Displaying publications 1 - 20 of 34 in total

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  1. Report of a rare case of tracheal foreign-body in an adult
    Majeed AR
    Med J Malaysia, 1978 Dec;33(2):146-8.
    PMID: 755166
  2. Fulltext Burden of Diarrhea in the Eastern Mediterranean Region, 1990-2013: Findings from the Global Burden of Disease Study 2013
    Khalil I, Colombara DV, Forouzanfar MH, Troeger C, Daoud F, Moradi-Lakeh M, et al.
    Am J Trop Med Hyg, 2016 Dec 07;95(6):1319-1329.
    PMID: 27928080 DOI: 10.4269/ajtmh.16-0339
    Diarrheal diseases (DD) are leading causes of disease burden, death, and disability, especially in children in low-income settings. DD can also impact a child's potential livelihood through stunted physical growth, cognitive impairment, and other sequelae. As part of the Global Burden of Disease Study, we estimated DD burden, and the burden attributable to specific risk factors and particular etiologies, in the Eastern Mediterranean Region (EMR) between 1990 and 2013. For both sexes and all ages, we calculated disability-adjusted life years (DALYs), which are the sum of years of life lost and years lived with disability. We estimate that over 125,000 deaths (3.6% of total deaths) were due to DD in the EMR in 2013, with a greater burden of DD in low- and middle-income countries. Diarrhea deaths per 100,000 children under 5 years of age ranged from one (95% uncertainty interval [UI] = 0-1) in Bahrain and Oman to 471 (95% UI = 245-763) in Somalia. The pattern for diarrhea DALYs among those under 5 years of age closely followed that for diarrheal deaths. DALYs per 100,000 ranged from 739 (95% UI = 520-989) in Syria to 40,869 (95% UI = 21,540-65,823) in Somalia. Our results highlighted a highly inequitable burden of DD in EMR, mainly driven by the lack of access to proper resources such as water and sanitation. Our findings will guide preventive and treatment interventions which are based on evidence and which follow the ultimate goal of reducing the DD burden.
  3. Fulltext Frequency of Root Canal Treatment among Patients Attending a Teaching Dental Hospital in Dammam, Saudi Arabia
    Khan SQ, Khabeer A, Al Harbi F, Arrejaie AS, Moheet IA, Farooqi FA, et al.
    Saudi J Med Med Sci, 2017 04 20;5(2):145-148.
    PMID: 30787773 DOI: 10.4103/1658-631X.204860
    Objective: The purpose of the study was to evaluate the frequency and status of root canal-treated teeth in patients treated at the College of Dentistry, University of Dammam in the Eastern Province of Saudi Arabia.

    Materials and Methods: A total of 3701 patients visited the clinics during the study period. Through the use of radiographs, 161 patients were initially selected who had endodontically treated teeth. However, after applying the inclusion criteria, the total number of eligible cases was reduced to 155. Patients were divided into three groups according to age (children 1-12 years, adults 13-65 years and geriatrics >65 years).

    Results: On average, each patient had 2.28 ± 1.88 root canal-treated teeth and 24.02 ± 5.03 teeth without root canal treatment. The average number of endodontically treated teeth increased with an increase in age. The adult group showed the highest number of root-filled teeth 314 (93.4%). Of the 336 endodontically treated teeth, only 75 (22.3%) teeth exhibited periapical radiolucency. First molars (28.43-36.36%) and second premolars (20.1-27.27%) were the most frequently root-filled teeth in both jaws, followed by the first maxillary premolars (11.76%). Periapical lesions showed an almost similar pattern with the highest number of radiolucencies found in the first molars in both jaws (29.3-33.3%) followed by the second premolars in the mandible (30.6%) and first premolars in both jaws (20.8-25%).

    Conclusion: The first molars and second premolars were the most frequently root-filled teeth in both jaws, followed by maxillary first premolars. Periapical lesions showed an almost similar pattern among teeth with a higher number of radiolucencies found in the first molars in both jaws, followed by the second premolars in the mandible and the first premolars in both jaws.

