Displaying publications 1 - 20 of 287 in total

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  1. Al-Qaness MAA, Ewees AA, Abualigah L, AlRassas AM, Thanh HV, Abd Elaziz M
    Entropy (Basel), 2022 Nov 17;24(11).
    PMID: 36421530 DOI: 10.3390/e24111674
    The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is an ANNs that has showed promising performance in time-series prediction. The DNR has the capability to deal with the nonlinear characteristics of historical data for time-series forecasting applications. However, it faces certain limitations in training and configuring its parameters. To this end, we utilized the power of metaheuristic optimization algorithms to boost the training process and optimize its parameters. A comprehensive evaluation is presented in this study with six MH optimization algorithms used for this purpose: whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), genetic algorithm (GA), sine-cosine algorithm (SCA), differential evolution (DE), and harmony search algorithm (HS). We used oil-production datasets for historical records of crude oil production from seven real-world oilfields (from Tahe oilfields, in China), provided by a local partner. Extensive evaluation experiments were carried out using several performance measures to study the validity of the DNR with MH optimization methods in time-series applications. The findings of this study have confirmed the applicability of MH with DNR. The applications of MH methods improved the performance of the original DNR. We also concluded that the PSO and WOA achieved the best performance compared with other methods.
  2. Ahmed A, Saeed F, Salim N, Abdo A
    J Cheminform, 2014;6:19.
    PMID: 24883114 DOI: 10.1186/1758-2946-6-19
    BACKGROUND: It is known that any individual similarity measure will not always give the best recall of active molecule structure for all types of activity classes. Recently, the effectiveness of ligand-based virtual screening approaches can be enhanced by using data fusion. Data fusion can be implemented using two different approaches: group fusion and similarity fusion. Similarity fusion involves searching using multiple similarity measures. The similarity scores, or ranking, for each similarity measure are combined to obtain the final ranking of the compounds in the database.

    RESULTS: The Condorcet fusion method was examined. This approach combines the outputs of similarity searches from eleven association and distance similarity coefficients, and then the winner measure for each class of molecules, based on Condorcet fusion, was chosen to be the best method of searching. The recall of retrieved active molecules at top 5% and significant test are used to evaluate our proposed method. The MDL drug data report (MDDR), maximum unbiased validation (MUV) and Directory of Useful Decoys (DUD) data sets were used for experiments and were represented by 2D fingerprints.

    CONCLUSIONS: Simulated virtual screening experiments with the standard two data sets show that the use of Condorcet fusion provides a very simple way of improving the ligand-based virtual screening, especially when the active molecules being sought have a lowest degree of structural heterogeneity. However, the effectiveness of the Condorcet fusion was increased slightly when structural sets of high diversity activities were being sought.

  3. Sarfaraz S, Ahmed N, Abbasi MS, Sajjad B, Vohra F, Al-Hamdan RS, et al.
    Work, 2020;67(4):791-798.
    PMID: 33325429 DOI: 10.3233/WOR-203332
    BACKGROUND: The aim of this study was to evaluate the self-perceived competency (FSPC) of medical faculty in E-Teaching and support received during the COVID-19 pandemic.

    METHODS: An online well-structured and validated faculty self-perceived competency questionnaire was used to collect responses from medical faculty. The questionnaire consisted of four purposely build sections on competence in student engagement, instructional strategy, technical communication and time management. The responses were recorded using a Likert ordinal scale (1-9). The Questionnaire was uploaded at www.surveys.google.com and the link was distributed through social media outlets and e-mails. Descriptive statistics and Independent paired t-test were used for analysis and comparison of quantitative and qualitative variables. A p-value of ≤0.05 was considered statistically significant.

