Tris-diamine-derived transition metal complexes of flurbiprofen as cholinesterase inhibitors

Purpose: To synthesize novel tris-diamine-derived transition metal  complexes of flurbiprofen M(C2H8N2)3 (fp)2 and M(C3H10N2)3 (fp)2, and to evaluate their acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitory activities.Method: Tris-diamine-derived transition metal complexes of Co(II),  Ni(II), and Mn(II) were synthesized and characterized using ultraviolet-visible (UV-Vis) spectroscopy, Fourier transform-infrared (FT-IR) spectroscopy, elemental analysis, magnetic susceptibility, conductivity  measurement and single crystal x-ray analysis. The synthesized  complexes were also evaluated for their AChE and BChE inhibitory activities.Results: Based on magnetic susceptibility and electronic studies, the synthesized complexes possessed distorted octahedral geometry.  Conductance measurements indicated that diamine-derived metal complexes of flurbiprofen were electrolytes, whereas, simple metal complexes of flurbiprofen were non-electrolytes. The structure of Ni (C2H8N2)3 (fp)2 was also confirmed by single crystal x-ray analysis. The synthesized metal complexes exhibited moderate-to-very good inhibition of AChE and BChE. In vitro assays revealed that Ni complexes were most active, with the least half-maximal inhibitory concentration (IC50) values against AChE and BChE, compared to Co and Mn  complexes. Furthermore, 1, 2-diaminoethane-derived complexes were more potent, with lower IC50 values against both AChE and BChE, compared to 1,3-diaminopropane-derived complexes. Among the complexes, 4a and 5a revealed significant cholinesterase inhibitory activities relative to the standard drug, galantamine.Conclusion: All the synthesized metal complexes are active against  AChE and BChE, but only 4a and 5a are more active than the standard drug, galantamine, indicating their potential for drug development.Keywords: Flurbiprofen, Cholinesterase, Diamines, Galantamine, Metal complexes, Cholinesterase inhibitio

i-Tasmik mobile platform – enabling tahfiz student to memorize Al-Quran independently

This paper proposes a conceptual solution to assist Tahfiz student on practicing memorizing Al-Quran easily and independently. This also will help Madrasah Tahfiz to monitor their students when they went back home and for long distance student. i-Tasmik is a mobile application adopting a freemium business model which aim to provide a platform for Madrasah Tahfiz student, Ustaz and Ustazah to help enhance their Al-Quran memorization. It also has a function for Madrasah and ustaz/ustazah to monitor their students’ performance. This platform uses a voice recognition system. Nine blocks of Business Model Canvas (BMC) framework, value proposition design (VPD) and environmental map have been used as the methodologies for this paper

Insurance ratemaking using the Exponential-Lognormal regression model

This paper is concerned with presenting the Exponential-Lognormal (ELN) regression model as a competitive alternative to the Pareto, or Exponential-Inverse Gamma, regression model that has been used in a wide range of areas, including insurance ratemaking. This is the first time that the ELN regression model is used in a statistical or actuarial context. The main contribution of the study is that we illustrate how maximum likelihood estimation of the ELN regression model, which does not have a density in closed form, can be accomplished relatively easily via an Expectation-Maximisation type algorithm. A real data application based on motor insurance data is examined in order to emphasise the versatility of the proposed algorithm. Finally, assuming that the number of claims is distributed according to the classic Negative Binomial and Poisson-Inverse Gaussian regression models, both the a priori and a posteriori, or Bonus–Malus, premium rates resulting from the ELN regression model are calculated via the net premium principle and compared to those determined by the Pareto regression model that has been traditionally used for modelling claim sizes