  4. Fulltext Information technology for patient safety
    Huckvale C, Car J, Akiyama M, Jaafar S, Khoja T, Bin Khalid A, et al.
    Qual Saf Health Care, 2010 Aug;19 Suppl 2:i25-33.
    PMID: 20693213 DOI: 10.1136/qshc.2009.038497
    BACKGROUND: Research on patient care has identified substantial variations in the quality and safety of healthcare and the considerable risks of iatrogenic harm as significant issues. These failings contribute to the high rates of potentially avoidable morbidity and mortality and to the rising levels of healthcare expenditure seen in many health systems. There have been substantial developments in information technology in recent decades and there is now real potential to apply these technological developments to improve the provision of healthcare universally. Of particular international interest is the use of eHealth applications. There is, however, a large gap between the theoretical and empirically demonstrated benefits of eHealth applications. While these applications typically have the technical capability to help professionals in the delivery of healthcare, inadequate attention to the socio-technical dimensions of their use can result in new avoidable risks to patients.

    RESULTS AND DISCUSSION: Given the current lack of evidence on quality and safety improvements and on the cost-benefits associated with the introduction of eHealth applications, there should be a focus on implementing more mature technologies; it is also important that eHealth applications should be evaluated against a comprehensive and rigorous set of measures, ideally at all stages of their application life cycle.

  5. Fulltext Surface Reconstruction from Parallel Curves with Application to Parietal Bone Fracture Reconstruction
    Majeed A, Mt Piah AR, Ridzuan Yahya Z
    PLoS One, 2016;11(3):e0149921.
    PMID: 26967643 DOI: 10.1371/journal.pone.0149921
    Maxillofacial trauma are common, secondary to road traffic accident, sports injury, falls and require sophisticated radiological imaging to precisely diagnose. A direct surgical reconstruction is complex and require clinical expertise. Bio-modelling helps in reconstructing surface model from 2D contours. In this manuscript we have constructed the 3D surface using 2D Computerized Tomography (CT) scan contours. The fracture part of the cranial vault are reconstructed using GC1 rational cubic Ball curve with three free parameters, later the 2D contours are flipped into 3D with equidistant z component. The constructed surface is represented by contours blending interpolant. At the end of this manuscript a case report of parietal bone fracture is also illustrated by employing this method with a Graphical User Interface (GUI) illustration.
  6. Fulltext Craniofacial reconstruction using rational cubic ball curves
    Majeed A, Mt Piah AR, Gobithaasan RU, Yahya ZR
    PLoS One, 2015;10(4):e0122854.
    PMID: 25880632 DOI: 10.1371/journal.pone.0122854
    This paper proposes the reconstruction of craniofacial fracture using rational cubic Ball curve. The idea of choosing Ball curve is based on its robustness of computing efficiency over Bezier curve. The main steps are conversion of Digital Imaging and Communications in Medicine (Dicom) images to binary images, boundary extraction and corner point detection, Ball curve fitting with genetic algorithm and final solution conversion to Dicom format. The last section illustrates a real case of craniofacial reconstruction using the proposed method which clearly indicates the applicability of this method. A Graphical User Interface (GUI) has also been developed for practical application.
  7. Fulltext A machine learning approach of predicting high potential archers by means of physical fitness indicators
    Muazu Musa R, P P Abdul Majeed A, Taha Z, Chang SW, Ab Nasir AF, Abdullah MR
    PLoS One, 2019;14(1):e0209638.
    PMID: 30605456 DOI: 10.1371/journal.pone.0209638
    k-nearest neighbour (k-NN) has been shown to be an effective learning algorithm for classification and prediction. However, the application of k-NN for prediction and classification in specific sport is still in its infancy. The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. Standard fitness measurements of the handgrip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were conducted. Multiple linear regression was utilised to ascertain the significant variables that affect the shooting score. It was demonstrated from the analysis that core muscle strength and vertical jump were statistically significant. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the significant variables identified. k-NN model variations, i.e., fine, medium, coarse, cosine, cubic and weighted functions as well as logistic regression, were trained based on the significant performance variables. The HACA clustered the archers into high potential archers (HPA) and low potential archers (LPA). The weighted k-NN outperformed all the tested models at itdemonstrated reasonably good classification on the evaluated indicators with an accuracy of 82.5 ± 4.75% for the prediction of the HPA and the LPA. Moreover, the performance of the classifiers was further investigated against fresh data, which also indicates the efficacy of the weighted k-NN model. These findings could be valuable to coaches and sports managers to recognise high potential archers from a combination of the selected few physical fitness performance indicators identified which would subsequently save cost, time and energy for a talent identification programme.