    RESULTS: A total of 738 responses were assessed. Nearly 54% (397) participants had less than 5 years of teaching experience, 24.7% (182) had 6-10 years and 11.7% (86) had 11-15 years teaching expertise. 75.6% (558) respondents have delivered online lectures during the pandemic. Asynchronous methods were used by 61% (450) and synchronous by 39% (288) of participants. Moreover, 22.4% (165) participants revealed that their online lectures were evaluated by a structured feedback from experts, while 38.3% participants chose that their lectures were not evaluated. A significant difference (p 

  4. Mirghani, M.E.S., Mohammedelnour, Ahmed A., Kabbashi, Nasser A., Alam, Md Z., Musa, Khalid H., Abdullah, Aminah.
    MyJurnal
    Acacia polyacantha gum (APG) is a dried exudate which obtained from the stems and branches of Acacia polyacantha trees. APG is rich in soluble dietary fibers as well as organic compounds. In this study quantitation of the levels of total phenolics content (TPC) and antioxidant activities were conducted using ABTS and CUPRAC assays for APG extraction using pure solvents (methanol, ethanol, acetone) and their aqueous mixtures at 50% and 100%. The antioxidant levels were evaluated by 2,2’-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS+) radical cation deculturization and cupric iron reducing capacity in the presence of neocuproine (CUPRAC) for the evaluation of reducing power, and (TPC) was evaluated by the Folin-Ciocalteu method. The solvent Methanol (50%) gave the best extraction ratio for APG presented by highest (TPC 60.78 mg GAE/100g of DW, CUPRAC 34.65 mg TE/100g DW, and, ABTS about 37.65 mg TE/100g DW respectively), followed by ethanol 50% extract. On the other hand, pure methanol showed the lowest TPC 5.33 mg GAE/100g of DW, ABTS 10.9 mg TE/100g DW, and CUPRAC 7.80 mg TE/100g DW, values respectively. Therefore, the variation in the antioxidant capacity of extracts was possibly due to the difference of polarity, immiscibility and the nature of the APG compounds extracted using various solvents. The higher content of antioxidant activity in APG shall be useful to human health if it is properly utilized.
  5. Gharaibeh M, Alfwares AA, Elobeid E, Khasawneh R, Rousan L, El-Heis M, et al.
    Front Med (Lausanne), 2023;10:1276434.
    PMID: 38076239 DOI: 10.3389/fmed.2023.1276434
    AIMS: To assess the diagnostic performance of digital breast tomosynthesis (DBT) in older women across varying breast densities and to compare its effectiveness for cancer detection with 2D mammography and ultrasound (U/S) for different breast density categories. Furthermore, our study aimed to predict the potential reduction in unnecessary additional examinations among older women due to DBT.

    METHODS: This study encompassed a cohort of 224 older women. Each participant underwent both 2D mammography and digital breast tomosynthesis examinations. Supplementary views were conducted when necessary, including spot compression and magnification, ultrasound, and recommended biopsies. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) were calculated for 2D mammography, DBT, and ultrasound. The impact of DBT on diminishing the need for supplementary imaging procedures was predicted through binary logistic regression.

    RESULTS: In dense breast tissue, DBT exhibited notably heightened sensitivity and NPV for lesion detection compared to non-dense breasts (61.9% vs. 49.3%, p  0.05) between DBT and the four dependent variables.

    CONCLUSION: Our findings indicate that among older women, DBT does not significantly decrease the requirement for further medical examinations.

