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International Patients' Travel Decision Making Process- A Conceptual Framework

Khan MJ, Chelliah S, Haron MS

Iran J Public Health, 2016 Feb;45(2):134-45.
PMID: 27114978

BACKGROUND: Role of information source, perceived benefits and risks, and destination image has significantly been examined in travel and tourism literature; however, in medical tourism it is yet to be examined thoroughly. The concept discussed in this article is drawn form well established models in tourism literature.
METHODS: The purpose of this research was to identify the source of information, travel benefits and perceived risks related to movement of international patients and develop a conceptual model based on well-established theory. Thorough database search (Science Direct, utmj.org, nih.gov, nchu.edu.tw, palgrave-journals, medretreat, Biomedcentral) was performed to fulfill the objectives of the study.
RESULTS: International patients always concern about benefits and risks related to travel. These benefits and risks form images of destination in the minds of international patients. Different sources of information make international patients acquaint about the associated benefits and risks, which later leads to development of intention to visit. This conceptual paper helps in establishing model for decision-making process of international patients in developing visit intention.
CONCLUSION: Ample amount of literature is available detailing different factors involved in travel decision making of international patients; however literature explaining relationship between these factors is scarce.
Effect of copper oxide nanoparticles on the conformation and activity of β-galactosidase

Rabbani G, Khan MJ, Ahmad A, Maskat MY, Khan RH

Colloids Surf B Biointerfaces, 2014 Nov 1;123:96-105.
PMID: 25260221 DOI: 10.1016/j.colsurfb.2014.08.035

The primary objective of this study is to explore the interaction of β-galactosidase with copper oxide nanoparticles (CuO NPs). Steady-state absorption, fluorescence and circular dichroism (CD) spectroscopic techniques have been employed to unveil the conformational changes of β-galactosidase induced by the binding of CuO NPs. Temperature dependent fluorescence quenching results indicates a static quenching mechanism in the present case. The binding thermodynamic parameters delineate the predominant role of H-bonding and van der Waals forces between β-galactosidase and CuO NPs binding process. The binding was studied by isothermal titration calorimetry (ITC) and the result revealed that the complexation is enthalpy driven, the ΔH°<0, ΔS°<0 indicates the formation of hydrogen bonds between β-galactosidase and CuO NPs occurs. Disruption of the native conformation of the protein upon binding with CuO NPs is reflected through a reduced functionality (in terms of hydrolase activity) of the protein CuO NPs conjugate system in comparison to the native protein and CuO NPs exhibited a competitive mode of inhibition. This also supports the general belief that H-bond formation occurs with NPs is associated with a lesser extent of modification in the native structure. Morphological features and size distributions were investigated using transmission electron microscopy (TEM) and dynamic light scattering (DLS). Additionally the considerable increase in the Rh following the addition of CuO NPs accounts for the unfolding of β-galactosidase. Chemical and thermal unfolding of β-galactosidase, when carried out in the presence of CuO NPs, also indicated a small perturbation in the protein structure. These alterations in functional activity of nanoparticle bound β-galactosidase which will have important consequences should be taken into consideration while using nanoparticles for diagnostic and therapeutic purposes.
Immobilization of horseradish peroxidase on β-cyclodextrin-capped silver nanoparticles: Its future aspects in biosensor application

Karim Z, Khan MJ, Maskat MY, Adnan R

Prep Biochem Biotechnol, 2016 May 18;46(4):321-7.
PMID: 25830286 DOI: 10.1080/10826068.2015.1031389

This study aimed to work out a simple and high-yield procedure for the immobilization of horseradish peroxidase on silver nanoparticle. Ultraviolet-visible (UV-vis) and Fourier-transform infrared spectroscopy and transmission electron microscopy were used to characterize silver nanoparticles. Horseradish peroxidase was immobilized on β-cyclodextrin-capped silver nanoparticles via glutaraldehyde cross-linking. Single-cell gel electrophoresis (Comet assay) was also performed to confirm the genotoxicity of silver nanoparticles. To decrease toxicity, silver nanoparticles were capped with β-cyclodextrin. A comparative stability study of soluble and immobilized enzyme preparations was investigated against pH, temperature, and chaotropic agent, urea. The results showed that the cross-linked peroxidase was significantly more stable as compared to the soluble counterpart. The immobilized enzyme exhibited stable enzyme activities after repeated uses.
Fulltext Green Synthesis and Characterization of Pullulan Mediated Silver Nanoparticles through Ultraviolet Irradiation