  8. Fulltext Technical and tactical performance indicators discriminating winning and losing team in elite Asian beach soccer tournament
    Muazu Musa R, P P Abdul Majeed A, Abdullah MR, Ab Nasir AF, Arif Hassan MH, Mohd Razman MA
    PLoS One, 2019;14(6):e0219138.
    PMID: 31247012 DOI: 10.1371/journal.pone.0219138
    The present study aims to identify the essential technical and tactical performance indicators that could differentiate winning and losing performance in the Asian elite beach soccer competition. A set of 20 technical and tactical performance indicators namely; shot back-third, shot mid-third, shot front-third, pass back-third, pass mid-third, pass front-third, shot in box, shot outbox, chances created, interception, turnover, goals scored 1st period, goals scored 2nd period, goals scored 3rd period, goals scored extra time, tackling, fouls committed, complete save, incomplete save and passing error were observed during the beach soccer Asian Football Confederation tournament 2017 held in Malaysia. A total of 23 matches from 12 teams were notated using StatWatch application in real-time. Discriminant analysis (DA) of standard, backward as well stepwise modes were used to develop a model for the winning (WT) and losing team (LT) whilst Mann-Whitney U test was utilized to ascertain the differences between the WT and LT with respect to the performance indicators evaluated. The standard backward, forward and stepwise discriminates the WT and the LT with an excellent accuracy of 95.65%, 91.30% and 89.13%, respectively. The standard DA model discriminated the teams from seven performance indicators whilst both the backward and forward stepwise identified two performance indicators. The Mann-Whitney U test analysis indicated that the WT is statistically significant from the LT based on the performance indicators determined from the standard mode model of the DA. It was demonstrated that seven performance indicators namely; shot front-third, pass front-third, chances created, goals scores at the 1st period, goals scored at the 2nd period, goals scored at 3rd period were directly linked to a successful performance whilst the incomplete save by the keeper attribute towards the poor performance of the team. The present finding could serve useful to the coaches as well as performance analysts as a measure of profiling successful performance and enables team improvement with respect to the associated performance indicators.
  9. Fulltext Assessment of risk factors associated with potential drug-drug interactions among patients suffering from chronic disorders
    Rasool MF, Rehman AU, Khan I, Latif M, Ahmad I, Shakeel S, et al.
    PLoS One, 2023;18(1):e0276277.
    PMID: 36693042 DOI: 10.1371/journal.pone.0276277
    Patients suffering from chronic diseases are more likely to experience pDDIs due to older age, prolonged treatment, severe illness and greater number of prescribed drugs. The objective of the current study was to assess the prevalence of pDDIs and risk factors associated with occurrence of pDDIs in chronic disease patients attending outpatient clinics for regular check-ups. Patients suffering from diabetes, chronic obstructive pulmonary disease (COPD), stroke and osteoporosis were included in the study. This study was a cross sectional, observational, prospective study that included 337 patients from outpatient clinics of respiratory ward, cardiac ward and orthopedic ward of Nishter Hospital Multan, Pakistan. The mean number of interactions per patient was 1.68. A greater risk for occurrence of pDDI was associated with older age ≥ 60 years (OR = 1.95, 95% CI = 1.44-2.37, p<0.001); polypharmacy (≥ 5 drugs) (OR = 3.74, 95% CI 2.32-4.54, p<0.001); overburden (OR = 2.23, 95% CI = 1.64-3.16, p<0.01); CCI score (OR = 1.28, 95% CI = 1.04-1.84, p<0.001); multiple prescribers to one patient (OR = 1.18, 95% CI = 1.06-1.41, p<0.01); and trainee practitioner (OR = 1.09, 95% CI = 1.01-1.28, p<0.01). Old age, polypharmacy, overburden healthcare system, higher comorbidity index, multiple prescribers to one patient and trainee practitioner were associated with increased risk of occurrence of pDDIs in chronic disease patients.