  6. Talab F, Alam A, Zainab, Ullah S, Elhenawy AA, Shah SAA, et al.
    J Biomol Struct Dyn, 2024 Feb 22.
    PMID: 38385366 DOI: 10.1080/07391102.2024.2319677
    This research work reports the synthesis of new derivatives of the hydrazone Schiff bases (1-17) based on polyhydroquinoline nucleus through multistep reactions. HR-ESIMS,1H- and 13C-NMR spectroscopy were used to structurally infer all of the synthesized compounds and lastly evaluated for prolyl oligopeptidase inhibitory activity. All the prepared products displayed good to excellent inhibitory activity when compared with standard z-prolyl-prolinal. Three derivatives 3, 15 and 14 showed excellent inhibition with IC50 values 3.21 ± 0.15 to 5.67 ± 0.18 µM, while the remaining 12 compounds showed significant activity. Docking studies indicated a good correlation with the biochemical potency of compounds estimated in the in-vitro test and showed the potency of compounds 3, 15 and 14. The MD simulation results confirmed the stability of the most potent inhibitors 3, 15 and 14 at 250 ns using the parameters RMSD, RMSF, Rg and number of hydrogen bonds. The RMSD values indicate the stability of the protein backbone in complex with the inhibitors over the simulation time. The RMSF values of the binding site residues indicate that the potent inhibitors contributed to stabilizing these regions of the protein, through formed stable interactions with the protein. The Rg. analysis assesses the overall size and compactness of the complexes. The maintenance of stable hydrogen bonds suggests the existence of favorable binding interactions. SASA analysis suggests that they maintained stable conformations without large-scale exposure to the solvent. These results indicate that the ligand-protein interactions are stable and could be exploited to design new drugs for disease treatment.Communicated by Ramaswamy H. Sarma.
  7. Umar MI, Asmawi MZ, Sadikun A, Atangwho IJ, Yam MF, Altaf R, et al.
    Molecules, 2012 Jul 23;17(7):8720-34.
    PMID: 22825623 DOI: 10.3390/molecules17078720
    This study evaluated the anti-inflammatory effect of Kaempferia galanga (KG) using an activity-guided approach. KG rhizomes were serially extracted with petroleum ether, chloroform, methanol and water. These extracts (2 g/kg each) were tested for their ability to inhibit carrageenan-induced rat paw edema. The chloroform extract was found to exert the highest inhibition (42.9%) compared to control (p < 0.001), hence it was further fractionated by washing serially with hexane, hexane-chloroform (1:1) and chloroform. The chloroform fraction (1 g/kg) showed the highest inhibitory effect (51.9%, (p < 0.001), on carrageenan-induced edema. This chloroform fraction was further fractionated with hexane-chloroform (1:3) and chloroform, and of the two fractions, the hexane-chloroform sub-fraction was the most effective in inhibiting edema (53.7%, p < 0.001). GC-MS analysis of the active sub-fraction identified ethyl-p-methoxycinnamate (EPMC) as the major component, which was re-crystallized. EPMC dose-dependently inhibited carrageenan-induced edema with an MIC of 100 mg/kg. Moreover, in an in vitro study, EPMC non-selectively inhibited the activities of cyclooxygenases 1 and 2, with IC₅₀ values of 1.12 µM and 0.83 µM respectively. These results validate the anti-inflammatory activity of KG which may be exerted by the inhibition of cyclooxygenases 1 and 2. EPMC isolated from this plant may be the active anti-inflammatory agent.
  8. Abdo A, Salim N, Ahmed A
    J Biomol Screen, 2011 Oct;16(9):1081-8.
    PMID: 21862688 DOI: 10.1177/1087057111416658
    Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-based virtual screening. To this end, different models of the Bayesian network have been developed. In our previous works, the retrieval performance of the Bayesian network was observed to improve significantly when multiple reference structures or fragment weightings were used. In this article, the authors enhance the Bayesian inference network (BIN) using the relevance feedback information. In this approach, a few high-ranking structures of unknown activity were filtered from the outputs of BIN, based on a single active reference structure, to form a set of active reference structures. This set of active reference structures was used in two distinct techniques for carrying out such BIN searching: reweighting the fragments in the reference structures and group fusion techniques. Simulated virtual screening experiments with three MDL Drug Data Report data sets showed that the proposed techniques provide simple ways of enhancing the cost-effectiveness of ligand-based virtual screening searches, especially for higher diversity data sets.
  9. Himmat M, Salim N, Al-Dabbagh MM, Saeed F, Ahmed A
    Molecules, 2016 Apr 13;21(4):476.
    PMID: 27089312 DOI: 10.3390/molecules21040476
    Quantifying the similarity of molecules is considered one of the major tasks in virtual screening. There are many similarity measures that have been proposed for this purpose, some of which have been derived from document and text retrieving areas as most often these similarity methods give good results in document retrieval and can achieve good results in virtual screening. In this work, we propose a similarity measure for ligand-based virtual screening, which has been derived from a text processing similarity measure. It has been adopted to be suitable for virtual screening; we called this proposed measure the Adapted Similarity Measure of Text Processing (ASMTP). For evaluating and testing the proposed ASMTP we conducted several experiments on two different benchmark datasets: the Maximum Unbiased Validation (MUV) and the MDL Drug Data Report (MDDR). The experiments have been conducted by choosing 10 reference structures from each class randomly as queries and evaluate them in the recall of cut-offs at 1% and 5%. The overall obtained results are compared with some similarity methods including the Tanimoto coefficient, which are considered to be the conventional and standard similarity coefficients for fingerprint-based similarity calculations. The achieved results show that the performance of ligand-based virtual screening is better and outperforms the Tanimoto coefficients and other methods.
  10. Teng S, Khong KW, Pahlevan Sharif S, Ahmed A
    JMIR Public Health Surveill, 2020 10 01;6(4):e19618.
    PMID: 33001036 DOI: 10.2196/19618
    BACKGROUND: Poor nutrition and food selection lead to health issues such as obesity, cardiovascular disease, diabetes, and cancer. This study of YouTube comments aims to uncover patterns of food choices and the factors driving them, in addition to exploring the sentiments of healthy eating in networked communities.

