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  1. Fulltext Parkinson's Disease Diagnosis Using Laplacian Score, Gaussian Process Regression and Self-Organizing Maps
    Nilashi M, Abumalloh RA, Alyami S, Alghamdi A, Alrizq M
    Brain Sci, 2023 Mar 24;13(4).
    PMID: 37190508 DOI: 10.3390/brainsci13040543
    Parkinson's disease (PD) is a complex degenerative brain disease that affects nerve cells in the brain responsible for body movement. Machine learning is widely used to track the progression of PD in its early stages by predicting unified Parkinson's disease rating scale (UPDRS) scores. In this paper, we aim to develop a new method for PD diagnosis with the aid of supervised and unsupervised learning techniques. Our method is developed using the Laplacian score, Gaussian process regression (GPR) and self-organizing maps (SOM). SOM is used to segment the data to handle large PD datasets. The models are then constructed using GPR for the prediction of the UPDRS scores. To select the important features in the PD dataset, we use the Laplacian score in the method. We evaluate the developed approach on a PD dataset including a set of speech signals. The method was evaluated through root-mean-square error (RMSE) and adjusted R-squared (adjusted R²). Our findings reveal that the proposed method is efficient in the prediction of UPDRS scores through a set of speech signals (dysphonia measures). The method evaluation showed that SOM combined with the Laplacian score and Gaussian process regression with the exponential kernel provides the best results for R-squared (Motor-UPDRS = 0.9489; Total-UPDRS = 0.9516) and RMSE (Motor-UPDRS = 0.5144; Total-UPDRS = 0.5105) in predicting UPDRS compared with the other kernels in Gaussian process regression.
  2. Early diagnosis of Parkinson's disease: A combined method using deep learning and neuro-fuzzy techniques
    Nilashi M, Abumalloh RA, Yusuf SYM, Thi HH, Alsulami M, Abosaq H, et al.
    Comput Biol Chem, 2023 Feb;102:107788.
    PMID: 36410240 DOI: 10.1016/j.compbiolchem.2022.107788
    Predicting Unified Parkinson's Disease Rating Scale (UPDRS) in Total- UPDRS and Motor-UPDRS clinical scales is an important part of controlling PD. Computational intelligence approaches have been used effectively in the early diagnosis of PD by predicting UPDRS. In this research, we target to present a combined approach for PD diagnosis using an ensemble learning approach with the ability of online learning from clinical large datasets. The method is developed using Deep Belief Network (DBN) and Neuro-Fuzzy approaches. A clustering approach, Expectation-Maximization (EM), is used to handle large datasets. The Principle Component Analysis (PCA) technique is employed for noise removal from the data. The UPDRS prediction models are constructed for PD diagnosis. To handle the missing data, K-NN is used in the proposed method. We use incremental machine learning approaches to improve the efficiency of the proposed method. We assess our approach on a real-world PD dataset and the findings are assessed compared to other PD diagnosis approaches developed by machine learning techniques. The findings revealed that the approach can improve the UPDRS prediction accuracy and the time complexity of previous methods in handling large datasets.
  3. The nexus between quality of customer relationship management systems and customers' satisfaction: Evidence from online customers' reviews
    Nilashi M, Abumalloh RA, Ahmadi H, Samad S, Alrizq M, Abosaq H, et al.
    Heliyon, 2023 Nov;9(11):e21828.
    PMID: 38034804 DOI: 10.1016/j.heliyon.2023.e21828
    Customer Relationship Management (CRM) is a method of management that aims to establish, develop, and improve relationships with targeted customers in order to maximize corporate profitability and customer value. There have been many CRM systems in the market. These systems are developed based on the combination of business requirements, customer needs, and industry best practices. The impact of CRM systems on the customers' satisfaction and competitive advantages as well as tangible and intangible benefits are widely investigated in the previous studies. However, there is a lack of studies to assess the quality dimensions of these systems to meet an organization's CRM strategy. This study aims to investigate customers' satisfaction with CRM systems through online reviews. We collected 5172 online customers' reviews from 8 CRM systems in the Google play store platform. The satisfaction factors were extracted using Latent Dirichlet Allocation (LDA) and grouped into three dimensions; information quality, system quality, and service quality. Data segmentation is performed using Learning Vector Quantization (LVQ). In addition, feature selection is performed by the entropy-weight approach. We then used the Adaptive Neuro Fuzzy Inference System (ANFIS), the hybrid of fuzzy logic and neural networks, to assess the relationship between these dimensions and customer satisfaction. The results are discussed and research implications are provided.
  4. Fulltext A combined method of optimized learning vector quantization and neuro-fuzzy techniques for predicting unified Parkinson's disease rating scale using vocal features