BPPO-Based Anion Exchange Membranes for Acid Recovery via Diffusion Dialysis

To reduce the environmental impact of acids present in various industrial wastes, improved and robust anion exchange membranes (AEMs) are highly desired. Moreover, they should exhibit high retention of salts, fast acid permeation and they should be able to operate with low energy input. In this work, AEMs are prepared using a facile solution-casting from brominated poly-(2,6-dimethyl-1,4-phenylene oxide) (BPPO) and increasing amounts of 2-phenylimidazole (PI). Neither quaternary ammonium salts, nor ionic liquids and silica-containing compounds are involved in the synthesis. The prepared membranes showed an ion exchange capacity of 1.1–1.8 mmol/g, a water uptake of 22%–47%, a linear expansion ratio of 1%–6% and a tensile strength of 0.83–10.20 MPa. These membranes have potential for recovering waste acid via diffusion dialysis, as the acid dialysis coefficient (UH) at room temperature for HCl is in the range of 0.006–0.018 m/h while the separation factor (S) is in the range of 16–28, which are higher than commercial DF-120B membranes (UH = 0.004 m/h, S = 24)

Therapeutic potential and bioactive phenolics of locally grown Pakistani and Chinese varieties of ginger in relation to extraction solvents

Current study compares the Therapeutic/nutra-pharmaceuticals potential and phenolics profile of Pakistani grown Pakistani and Chinese varieties of ginger. Crude yield of bioactive components from the varieties tested, using different extraction solvents including chloroform, ethyl acetate, ether, methanol, ethanol and distilled water. The crude bioactives varied from 14.1-82.5%. The highest extraction yield was noted for Pakistani species. The HPLC analysis revalued significant amounts of phenolics including vanillin, protocatechuic, vanillic, ferulic, sinapinic and cinnamic acids. The highest anti-inflammatory activity was shown by ethanolic extract of Pakistani variety (IC50: 26.5±1.8) whereas Chinese variety exhibited potent anticancer potential against MCF-7 cell line (Inhibition: 91.38 %). The Chinese variety in general showed higher phenolics and anticancer, while the Pakistani exhibited higher anti-inflammatory activity. Pakistani grown ginger and ethanolic extract of Chinese ginger showed highest antimicrobial activity against Pseudomonas aeruginosa 18.0±0.02 & 15.00±0.02 mm respectively. Minimum results obtained with water for both varieties of ginger with range of 7.2±0.22 and 6±0.07 respectively. Moreover, the phenolics composition, anti-inflammatory, antibacterial and anticancer activities of both tested varieties of ginger were notably affected as a function of extraction solvents. Our findings advocate selection of appropriate solvent for recovery of effective phenolic bioactive compounds from ginger verities to support the Nutra-pharmaceutical formulation

Effectiveness of Natural Antioxidants against SARS-CoV-2? Insights from the In-Silico World

The SARS CoV-2 pandemic has affected millions of people around the globe. Despite many efforts to find some effective medicines against SARS CoV-2, no established therapeutics are available yet. The use of phytochemicals as antiviral agents provides hope against the proliferation of SARS-CoV-2. Several natural compounds were analyzed by virtual screening against six SARS CoV-2 protein targets using molecular docking simulations in the present study. More than a hundred plant-derived secondary metabolites have been docked, including alkaloids, flavonoids, coumarins, and steroids. SARS CoV-2 protein targets include Main protease (M(Pro)), Papain-like protease (PL(pro)), RNA-dependent RNA polymerase (RdRp), Spike glycoprotein (S), Helicase (Nsp13), and E-Channel protein. Phytochemicals were evaluated by molecular docking, and MD simulations were performed using the YASARA structure using a modified genetic algorithm and AMBER03 force field. Binding energies and dissociation constants allowed the identification of potentially active compounds. Ligand-protein interactions provide an insight into the mechanism and potential of identified compounds. Glycyrrhizin and its metabolite 18-β-glycyrrhetinic acid have shown a strong binding affinity for M(Pro), helicase, RdRp, spike, and E-channel proteins, while a flavonoid Baicalin also strongly binds against PL(pro) and RdRp. The use of identified phytochemicals may help to speed up the drug development and provide natural protection against SARS-CoV-2

Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants

Arrhythmia is considered a life-threatening disease causing serious health issues in patients, when left untreated. An early diagnosis of arrhythmias would be helpful in saving lives. This study is conducted to classify patients into one of the sixteen subclasses, among which one class represents absence of disease and the other fifteen classes represent electrocardiogram records of various subtypes of arrhythmias. The research is carried out on the dataset taken from the University of California at Irvine Machine Learning Data Repository. The dataset contains a large volume of feature dimensions which are reduced using wrapper based feature selection technique. For multiclass classification, support vector machine (SVM) based approaches including one-against-one (OAO), one-against-all (OAA), and error-correction code (ECC) are employed to detect the presence and absence of arrhythmias. The SVM method results are compared with other standard machine learning classifiers using varying parameters and the performance of the classifiers is evaluated using accuracy, kappa statistics, and root mean square error. The results show that OAO method of SVM outperforms all other classifiers by achieving an accuracy rate of 81.11% when used with 80/20 data split and 92.07% using 90/10 data split option

Symmetrical Heterocyclic Cage Skeleton: Synthesis, Urease Inhibition Activity, Kinetic Mechanistic Insight, and Molecular Docking Analyses

The present study focuses on the design and synthesis of a cage-like organic skeleton containing two triazole rings jointed via imine linkage. These molecules can act as urease inhibitors. The in-vitro urease inhibition screening results showed that the combination of the two triazole skeleton in the cage-like morphology exhibited comparable urease inhibition activity to that of the reference thiourea while the metallic complexation, especially with copper, nickel, and palladium, showed excellent activity results with IC50 values of 0.94 ± 0.13, 3.71 ± 0.61, and 7.64 ± 1.21 (3a–c), and 1.20 ± 0.52, 3.93 ± 0.45, and 12.87 ± 2.11 µM (4a–c). However, the rest of compounds among the targeted series exhibited a low to moderate enzyme inhibition potential. To better understand the compounds’ underlying mechanisms of the inhibitory effect (3a and 4a) and their most active metal complexes (3b and 4b), we performed an enzymatic kinetic analysis using the Lineweaver–Burk plot in the presence of different concentrations of inhibitors to represent the non-competitive inhibition nature of the compounds, 3a, 4a, and 4b, while mixed type inhibition was represented by the compound, 3b. Moreover, molecular docking confirmed the binding interactive behavior of 3a within the active site of the target protein

Fourth Hankel Determinant Problem Based on Certain Analytic Functions

In recent years, the Hankel determinant bounds for different subclasses of analytic, starlike and symmetric starlike functions have been discussed and studied by the many well-known authors. In this paper, we first consider a new subclass of analytic function and then we derive the fourth Hankel determinant bound for this class

Automated Wheat Diseases Classification Framework Using Advanced Machine Learning Technique

Around the world, agriculture is one of the important sectors of human life in terms of food, business, and employment opportunities. In the farming field, wheat is the most farmed crop but every year, its ultimate production is badly influenced by various diseases. On the other hand, early and precise recognition of wheat plant diseases can decrease damage, resulting in a greater yield. Researchers have used conventional and Machine Learning (ML)-based techniques for crop disease recognition and classification. However, these techniques are inaccurate and time-consuming due to the unavailability of quality data, inefficient preprocessing techniques, and the existing selection criteria of an efficient model. Therefore, a smart and intelligent system is needed which can accurately identify crop diseases. In this paper, we proposed an efficient ML-based framework for various kinds of wheat disease recognition and classification to automatically identify the brown- and yellow-rusted diseases in wheat crops. Our method consists of multiple steps. Firstly, the dataset is collected from different fields in Pakistan with consideration of the illumination and orientation parameters of the capturing device. Secondly, to accurately preprocess the data, specific segmentation and resizing methods are used to make differences between healthy and affected areas. In the end, ML models are trained on the preprocessed data. Furthermore, for comparative analysis of models, various performance metrics including overall accuracy, precision, recall, and F1-score are calculated. As a result, it has been observed that the proposed framework has achieved 99.8% highest accuracy over the existing ML techniques