin (IC50 4.84 ± 6.43 μM) and baicalein (IC50 22.46 ± 1.32 μM). The most potent compounds in the series were compounds 7c (IC50 4.61 ± 3.21 μM), and 7f (IC50 6.64 ± 4.31 μM). These compounds were found least cytotoxic and showed 96.42 ± 1.3 % and 94.87 ± 1.6 % viability to cells at 0.25 mM concentration respectively. ADME and in silico studies supported the drug-likeness and binding studies of the molecules with the target enzyme.

Abstract Background According to the recent global cancer statistics, breast cancer is the leading cause of deaths among women with 2.3 million new cases globally. Likewise, cervical cancer is also among the leading causes of mortality among women. Polygonum hydropiper is traditionally known for its cytotoxic effects and several bioactive cytotoxic compounds were isolated from it. This study was aimed to isolate potential anticancer compounds from its most potent fractions and evaluate their anticancer potentials. Methods Based on our earlier studies, active fractions including chloroform and ethyl acetate were subjected to column chromatography for isolation of compounds. Chemical structures of isolated compounds were confirmed via 1H NMR, 13C NMR, mass spectrometry. Purified compounds were tested for cytotoxicity against breast cancer cells (MCF-7), cervical cancer cells (HeLA) and NIH/3T3 fibroblasts cells cultures using MTT assy. Anti-angiogenic potentials of isolated compounds were evaluated via chorioallantoic membrane assay. Anti-tumor studies were done using Agrobacterium tumefaciens induced potato tumor assay. Furthermore, to understand the binding modes of Isolated compounds, molecular docking was performed against EGFR, HER2 and VEGFR using MOE as docking software. Results Two bioactive compounds PH-1 (4-methyl-5-oxo-tetrahydrofuran-3-yl acetate) and PH-2 (methyl 4-hydroxy-3-methoxybenzoate) were purified from the active fractions. In cytotoxicity studies, PH-1 exhibited highest cytotoxicity against HeLA cells with 87.50% lethality at 1 mgmL−1 concentration and LD50 of 60 µgmL−1. Likewise, PH-2 showed 82.33% cytotoxicity against HeLA cells with LD50 of 160 µgmL−1. Similarly, PH-1 and PH-2 exhibited LD50 of 170 and 380 µgmL−1 respectively. Moreover, PH-1 and PH-2 were also very potent cytotoxic compounds against NIH/3T3 cells with 81.45 and 85.55% cytotoxicity at 1 mgL−1 concentration and LD50 of 140 and 58 µgL−1 respectively. Isolated compounds exhibited considerable anti-angiogenic potentials with IC50 of 340 and 500 µgL−1 respectively for PH-1 and PH-2. In anti-tumor assay, PH-1 and PH-2 exhibited 81.15 and 76.09% inhibitions with LD50 of 340 and 550 µgL−1 respectively. Both compounds selectively binds with EGFR and HER2 receptors with low binding energies. Both compounds exhibited stronger interactions with VEGFR through binding pocket residues Lys868, Val916 and Asp1046. Conclusions Both compounds cause considerable cytotoxicity against cancer cells. The anti-angiogenic and anti-tumor results suggests additional tumor suppressive properties. Docking analysis suggests that these compound not only has the ability to bind to EGFR and HER2 but also equally binds to VEGFR and may act as potential anti-angiogenic agents.

Intestinal perforation from a plastic biliary stent is a known but rare complication of endoscopic biliary stent placement. Intra-peritoneal perforation is less common but carries more morbidity and mortality. Only a few cases of early stent migration and perforation have been reported. We present the case of a duodenal perforation caused by early migration of plastic biliary stent that resulted in intra-peritoneal biliary peritonitis. Keywords: Biliary stent, bowel perforation, early migration.

Enzyme inhibitors are vital aspects for studying enzymes and are employed as drugs to treat certain disorders, thus implying pivotal role in drug discovery. In the current study, a series of triazole compounds 4(a-o) were synthesised to explore their inhibitory potential against α-glucosidase and urease enzymes. These derivatives with dichlorophenyl substituents were prepared by cyclization of thiosemicarbazides and their structures were confirmed through spectroanalytical techniques. The in vitro biological screening revealed that the compounds 4a, 4b, 4k, 4l, 4m, 4o having IC50 values of 121.09 ± 1.25, 137.22 ± 0.22, 110.4 ± 2.4, 114.79 ± 1.1, 146.72 ± 1.29, 94.21 ± 0.15 [µM] respectively, exhibited good potential α-glucosidase inhibition, in comparison to Acarbose: IC50 51.23 µM, while the compounds 4a, 4b, 4c, 4k, 4l, having IC50 values of 48.52 ± 0.39, 52.22 ± 1.37, 60.98 ± 0.34, 37.06 ± 0.51, 38.66 ± 1.7 [µM] respectively exhibited good potential for urease inhibition near to standard(Thiourea: IC50 24.14 [µM]). These in vitro findings were accompanied further by molecular docking simulations, which revealed significant binding interactions of the synthesized derivatives within the active sites of the enzymes.