  10. Fulltext The classification of movement intention through machine learning models: the identification of significant time-domain EMG features
    Mohd Khairuddin I, Sidek SN, P P Abdul Majeed A, Mohd Razman MA, Ahmad Puzi A, Md Yusof H
    PeerJ Comput Sci, 2021;7:e379.
    PMID: 33817026 DOI: 10.7717/peerj-cs.379
    Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject's intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects' biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of features were extracted from the filtered signals namely waveform length (WL), mean absolute value (MAV), root mean square (RMS), standard deviation (SD), minimum (MIN) and maximum (MAX). Several different classifiers viz. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) were investigated on its efficacy to accurately classify the pre-intention and intention classes based on the significant features identified (MIN and MAX) via Extremely Randomised Tree feature selection technique. It was observed from the present investigation that the DT classifier yielded an excellent classification with a classification accuracy of 100%, 99% and 99% on training, testing and validation dataset, respectively based on the identified features. The findings of the present investigation are non-trivial towards facilitating the rehabilitation phase of patients based on their actual capability and hence, would eventually yield a more active participation from them.
  11. Fulltext The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN
    Rashid M, Bari BS, Hasan MJ, Razman MAM, Musa RM, Ab Nasir AF, et al.
    PeerJ Comput Sci, 2021;7:e374.
    PMID: 33817022 DOI: 10.7717/peerj-cs.374
    Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier's performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k-nearest neighbour (k-NN) has been proposed to classify the motor imagery (MI) data. The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. In order to evaluate the efficacy of the proposed method, an experimental study has been implemented using four publicly available MI dataset (BCI Competition III dataset 1 (data-1), dataset IIIA (data-2), dataset IVA (data-3) and BCI Competition IV dataset II (data-4)). It was shown that the ensemble-based random subspace k-NN approach achieved the superior classification accuracy (CA) of 99.21%, 93.19%, 93.57% and 90.32% for data-1, data-2, data-3 and data-4, respectively against other models evaluated, namely linear discriminant analysis, support vector machine, random forest, Naïve Bayes and the conventional k-NN. In comparison with other classification approaches reported in the recent studies, the proposed method enhanced the accuracy by 2.09% for data-1, 1.29% for data-2, 4.95% for data-3 and 5.71% for data-4, respectively. Moreover, it is worth highlighting that the RF feature selection technique employed in the present study was able to significantly reduce the feature dimension without compromising the overall CA. The outcome from the present study implies that the proposed method may significantly enhance the accuracy of MI data classification.
  12. Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline
    Islam MN, Sulaiman N, Farid FA, Uddin J, Alyami SA, Rashid M, et al.
    PeerJ Comput Sci, 2021;7:e638.
    PMID: 34712786 DOI: 10.7717/peerj-cs.638
    Hearing deficiency is the world's most common sensation of impairment and impedes human communication and learning. Early and precise hearing diagnosis using electroencephalogram (EEG) is referred to as the optimum strategy to deal with this issue. Among a wide range of EEG control signals, the most relevant modality for hearing loss diagnosis is auditory evoked potential (AEP) which is produced in the brain's cortex area through an auditory stimulus. This study aims to develop a robust intelligent auditory sensation system utilizing a pre-train deep learning framework by analyzing and evaluating the functional reliability of the hearing based on the AEP response. First, the raw AEP data is transformed into time-frequency images through the wavelet transformation. Then, lower-level functionality is eliminated using a pre-trained network. Here, an improved-VGG16 architecture has been designed based on removing some convolutional layers and adding new layers in the fully connected block. Subsequently, the higher levels of the neural network architecture are fine-tuned using the labelled time-frequency images. Finally, the proposed method's performance has been validated by a reputed publicly available AEP dataset, recorded from sixteen subjects when they have heard specific auditory stimuli in the left or right ear. The proposed method outperforms the state-of-art studies by improving the classification accuracy to 96.87% (from 57.375%), which indicates that the proposed improved-VGG16 architecture can significantly deal with AEP response in early hearing loss diagnosis.