    Zogaan WA, Nilashi M, Ahmadi H, Abumalloh RA, Alrizq M, Abosaq H, et al.
    MethodsX, 2024 Jun;12:102553.
    PMID: 38292319 DOI: 10.1016/j.mex.2024.102553
    Parkinson's Disease (PD) is a common disorder of the central nervous system. The Unified Parkinson's Disease Rating Scale or UPDRS is commonly used to track PD symptom progression because it displays the presence and severity of symptoms. To model the relationship between speech signal properties and UPDRS scores, this study develops a new method using Neuro-Fuzzy (ANFIS) and Optimized Learning Rate Learning Vector Quantization (OLVQ1). ANFIS is developed for different Membership Functions (MFs). The method is evaluated using Parkinson's telemonitoring dataset which includes a total of 5875 voice recordings from 42 individuals in the early stages of PD which comprises 28 men and 14 women. The dataset is comprised of 16 vocal features and Motor-UPDRS, and Total-UPDRS. The method is compared with other learning techniques. The results show that OLVQ1 combined with the ANFIS has provided the best results in predicting Motor-UPDRS and Total-UPDRS. The lowest Root Mean Square Error (RMSE) values (UPDRS (Total)=0.5732; UPDRS (Motor)=0.5645) and highest R-squared values (UPDRS (Total)=0.9876; UPDRS (Motor)=0.9911) are obtained by this method. The results are discussed and directions for future studies are presented.i.ANFIS and OLVQ1 are combined to predict UPDRS.ii.OLVQ1 is used for PD data segmentation.iii.ANFIS is developed for different MFs to predict Motor-UPDRS and Total-UPDRS.
  5. Parkinson's disease diagnosis using deep learning: A bibliometric analysis and literature review
    Abumalloh RA, Nilashi M, Samad S, Ahmadi H, Alghamdi A, Alrizq M, et al.
    Ageing Res Rev, 2024 Apr;96:102285.
    PMID: 38554785 DOI: 10.1016/j.arr.2024.102285
    Parkinson's Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis.
  6. Fulltext What is the impact of service quality on customers' satisfaction during COVID-19 outbreak? New findings from online reviews analysis
    Nilashi M, Abumalloh RA, Alghamdi A, Minaei-Bidgoli B, Alsulami AA, Thanoon M, et al.
    Telemat Inform, 2021 Nov;64:101693.
    PMID: 34887617 DOI: 10.1016/j.tele.2021.101693
    The COVID-19 pandemic has caused major global changes both in the areas of healthcare and economics. This pandemic has led, mainly due to conditions related to confinement, to major changes in consumer habits and behaviors. Although there have been several studies on the analysis of customers' satisfaction through survey-based and online customers' reviews, the impact of COVID-19 on customers' satisfaction has not been investigated so far. It is important to investigate dimensions of satisfaction from the online customers' reviews to reveal their preferences on the hotels' services during the COVID-19 outbreak. This study aims to reveal the travelers' satisfaction in Malaysian hotels during the COVID-19 outbreak through online customers' reviews. In addition, this study investigates whether service quality during COVID-19 has an impact on hotel performance criteria and consequently customers' satisfaction. Accordingly, we develop a new method through machine learning approaches. The method is developed using text mining, clustering, and prediction learning techniques. We use Latent Dirichlet Allocation (LDA) for big data analysis to identify the voice-of-the-customer, Expectation-Maximization (EM) for clustering, and ANFIS for satisfaction level prediction. In addition, we use Higher-Order Singular Value Decomposition (HOSVD) for missing value imputation. The data was collected from TripAdvisor regarding the travelers' concerns in the form of online reviews on the COVID-19 outbreak and numerical ratings on hotel services from different perspectives. The results from the analysis of online customers' reviews revealed that service quality during COVID-19 has an impact on hotel performance criteria and consequently customers' satisfaction. In addition, the results showed that although the customers are always seeking hotels with better performance, they are also concerned with the quality of related services in the COVID-19 outbreak.
  7. Deciphering Mutational Impacts on c-Src-HK2 Interaction in Colorectal Cancer Progression, and Identification of Potential Phytocompounds Inhibitors: A Molecular Simulation and Free Energy Calculation Approach
    Suleman M, Faizullah, Khan A, Mohammad Sayaf A, Alghamdi A, Alghamdi SA, et al.
    Curr Med Chem, 2024 Aug 27.
    PMID: 39253929 DOI: 10.2174/0109298673311962240815055821
    BACKGROUND: Colorectal cancer (CRC) stands as the third most widespread cancer worldwide in both men and women, witnessing a concerning rise, especially in younger demographics. Abnormal activation of the Non-Receptor Tyrosine Kinase c-Src has been linked to the advancement of several human cancers, including colorectal, breast, lung, and pancreatic ones. The interaction between c-Src and Hexokinase 2 (HK2) triggers enzyme phosphorylation, significantly boosting glycolysis, and ultimately contributing to the development of CRC.