Electrophoretic deposition (EPD) is a powerful technique to assemble metals, polymer, ceramics, and composite materials into 2D, 3D, and intricately shaped implants. Polymers, proteins, and peptides can be deposited via EPD at room temperature without affecting their chemical structures. Furthermore, EPD is being used to deposit multifunctional coatings (i.e., bioactive, antibacterial, and biocompatible coatings). Recently, EPD was used to architect multi-structured coatings to improve mechanical and biological properties along with the controlled release of drugs/metallic ions. The key characteristics of EPD coatings in terms of inorganic bioactivity and their angiogenic potential coupled with antibacterial properties are the key elements enabling advanced applications of EPD in orthopedic applications. In the emerging field of EPD coatings for hard tissue and soft tissue engineering, an overview of such applications will be presented. The progress in the development of EPD-based polymeric or composite coatings, including their application in orthopedic and targeted drug delivery approaches, will be discussed, with a focus on the effect of different biologically active ions/drugs released from EPD deposits. The literature under discussion involves EPD coatings consisting of chitosan (Chi), zein, polyetheretherketone (PEEK), and their composites. Moreover, in vitro and in vivo investigations of EPD coatings will be discussed in relation to the current main challenge of orthopedic implants, namely that the biomaterial must provide good bone-binding ability and mechanical compatibility.

This work has described the synthesis of novel class (1–25) of benzofuran based hydrazone. The hybrid scaffolds (1–25) of benzofuran based hydrazone were evaluated in vitro, for their urease inhibition. All the newly synthesized analogues (1–25) were found to illustrate moderate to good urease inhibitory profile ranging from 0.20 ± 0.01 to 36.20 ± 0.70 µM. Among the series, compounds 22 (IC50 = 0.20 ± 0.01 µM), 5 (IC50 = 0.90 ± 0.01 µM), 23 (IC50 = 1.10 ± 0.01 µM) and 25 (IC50 = 1.60 ± 0.01 µM) were found to be the many folds more potent than thiourea as standard inhibitor (IC50 = 21.86 ± 0.40 µM). The elevated inhibitory profile of these analogues might be due to presence of dihydroxy and flouro groups at different position of phenyl ring B attached to hydrazone skeleton. These dihydroxy and fluoro groups bearing compounds have shown many folds better inhibitory profile through involvement of oxygen of dihydroxy groups in hydrogen bonding with active site of enzymes. Various types of spectroscopic techniques such as 1H-, 13C- NMR and HREI-MS spectroscopy were used to confirm the structure of all the newly developed compounds. To find SAR, molecular docking studies were performed to understand, the binding mode of potent inhibitors with active site of enzymes and results supported the experimental data.

In the present study, twenty (20) structural variants of nitrofurazone were synthesized based on BIODS (Biology-oriented drug synthesis) approach. The structure elucidation of the synthetic molecules (1–20) was carried out using different spectroscopic techniques, and their α-glucosidase inhibitory activity was also determined. The synthetic molecules 1–20 exhibited good α-glucosidase inhibition than the parent, nitrofurazone. Four compounds 2, 4, 6, and 7 showed potential inhibition against α-glucosidase with IC50 values ranging between 0.63 ± 0.25–1.29 ± 0.46 µM as compared to the standard acarbose (IC50 = 2.05 ± 0.41 µM). Nevertheless, compounds 15 (IC50 = 0.74 ± 0.12 µM), and 19 (IC50 = 0.54 ± 0.3 µM) also displayed good α-glucosidase inhibition and compound 19 was the most active compound of the series. Kinetic study of the active compounds 7 and 19 was also carried out to confirm the mode of inhibition. The binding interactions of the most active compounds within the active site of enzyme were determined by molecular docking. Moreover, molecular dynamic simulation of compound 19 was also performed in order to determine the stability of the overall complex (α-glucosidase + c19) in an explicit watery environment. The synthetic molecules were predicted as non-cytotoxic, however, seven compounds 1, 3, 4, 9, 10, 11, and 12 were predicted as carcinogenic.