    OBJECTIVE: The objectives of the study are to explore the determinants, motives, and barriers to healthy eating behaviors in online communities and provide insight into YouTube video commenters' perceptions and sentiments of healthy eating through text mining techniques.

    METHODS: This paper applied text mining techniques to identify and categorize meaningful healthy eating determinants. These determinants were then incorporated into hypothetically defined constructs that reflect their thematic and sentimental nature in order to test our proposed model using a variance-based structural equation modeling procedure.

    RESULTS: With a dataset of 4654 comments extracted from YouTube videos in the context of Malaysia, we apply a text mining method to analyze the perceptions and behavior of healthy eating. There were 10 clusters identified with regard to food ingredients, food price, food choice, food portion, well-being, cooking, and culture in the concept of healthy eating. The structural equation modeling results show that clusters are positively associated with healthy eating with all P values less than .001, indicating a statistical significance of the study results. People hold complex and multifaceted beliefs about healthy eating in the context of YouTube videos. Fruits and vegetables are the epitome of healthy foods. Despite having a favorable perception of healthy eating, people may not purchase commonly recognized healthy food if it has a premium price. People associate healthy eating with weight concerns. Food taste, variety, and availability are identified as reasons why Malaysians cannot act on eating healthily.

    CONCLUSIONS: This study offers significant value to the existing literature of health-related studies by investigating the rich and diverse social media data gleaned from YouTube. This research integrated text mining analytics with predictive modeling techniques to identify thematic constructs and analyze the sentiments of healthy eating.

  11. Sawal I, Ahmad S, Tariq W, Tahir MJ, Essar MY, Ahmed A
    J Med Virol, 2021 09;93(9):5228-5230.
    PMID: 33942326 DOI: 10.1002/jmv.27031
  12. Ullah I, Hassan W, Tahir MJ, Ahmed A
    J Med Virol, 2021 Oct;93(10):5689-5690.
    PMID: 34143897 DOI: 10.1002/jmv.27134
  13. Tahir MJ, Saqlain M, Tariq W, Waheed S, Tan SHS, Nasir SI, et al.
    BMC Public Health, 2021 09 26;21(1):1759.
    PMID: 34565351 DOI: 10.1186/s12889-021-11814-5
    BACKGROUND: While vaccine development is itself a challenge; ensuring optimal vaccine uptake at population level can present an even more significant challenge. Therefore, this study aimed to assess the Pakistani population's attitude and preferences towards the Coronavirus disease 2019 (COVID-19) vaccine.