    OBJECTIVES: The objectives of this study are to examine the influence of newly identified mutations on the interaction between c-Src and the HK2 enzyme and to discover potent phytocompounds capable of disrupting this interaction.

    METHODS: In this study, we utilized molecular docking to check the effect of the identified mutation on the binding of c-Src with HK2. Virtual drug screening, MD simulation, and binding free energy were employed to identify potent drugs against the binding interface of c-Src and HK2.

    RESULTS: Among these mutations, six (W151C, L272P, A296S, A330D, R391H, and P434A) were observed to significantly disrupt the stability of the c-Src structure. Additionally, through molecular docking analysis, we demonstrated that the mutant forms of c-Src exhibited high binding affinities with HK2. The wildtype showed a docking score of -271.80 kcal/mol, while the mutants displayed scores of -280.77 kcal/mol, -369.01 kcal/mol, -324.41 kcal/mol, -362.18 kcal/mol, 266.77 kcal/mol, and -243.28 kcal/mol for W151C, L272P, A296S, A330D, R391H, and P434A respectively. Furthermore, we identified five lead phytocompounds showing strong potential to impede the binding of c-Src with HK2 enzyme, essential for colon cancer progression. These compounds exhibit robust bonding with c-Src with docking scores of -7.37 kcal/mol, -7.26 kcal/mol, -6.88 kcal/mol, -6.81 kcal/mol, and -6.73 kcal/mol. Moreover, these compounds demonstrate dynamic stability, structural compactness, and the lowest residual fluctuation during MD simulation. The binding free energies for the top five hits (-42.44±0.28 kcal/mol, -28.31±0.25 kcal/mol, -34.95±0.44 kcal/mol, -38.92±0.25 kcal/mol, and -30.34±0.27 kcal/mol), further affirm the strong interaction of these drugs with c-Src which might impede the cascade of events that drive the progression of colon cancer.

    CONCLUSION: Our findings serve as a promising foundation, paving the way for future discoveries in the fight against colorectal cancer.