Abstract Background The aim of this paper is to investigate the prevalence of diabetes and its associated risk factors in Afghanistan through a systematic review and meta–analysis. Methods A comprehensive literature search was conducted using EMBASE, PubMed, Web of Sciences, Google Scholar and the Cochrane library, carried out from inception to April 312,020, without language restriction. Meta–analysis was performed using DerSimonian and Laird random-effects models with inverse variance weighting. The existence of publication bias was initially assessed by visual inspection of a funnel plot and then tested by the Egger regression test. Subgroup analyses and meta-regression were used to explore potential sources of heterogeneity. This systematic review was reported by following the PRISMA guidelines and the methodological quality of each included study was evaluated using the STROBE guidelines. Results Out of 64 potentially relevant studies, only 06 studies fulfilled the inclusion criteria and were considered for meta-analysis. The pooled prevalence of diabetes in the general population based on population-based studies were 12.13% (95% CI: 8.86–16.24%), based on a pooled sample of 7071 individuals. Results of univariate meta-regression analysis revealed that the prevalence of diabetes increased with mean age, hypertension and obesity. There was no significant association between sex (male vs female), smoking, the methodological quality of included articles or education (illiterate vs literate) and the prevalence of diabetes. Conclusions This meta-analysis reports the 12.13% prevalence of diabetes in Afghanistan,with the highest prevalence in Kandahar and the lowest in Balkh province. The main risk factors include increasing age, obesity and hypertension. Community-based care and preventive training programmes are recommended. Trial registration This review was registered on PROSPERO (registration number CRD42020172624 ).

It is evident that inflammation and metabolic syndrome instigated by diabetes mellitus can precipitate diabetes-induced neuropathy (DIN) and pain. In order to find an effective therapeutic method for diabetes-related problems, a multi-target-directed ligand model was used. 6-Hydroxyflavanone (6-HF) carrying anti-inflammatory and anti-neuropathic pain potential due to its quadruplicate mechanisms, targeting cyclooxygenase-2 (COX-2), 5-lipoxygenase (5-LOX), and opioid and GABA-A receptors was investigated. The anti-inflammatory potential of the test drug was confirmed utilizing in silico, in vitro, and in vivo tests. A molecular simulation approach was utilized to observe the interaction of 6-HF with the inflammatory enzyme COX-2 as well as opioid and GABA-A receptors. The same was confirmed via in vitro COX-2 and 5-LOX inhibitory assays. In vivo tests were performed to analyze the thermal anti-nociception in the hot-plate analgesiometer and anti-inflammatory action in the carrageenan-induced paw edema model in rodents. The potential anti-nociceptive effect of 6-HF was evaluated in the DIN model in rats. The Naloxone and Pentylenetetrazole (PTZ) antagonists were used to confirm the underlying mechanism of 6-HF. The molecular modeling studies revealed a favorable interaction of 6-HF with the identified protein molecules. In vitro inhibitory studies revealed that 6-HF inhibited the COX-2 and 5-LOX enzymes significantly. The 6-HF at dosages of 15, 30, and 60 mg/kg substantially reduced heat nociception in a hot plate analgesiometer as well as carrageenan-induced paw edema in rodent models. The authors discovered that 6-HF had anti-nociception properties in a streptozotocin-induced diabetic neuropathy model. According to the findings of this study, 6-HF was demonstrated to diminish inflammation caused by diabetes as well as its anti-nociception effect in DIN.

The hybridization of PSO for the Optimal Coordination of Directional Overcurrent Protection Relays (DOPR) of the IEEE bus system proposes a new method for coordinating directional overcurrent protection relays in power systems. The method combines the hybrid particle swarm optimization (HPSO) algorithm and a heuristic PSO algorithm to find the minimum total operating time of the directional overcurrent protection relays with speed and accuracy. The proposed method is tested on the IEEE 4-bus, 6-bus, and 8-bus systems, and the results are compared with those obtained using traditional coordination methods. The collected findings suggest that the proposed method may produce better coordination and faster operation of DOPRs than the previous methods, with an increase of up to 74.9% above the traditional technique. The hybridization of the PSO algorithm and heuristic PSO algorithm offers a promising approach to optimize power system protection.