    METHOD: A cross-sectional study was carried out through an online self-administered questionnaire from 27 September 2020 to 11 October 2020. A total of 883 people responded to the survey. The questionnaire included the participants' socio-demographic variables, attitudes, beliefs towards the COVID-19 vaccine and acceptance and rejection of vaccination, and reasons for them. Logistic regression analysis was used to analyze the predictors for vaccine acceptance and willingness to pay for the vaccine.

    RESULTS: A majority (70.8%) of respondents will accept the COVID-19vaccine if available, and 66.8% showed a positive attitude towards vaccination. Monthly family income, education level, self-diagnosis of COVID-19 or a friend, family member, or colleague are significant factors influencing the acceptance of COVID-19 vaccination. The dogma of being naturally immune to COVID-19 was a key reason for the refusal of the vaccine. Less than half (48%) of those who refuse will vaccinate themselves if government officials have made it compulsory. A third (33.9%) of participants were willing to pay up to (7 USD) 1000 Pkr (Pakistani Rupees) for the vaccine.

    CONCLUSION: The population's positive attitude should be improved by increasing awareness and eradicating false myths about vaccines through large-scale campaigns.

  14. Ahmad A, Zulaily N, Shahril MR, Syed Abdullah EFH, Ahmed A
    PLoS One, 2018;13(7):e0200577.
    PMID: 30044842 DOI: 10.1371/journal.pone.0200577
    The epidemic of obesity in developed countries is commonly associated with poor dietary habit and sedentary lifestyle. However, other determinants, including education background and family income, may contribute towards the problem especially in developing countries. This study aimed to determine the influence of socioeconomic status (SES) on obesity among 12-year-old school adolescents in Terengganu, Malaysia. Body weight and height were measured and BMI was categorised based on WHO z-score cut-off points. Information was obtained from self-reported questionnaire on parents' education background, family income and occupation. A total of 3,798 school adolescents aged 12 years (44% boys and 56% girls) were recruited. There was no significant difference in BMI status between boys and girls, or between rural and urban participants. There were significant differences between BMI categories and gender, household income and SES level within rural areas. In the urban areas, significant differences were found between BMI categories and gender, parents' occupational and educational level, household income and size, and SES level. A logistic regression model found several SES factors to be predictors of obesity in this population, namely, gender, household size, father's occupation level, household income level and SES level. Each component of SES has been significantly associated with the BMI category of school adolescents, particularly in the urban areas. This suggests the requirement of multifaceted approaches, including the role of family, society and authorities, in the effort to curtail adolescent obesity.
  15. Anwar A, Kee DMH, Ahmed A
    Cyberpsychol Behav Soc Netw, 2020 May;23(5):290-296.
    PMID: 32282237 DOI: 10.1089/cyber.2019.0407
    Workplace cyberbullying (WCB) is a new form of hostility in organizations in which information technology is used as a means to bully employees. The objective of this study is to determine the association between WCB and the interpersonal deviance (ID) of victims through parallel mediation through the ineffectual silence of employees and emotional exhaustion (EE). Conservation of resource (COR) theory and affective events theory were used as the study's guiding framework, and data were drawn from 351 white-collar employees who were employed in a variety of industries-such as banking, telecommunications sector, education, health care, insurance, and consultancy-in Lahore, Pakistan. The results show that ineffectual silence negatively mediated the relationship between cyberbullying and deviance, decreasing the level of deviance of employees who used silence as a coping mechanism. EE, however, positively mediated the relationship between cyberbullying and deviance. This means that when employees felt emotionally overwhelmed they retaliated by engaging in deviant behaviors and acting as a bully toward colleagues. Drawing on the COR theory and the affective events theory, the findings show that WCB has an impact on ID. From a practical standpoint, the study reveals that WCB can lead to ID and it also may associate with large financial costs and workplace disruptions. Thus, organizations should establish a culture that prevent employees from engaging in WCB and adopt practices of prevention and intervention because it is not only harmful to the employees but also to the organization.
  16. Zaman K, Rahim F, Taha M, Wadood A, Adnan Ali Shah S, Gollapalli M, et al.
    Bioorg Chem, 2019 08;89:102999.
    PMID: 31151055 DOI: 10.1016/j.bioorg.2019.102999
    Isoquinoline analogues (KA-1 to 16) have been synthesized and evaluated for their E. coli thymidine phosphorylase inhibitory activity. Except compound 11, all other analogs showed outstanding thymidine inhibitory potential ranging in between 4.40 ± 0.20 to 69.30 ± 1.80 µM when compared with standard drug 7-Deazaxanthine (IC50 = 38.68 ± 4.42 µM). Structure Activity Relationships has been established for all compounds, mainly based on substitution pattern on phenyl ring. All analogs were characterized by various spectroscopic techniques such as 1H NMR, 13C NMR and EI-MS. The binding interactions of isoquinoline analogues with the active site of TP enzyme, the molecular docking studies were performed. Furthermore, the angiogenic inhibitory potentials of isoquinoline analogues (KA-1-9, 14, 12 and 16) were determined in the presence of standard drug Dexamethasone based on percentage inhibitions at various concentrations. Herein this work analogue KA-12, 14 and 16 emerged with most potent angiogenic inhibitory potentials among the synthesized analogues.
  17. Ahmad A, Zulaily N, Shahril MR, Wafa SW, Mohd Amin R, Piernas C, et al.
    BMC Pediatr, 2021 09 23;21(1):418.
    PMID: 34556071 DOI: 10.1186/s12887-021-02899-3
    BACKGROUND: Childhood obesity has been associated with increased odds of adult obesity and co-morbidities in later life. Finding the key determinants may help in designing the most appropriate and effective interventions to prevent obesity. This study aimed to identify the determinants of obesity among school adolescents in a sub-urban state of Malaysia.