  8. Fulltext COVID-19 and sustainable development goals: A bibliometric analysis and SWOT analysis in Malaysian context
    Nilashi M, Ali Abumalloh R, Mohd S, Nurlaili Farhana Syed Azhar S, Samad S, Hang Thi H, et al.
    Telemat Inform, 2023 Jan;76:101923.
    PMID: 36510580 DOI: 10.1016/j.tele.2022.101923
    The COVID-19 crisis has been a core threat to the lives of billions of individuals over the world. The COVID-19 crisis has influenced governments' aims to meet UN Sustainable Development Goals (SDGs); leading to exceptional conditions of fragility, poverty, job loss, and hunger all over the world. This study aims to investigate the current studies that concentrate on the COVID-19 crisis and its implications on SDGs using a bibliometric analysis approach. The study also deployed the Strengths, Weaknesses, Opportunities, and Threats (SWOT) approach to perform a systematic analysis of the SDGs, with an emphasis on the COVID-19 crisis impact on Malaysia. The results of the study indicated the unprecedented obstacles faced by countries to meet the UN's SDGs in terms of implementation, coordination, trade-off decisions, and regional issues. The study also stressed the impact of COVID-19 on the implementation of the SDGs focusing on the income, education, and health aspects. The outcomes highlighted the emerging opportunities of the crisis that include an improvement in the health sector, the adoption of online modes in education, the swift digital transformation, and the global focus on environmental issues. Our study demonstrated that, in the post-crisis time, the ratio of citizens in poverty could grow up more than the current national stated values. We stressed the need to design an international agreement to reconsider the implementation of SDGs, among which, are strategic schemes to identify vital and appropriate policies.
  9. Fulltext Medical image processing and COVID-19: A literature review and bibliometric analysis
    Abumalloh RA, Nilashi M, Yousoof Ismail M, Alhargan A, Alghamdi A, Alzahrani AO, et al.
    J Infect Public Health, 2022 Jan;15(1):75-93.
    PMID: 34836799 DOI: 10.1016/j.jiph.2021.11.013
    COVID-19 crisis has placed medical systems over the world under unprecedented and growing pressure. Medical imaging processing can help in the diagnosis, treatment, and early detection of diseases. It has been considered as one of the modern technologies applied to fight against the COVID-19 crisis. Although several artificial intelligence, machine learning, and deep learning techniques have been deployed in medical image processing in the context of COVID-19 disease, there is a lack of research considering systematic literature review and categorization of published studies in this field. A systematic review locates, assesses, and interprets research outcomes to address a predetermined research goal to present evidence-based practical and theoretical insights. The main goal of this study is to present a literature review of the deployed methods of medical image processing in the context of the COVID-19 crisis. With this in mind, the studies available in reliable databases were retrieved, studied, evaluated, and synthesized. Based on the in-depth review of literature, this study structured a conceptual map that outlined three multi-layered folds: data gathering and description, main steps of image processing, and evaluation metrics. The main research themes were elaborated in each fold, allowing the authors to recommend upcoming research paths for scholars. The outcomes of this review highlighted that several methods have been adopted to classify the images related to the diagnosis and detection of COVID-19. The adopted methods have presented promising outcomes in terms of accuracy, cost, and detection speed.
  10. Fulltext Phytopharmacological Evaluation of Different Solvent Extract/Fractions From Sphaeranthus indicus L. Flowers: From Traditional Therapies to Bioactive Compounds
    Ahmad HI, Nadeem MF, Shoaib Khan HM, Sarfraz M, Saleem H, Khurshid U, et al.
    Front Pharmacol, 2021;12:708618.
    PMID: 34776946 DOI: 10.3389/fphar.2021.708618
    Sphaeranthus indicus L. is a medicinal herb having widespread traditional uses for treating common ailments. The present research work aims to explore the in-depth phytochemical composition and in vitro reactivity of six different polarity solvents (methanol, n-hexane, benzene, chloroform, ethyl acetate, and n-butanol) extracts/fractions of S. indicus flowers. The phytochemical composition was accomplished by determining total bioactive contents, HPLC-PDA polyphenolic quantification, and UHPLC-MS secondary metabolomics. The reactivity of the phenolic compounds was tested through the following biochemical assays: antioxidant (DPPH, ABTS, FRAP, CUPRAC, phosphomolybdenum, and metal chelation) and enzyme inhibition (AChE, BChE, α-glucosidase, α-amylase, urease, and tyrosinase) assays were performed. The methanol extract showed the highest values for phenolic (94.07 mg GAE/g extract) and flavonoid (78.7 mg QE/g extract) contents and was also the most active for α-glucosidase inhibition as well as radical scavenging and reducing power potential. HPLC-PDA analysis quantified rutin, naringenin, chlorogenic acid, 3-hydroxybenzoic acid, gallic acid, and epicatechin in a significant amount. UHPLC-MS analysis of methanol and ethyl acetate extracts revealed the presence of well-known phytocompounds; most of these were phenolic, flavonoid, and glycoside derivatives. The ethyl acetate fraction exhibited the highest inhibition against tyrosinase and urease, while the n-hexane fraction was most active for α-amylase. Moreover, principal component analysis highlighted the positive correlation between bioactive compounds and the tested extracts. Overall, S. indicus flower extracts were found to contain important phytochemicals, hence could be further explored to discover novel bioactive compounds that could be a valid starting point for future pharmaceutical and nutraceuticals applications.
  11. Statistical analysis plan for the replacing protein via enteral nutrition in a stepwise approach in critically ill patients (REPLENISH) randomized clinical trial
    Arabi YM, Al-Dorzi HM, Aldibaasi O, Sadat M, Jose J, Muharib D, et al.
    Trials, 2024 May 02;25(1):296.
    PMID: 38698442 DOI: 10.1186/s13063-024-08105-w
    BACKGROUND: The optimal amount and timing of protein intake in critically ill patients are unknown. REPLENISH (Replacing Protein via Enteral Nutrition in a Stepwise Approach in Critically Ill Patients) trial evaluates whether supplemental enteral protein added to standard enteral nutrition to achieve a high amount of enteral protein given from ICU day five until ICU discharge or ICU day 90 as compared to no supplemental enteral protein to achieve a moderate amount of enteral protein would reduce all-cause 90-day mortality in adult critically ill mechanically ventilated patients.

    METHODS: In this multicenter randomized trial, critically ill patients will be randomized to receive supplemental enteral protein (1.2 g/kg/day) added to standard enteral nutrition to achieve a high amount of enteral protein (range of 2-2.4 g/kg/day) or no supplemental enteral protein to achieve a moderate amount of enteral protein (0.8-1.2 g/kg/day). The primary outcome is 90-day all-cause mortality; other outcomes include functional and health-related quality-of-life assessments at 90 days. The study sample size of 2502 patients will have 80% power to detect a 5% absolute risk reduction in 90-day mortality from 30 to 25%. Consistent with international guidelines, this statistical analysis plan specifies the methods for evaluating primary and secondary outcomes and subgroups. Applying this statistical analysis plan to the REPLENISH trial will facilitate unbiased analyses of clinical data.

    CONCLUSION: Ethics approval was obtained from the institutional review board, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia (RC19/414/R). Approvals were also obtained from the institutional review boards of each participating institution. Our findings will be disseminated in an international peer-reviewed journal and presented at relevant conferences and meetings.

    TRIAL REGISTRATION: ClinicalTrials.gov, NCT04475666 . Registered on July 17, 2020.

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