Khan MJ, Kumari S, Shameli K, Selamat J, Sazili AQ

Materials (Basel), 2019 Jul 26;12(15).
PMID: 31357398 DOI: 10.3390/ma12152382

Nanoparticles (NPs) are, frequently, being utilized in multi-dimensional enterprises. Silver nanoparticles (AgNPs) have attracted researchers in the last decade due to their exceptional efficacy at very low volume and stability at higher temperatures. Due to certain limitations of the chemical method of synthesis, AgNPs can be obtained by physical methods including sun rays, microwaves and ultraviolet (UV) radiation. In the current study, the synthesis of pullulan mediated silver nanoparticles (P-AgNPs) was achieved through ultraviolet (UV) irradiation, with a wavelength of 365 nm, for 96 h. P-AgNPs were formed after 24 h of UV-irradiation time and expressed spectra maxima as 415 nm, after 96 h, in UV-vis spectroscopy. The crystallographic structure was "face centered cubic (fcc)" as confirmed by powder X-ray diffraction (PXRD). Furthermore, high resolution transmission electron microscopy (HRTEM) proved that P-AgNPs were covered with a thin layer of pullulan, with a mean crystalline size of 6.02 ± 2.37. The average lattice fringe spacing of nanoparticles was confirmed as 0.235 nm with quasi-spherical characteristics, by selected area electron diffraction (SAED) analysis. These green synthesized P-AgNPs can be utilized efficiently, as an active food and meat preservative, when incorporated into the edible films.
Fulltext Rapid Green Synthesis and Characterization of Silver Nanoparticles Arbitrated by Curcumin in an Alkaline Medium

Khan MJ, Shameli K, Sazili AQ, Selamat J, Kumari S

Molecules, 2019 Feb 16;24(4).
PMID: 30781541 DOI: 10.3390/molecules24040719

Green synthesis of silver nanoparticles is desirable practice. It is not only the required technique for industrial and biomedical purposes but also a promising research area. The aim of this study was to synthesize green curcumin silver nanoparticles (C-Ag NPs). The synthesis of C-Ag NPs was achieved by reduction of the silver nitrate (AgNO₃) in an alkaline medium. The characterizations of the prepared samples were conducted by ultraviolet visible (UV-vis) spectroscopy, powder X-ray diffraction (PXRD), field emission scanning electron microscopy (FESEM), high-resolution transmission electron microscopy (HRTEM), selected area electron diffraction (SAED) and zeta potential (ZP) analyses. The formation of C-Ag NPs was evaluated by the dark color of the colloidal solutions and UV-vis spectra, with 445 nm as the maximum. The size of the crystalline nanoparticles, recorded as 12.6 ± 3.8nm, was confirmed by HRTEM, while the face-centered cubic (fcc) crystallographic structure was confirmed by PXRD and SAED. It is assumed that green synthesized curcumin silver nanoparticles (C-Ag NPs) can be efficiently utilized as a strong antimicrobial substance for food and meat preservation due to their homogeneous nature and small size.
Fulltext AI inspired EEG-based spatial feature selection method using multivariate empirical mode decomposition for emotion classification

Asghar MA, Khan MJ, Rizwan M, Shorfuzzaman M, Mehmood RM

Multimed Syst, 2021 Apr 21.
PMID: 33897112 DOI: 10.1007/s00530-021-00782-w

Classification of human emotions based on electroencephalography (EEG) is a very popular topic nowadays in the provision of human health care and well-being. Fast and effective emotion recognition can play an important role in understanding a patient's emotions and in monitoring stress levels in real-time. Due to the noisy and non-linear nature of the EEG signal, it is still difficult to understand emotions and can generate large feature vectors. In this article, we have proposed an efficient spatial feature extraction and feature selection method with a short processing time. The raw EEG signal is first divided into a smaller set of eigenmode functions called (IMF) using the empirical model-based decomposition proposed in our work, known as intensive multivariate empirical mode decomposition (iMEMD). The Spatio-temporal analysis is performed with Complex Continuous Wavelet Transform (CCWT) to collect all the information in the time and frequency domains. The multiple model extraction method uses three deep neural networks (DNNs) to extract features and dissect them together to have a combined feature vector. To overcome the computational curse, we propose a method of differential entropy and mutual information, which further reduces feature size by selecting high-quality features and pooling the k-means results to produce less dimensional qualitative feature vectors. The system seems complex, but once the network is trained with this model, real-time application testing and validation with good classification performance is fast. The proposed method for selecting attributes for benchmarking is validated with two publicly available data sets, SEED, and DEAP. This method is less expensive to calculate than more modern sentiment recognition methods, provides real-time sentiment analysis, and offers good classification accuracy.
Fulltext Role of Travel Motivations, Perceived Risks and Travel Constraints on Destination Image and Visit Intention in Medical Tourism: Theoretical model

Khan MJ, Chelliah S, Haron MS, Ahmed S

Sultan Qaboos Univ Med J, 2017 Feb;17(1):e11-e17.
PMID: 28417022 DOI: 10.18295/squmj.2016.17.01.003

Travel motivations, perceived risks and travel constraints, along with the attributes and characteristics of medical tourism destinations, are important issues in medical tourism. Although the importance of these factors is already known, a comprehensive theoretical model of the decision-making process of medical tourists has yet to be established, analysing the intricate relationships between the different variables involved. This article examines a large body of literature on both medical and conventional tourism in order to propose a comprehensive theoretical framework of medical tourism decision-making. Many facets of this complex phenomenon require further empirical investigation.
Fulltext An Innovative Multi-Model Neural Network Approach for Feature Selection in Emotion Recognition Using Deep Feature Clustering