  13. A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework
    Bari BS, Islam MN, Rashid M, Hasan MJ, Razman MAM, Musa RM, et al.
    PeerJ Comput Sci, 2021;7:e432.
    PMID: 33954231 DOI: 10.7717/peerj-cs.432
    The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms' edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.
  14. Fulltext The classification of EEG-based winking signals: a transfer learning and random forest pipeline
    Mahendra Kumar JL, Rashid M, Muazu Musa R, Mohd Razman MA, Sulaiman N, Jailani R, et al.
    PeerJ, 2021;9:e11182.
    PMID: 33850667 DOI: 10.7717/peerj.11182
    Brain Computer-Interface (BCI) technology plays a considerable role in the control of rehabilitation or peripheral devices for stroke patients. This is particularly due to their inability to control such devices from their inherent physical limitations after such an attack. More often than not, the control of such devices exploits electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features and the classification of the signals is non-trivial for a successful BCI system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards BCI applications, particularly in regard to EEG signals, are somewhat limited. The present study aims to evaluate the effectiveness of different TL models in extracting features for the classification of wink-based EEG signals. The extracted features are classified by means of fine-tuned Random Forest (RF) classifier. The raw EEG signals are transformed into a scalogram image via Continuous Wavelet Transform (CWT) before it was fed into the TL models, namely InceptionV3, Inception ResNetV2, Xception and MobileNet. The dataset was divided into training, validation, and test datasets, respectively, via a stratified ratio of 60:20:20. The hyperparameters of the RF models were optimised through the grid search approach, in which the five-fold cross-validation technique was adopted. The optimised RF classifier performance was compared with the conventional TL-based CNN classifier performance. It was demonstrated from the study that the best TL model identified is the Inception ResNetV2 along with an optimised RF pipeline, as it was able to yield a classification accuracy of 100% on both the training and validation dataset. Therefore, it could be established from the study that a comparable classification efficacy is attainable via the Inception ResNetV2 with an optimised RF pipeline. It is envisaged that the implementation of the proposed architecture to a BCI system would potentially facilitate post-stroke patients to lead a better life quality.
  15. Fulltext Mapping 123 million neonatal, infant and child deaths between 2000 and 2017
    Burstein R, Henry NJ, Collison ML, Marczak LB, Sligar A, Watson S, et al.
    Nature, 2019 Oct;574(7778):353-358.
    PMID: 31619795 DOI: 10.1038/s41586-019-1545-0
    Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2-to end preventable child deaths by 2030-we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000-2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations.
  16. A prospective study on the pattern of medication use for schizophrenia in the outpatient pharmacy department, Hospital Tengku Ampuan Rahimah, Selangor, Malaysia
    Ponto T, Ismail NI, Abdul Majeed AB, Marmaya NH, Zakaria ZA
    Methods Find Exp Clin Pharmacol, 2010 Jul-Aug;32(6):427-32.
    PMID: 20852752 DOI: 10.1358/mf.2010.32.6.1477907
    Schizophrenia is a chronic psychiatric disorder and pharmacotherapy plays a major role in its management. The 1950s and early 1960s saw milestones in the introduction of psychotropic drugs in clinical practice. A review of drug prescriptions in different settings provides an insight into the pattern of drug use, identifies drug-related problems and may be used to compare recommended guidelines with actual practice. This effort led to the evaluation of the drug prescribing pattern of antipsychotics in patients attending the psychiatric clinic at a government hospital. The data from 371 antipsychotic medication prescriptions that included 200 prescriptions for schizophrenia were collected during one month (1rst-31rst August 2008) at the outpatient pharmacy department. The mean age of patients was 35.0 years (SD = 1.131), with a male to female ratio of 2:1. The most widely used oral antipsychotic was haloperidol (16.3%) while the most common depot preparation prescribed was zuclopenthixol decanoate (8.8%). The daily dose of the average antipsychotic prescribed in this clinic was 342.06 mg equivalent of chlorpromazine. There was no relation between the doses received and ethnicity of the patient (Malay, Chinese or Indian). However, there was a significant relationship between the prescribed dose and patient age (P < 0.042). Nearly 32% of the schizophrenia patients were prescribed with atypical antipsychotics such as olanzapine (10.8%), risperidone (10.0%), quetiapine (7.6%) and clozapine (3.2%). Monotherapy was given to 73.0% of the schizophrenia patients. The majority of patients also received antidepressants. To conclude, this study gave evidence that physicians had a strong preference for monotherapy with conventional antipsychotic drugs while the use of atypical drugs was less prevalent.