    METHODS: This cross-sectional study involved 1,404 school adolescents aged 12 years (46% boys and 54% girls). Socio-demographic, dietary and physical activity data were collected using questionnaires whilst body weight and height were measured and body mass index was classified based on WHO BMI-for-age Z-scores cut-off.

    RESULTS: A multivariable linear regression model showed that BMI z-score was positively associated with parents' BMI (P<0.001), birth weight (P=0.003), and serving size of milk and dairy products (P=0.036) whilst inversely associated with household size (P=0.022). Overall, 13.1% of the variances in BMI Z-scores were explained by parents' BMI, birth weight, servings of milk and dairy products and household size.

    CONCLUSION: This study found important determinants of body weight status among adolescents mainly associated with family and home environmental factor. This evidence could help to form the effective and tailored strategies at the earliest stage to prevent obesity in this population.

  18. Lim WX, Chen Z, Ahmed A
    Med Biol Eng Comput, 2022 Mar;60(3):633-642.
    PMID: 35083634 DOI: 10.1007/s11517-021-02487-8
    Diabetic retinopathy (DR) is a chronic eye condition that is rapidly growing due to the prevalence of diabetes. There are challenges such as the dearth of ophthalmologists, healthcare resources, and facilities that are unable to provide patients with appropriate eye screening services. As a result, deep learning (DL) has the potential to play a critical role as a powerful automated diagnostic tool in the field of ophthalmology, particularly in the early detection of DR when compared to traditional detection techniques. The DL models are known as black boxes, despite the fact that they are widely adopted. They make no attempt to explain how the model learns representations or why it makes a particular prediction. Due to the black box design architecture, DL methods make it difficult for intended end-users like ophthalmologists to grasp how the models function, preventing model acceptance for clinical usage. Recently, several studies on the interpretability of DL methods used in DR-related tasks such as DR classification and segmentation have been published. The goal of this paper is to provide a detailed overview of interpretability strategies used in DR-related tasks. This paper also includes the authors' insights and future directions in the field of DR to help the research community overcome research problems.
  19. Tahir MJ, Sawal I, Essar MY, Jabbar A, Ullah I, Ahmed A
    Infect Control Hosp Epidemiol, 2022 Nov;43(11):1758-1759.
    PMID: 34308811 DOI: 10.1017/ice.2021.342
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