Asghar MA, Khan MJ, Rizwan M, Mehmood RM, Kim SH

Sensors (Basel), 2020 Jul 05;20(13).
PMID: 32635609 DOI: 10.3390/s20133765

Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to measure and track a user's emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods. The DFC method shortens the training time on the network by omitting unusable attributes. First, Empirical Mode Decomposition (EMD) is applied as a series of frequencies to decompose the raw EEG signal. The spatiotemporal component of the decomposed EEG signal is expressed as a two-dimensional spectrogram before the feature extraction process using Analytic Wavelet Transform (AWT). Four pre-trained Deep Neural Networks (DNN) are used to extract deep features. Dimensional reduction and feature selection are achieved utilising the differential entropy-based EEG channel selection and the DFC technique, which calculates a range of vocabularies using k-means clustering. The histogram characteristic is then determined from a series of visual vocabulary items. The classification performance of the SEED, DEAP and MAHNOB datasets combined with the capabilities of DFC show that the proposed method improves the performance of emotion recognition in short processing time and is more competitive than the latest emotion recognition methods.
Fulltext Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network

Khan MJ, Singh PP, Pradhan B, Alamri A, Lee CW

Sensors (Basel), 2023 Oct 28;23(21).
PMID: 37960482 DOI: 10.3390/s23218783

Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road networks, such as varying lengths, widths, materials, and geometries across different regions, pose a formidable obstacle for road extraction from RS imagery. The issue of road extraction can be defined as a task that involves capturing contextual and complex elements while also preserving boundary information and producing high-resolution road segmentation maps for RS data. The objective of the proposed Archimedes tuning process quantum dilated convolutional neural network for road Extraction (ATP-QDCNNRE) technology is to tackle the aforementioned issues by enhancing the efficacy of image segmentation outcomes that exploit remote sensing imagery, coupled with Archimedes optimization algorithm methods (AOA). The findings of this study demonstrate the enhanced road-extraction capabilities achieved by the ATP-QDCNNRE method when used with remote sensing imagery. The ATP-QDCNNRE method employs DL and a hyperparameter tuning process to generate high-resolution road segmentation maps. The basis of this approach lies in the QDCNN model, which incorporates quantum computing (QC) concepts and dilated convolutions to enhance the network's ability to capture both local and global contextual information. Dilated convolutions also enhance the receptive field while maintaining spatial resolution, allowing fine road features to be extracted. ATP-based hyperparameter modifications improve QDCNNRE road extraction. To evaluate the effectiveness of the ATP-QDCNNRE system, benchmark databases are used to assess its simulation results. The experimental results show that ATP-QDCNNRE performed with an intersection over union (IoU) of 75.28%, mean intersection over union (MIoU) of 95.19%, F1 of 90.85%, precision of 87.54%, and recall of 94.41% in the Massachusetts road dataset. These findings demonstrate the superior efficiency of this technique compared to more recent methods.
Fulltext Examining the psychosocial impacts of the COVID-19 pandemic: an international cross-sectional study protocol

Tanveer S, Schluter PJ, Porter RJ, Boden J, Beaglehole B, Sulaiman-Hill R, et al.

BMJ Open, 2023 Apr 12;13(4):e067886.
PMID: 37045574 DOI: 10.1136/bmjopen-2022-067886

INTRODUCTION: The COVID-19 pandemic exposed people to significant and prolonged stress. The psychosocial impacts of the pandemic have been well recognised and reported in high-income countries (HICs) but it is important to understand the unique challenges posed by COVID-19 in low- and middle-income countries (LMICs) where limited international comparisons have been undertaken. This protocol was therefore devised to study the psychosocial impacts of the COVID-19 pandemic in seven LMICs using scales that had been designed for or translated for this purpose.
METHODS AND ANALYSIS: This cross-sectional study uses an online survey to administer a novel COVID Psychosocial Impacts Scale (CPIS) alongside established measures of psychological distress, post-traumatic stress, well-being and post-traumatic growth in the appropriate language. Participants will include adults aged 18 years and above, recruited from Indonesia, Iraq, Iran, Malaysia, Pakistan, Somalia and Turkey, with a pragmatic target sample size of 500 in each country.Data will be analysed descriptively on sociodemographic and study variables. In addition, CPIS will be analysed psychometrically (for reliability and validity) to assess the suitability of use in a given context. Finally, within-subjects and between-subjects analyses will be carried out using multi-level mixed-effect models to examine associations between key sociodemographic and study variables.
ETHICS AND DISSEMINATION: Ethical approval was granted by the Human Ethics Committee, University of Otago, New Zealand (Ref. No. 21/102). In addition, international collaborators obtained local authorisation or ethical approval in their respective host universities before data collection commenced.Participants will give informed consent before taking part. Data will be collected and stored securely on the University of Otago, New Zealand Qualtrics platform using an auto-generated non-identifiable letter-number string. Data will be available on reasonable request. Findings will be disseminated by publications in scientific journals and/or conference presentations.
TRIAL REGISTRATION NUMBER: NCT05052333.

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