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  1. Mi Alnaser F, Rahi S, Alghizzawi M, Ngah AH
    Heliyon, 2023 Aug;9(8):e18930.
    PMID: 37600389 DOI: 10.1016/j.heliyon.2023.e18930
    In the era disruptive technology the emergence of artificial intelligence has fundamentally improved banking operations. The execution of artificial intelligence is no longer discretionary for financial institutions and now it is considered an essential tool to meet customer expectations. Although artificial intelligence enabled digital banking is faster efficient and effective however user acceptance of digital banking driven by artificial intelligence is in its initial stages. Therefore, current study develops and integrated research framework with expectation confirmation model and examines digital banking user satisfaction and acceptance of AI enabled digital banking. Data were collected from digital banking user through structured questionnaire. Overall, 320 respondents were approached and requested to participate in digital banking survey. In return 251 valid responses were received and analyzed with structural equation modeling. Findings of the structural model indicate that satisfaction is jointly determined by expectation confirmation, perceived performance, trendiness, visual attractiveness, problem solving, customization, communication quality and revealed substantial variance R^2 51.1% in digital banking user satisfaction. Therefore, satisfaction and corporate reputation have shown considerable variance R^2 48.3 in user acceptance of AI enabled digital banking. Moreover, the research framework has revealed substantial predictive power Q^2 0.449 to predict digital banking user satisfaction and Q^2 0.493 user acceptance of artificial intelligence enabled digital banking. Concerning with hypotheses relationships exogenous factors have shown positive and significant impact user satisfaction except trendiness and customization. Practically, this research has suggested that policy makers should pay attention in improving user expectation confirmation, perceived performance, visual attractiveness, communication quality and corporate reputation which in turn enhance satisfaction and boost digital banking user's confidence to accept artificial intelligence enabled digital banking. This study is original as it integrates expectation confirmation model with the antecedents of artificial intelligence and examines user behavior towards acceptance of artificial intelligence enabled digital banking.
  2. Zaid OI, Majid RA, Hasidah MS, Sabariah MN, Al-Zihiry K, Rahi S, et al.
    Pharmacogn Mag, 2017 Jan;13(Suppl 1):S48-S55.
    PMID: 28479726 DOI: 10.4103/0973-1296.203982
    BACKGROUND: Emergence of chloroquine (CQ) resistance among different strains of Plasmodium falciparum is the worst catastrophe that has ever perplexed the dedicated efforts to eradicate malaria. This urged the scientists to search for new alternatives or sensitizers to augment its antiplasmodium effect.

    MATERIALS AND METHOD: In this experiment, the potential of embelin, isolated from Embelia ribes, to inhibit the growth and sensitize CQ action was screened using SYBRE-green-I based drug sensitivity and isobologram assays, respectively. Its effect on red blood cells stability was screened to assess its safety. To explore its molecular mechanism, its effect on plasmodial Hemozoin and the in vitro β-hematin formation was screened as well. Furthermore, its anti-oxidant activity was measured using the conventional in vitro tests and its molecular characters were obtained using Molispiration program.

    RESULTS: The results showed that its anti-plasmodial effect was weaker than CQ but synergism was obtained when they were combined at ratios lower than 5:5 CQ/embelin. Furthermore, β-hematin formation was inhibited by embelin without showing any synergism after mixing with CQ.

    CONCLUSION: Overall, embelin is not ideal to be suggested as a conventional antiplasmodium but it has a potential to ameliorate CQ resistance. Furthermore, its action is not related to its impact on hemozoin formation. Further, investigations are recommended to illustrate its detailed mechanism of action. Abbreviation used: CQ-DV-PBS-HEPES: Chloroquine-Digestive vacuole-Phosphate-buffer-saline-4-(2-hydroxyethyl-1-piperazin-ethan-sulphoni-acid), EDTA: Ethylen-diamin-tetra-acetic-acid, g.m.wt: Gram molecular weight, cMCM: Complete-malaria-culture-medium, Hct: Hematocrite, PRBCs: Parasitized-redblood-cells, nRBCs: Normal-red-blood-cells, RT: Room temperature, IC: Inhibitory concentration, FIC: Fractional inhibitory concentration, iCM: Incomplete-culturemedium, BSA: Bovin serum albumin, MTT: 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, DPPH: 2,2-diphenyl-1- picrylhydrazy, BHT: Butylatedhydroxyl-toleuen, PSA: Polar surface area, ClogP: Log partition coefficient (octanol/water), GPCR: G-protein-coupled-receptors, DMSO: Dimethylsulphoxide, NaOH: Sodium hydroxide.

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