  17. Antimicrobial and in vitro wound healing properties of novel clay based bionanocomposite films
    Mishra RK, Ramasamy K, Lim SM, Ismail MF, Majeed AB
    J Mater Sci Mater Med, 2014 Aug;25(8):1925-39.
    PMID: 24831081 DOI: 10.1007/s10856-014-5228-y
    The present study investigates the development of methyl cellulose (MC)-sodium alginate (SA)-montmorillonite (MMT) clay based bionanocomposite films with interesting wound healing properties. The differential scanning calorimetry analysis of the composite films revealed presence of single glass transition temperature (Tg) confirming the miscible nature of the ternary blended films. The increase in MMT ratio in the composite films reduced the mobility of biopolymer chains (MC/SA) which increased the Tg of the film. Thermogravimetric analysis showed that dispersion of clay (MMT) at nano level significantly delayed the weight loss that correlated with higher thermal stability of the composite films. It was observed that the developed films were able to exhibit antimicrobial activity against four typical pathogenic bacteria found in the presence of wound. The developed films were able to significantly inhibit (10 mg/ml) the growth of Enterococcus faecium and Pseudomonas aeruginosa. In vitro scratch assay indicated potential wound closure activities of MC-2-4 bionanocomposite films at their respective highest subtoxic doses. In conclusion, these ternary bionanocomposite films were found to be promising systems for wound healing applications.
  18. pH dependent poly[2-(methacryloyloxyethyl)trimetylammonium chloride-co-methacrylic acid]hydrogels for enhanced targeted delivery of 5-fluorouracil in colon cancer cells
    Mishra RK, Ramasamy K, Ahmad NA, Eshak Z, Majeed AB
    J Mater Sci Mater Med, 2014 Apr;25(4):999-1012.
    PMID: 24398912 DOI: 10.1007/s10856-013-5132-x
    Stimuli responsive hydrogels have shown enormous potential as a carrier for targeted drug delivery. In this study we have developed novel pH responsive hydrogels for the delivery of 5-fluorouracil (5-FU) in order to alleviate its antitumor activity while reducing its toxicity. We used 2-(methacryloyloxyethyl) trimetylammonium chloride a positively charged monomer and methacrylic acid for fabricating the pH responsive hydrogels. The released 5-FU from all except hydrogel (GEL-5) remained biologically active against human colon cancer cell lines [HT29 (IC50 = 110-190 μg ml(-1)) and HCT116 (IC50 = 210-390 μg ml(-1))] but not human skin fibroblast cells [BJ (CRL2522); IC50 ≥ 1000 μg ml(-1)]. This implies that the copolymer hydrogels (1-4) were able to release 5-FU effectively to colon cancer cells but not normal human skin fibroblast cells. This is probably due to the shorter doubling time that results in reduced pH in colon cancer cells when compared to fibroblast cells. These pH sensitive hydrogels showed well defined cell apoptosis in HCT116 cells through series of events such as chromatin condensation, membrane blebbing, and formation of apoptotic bodies. No cell killing was observed in the case of blank hydrogels. The results showed the potential of these stimuli responsive polymer hydrogels as a carrier for colon cancer delivery.
  19. Fulltext Conductometric and volumetric studies of atorvastatin in aqueous solution of arginine from 298.15 to 313.15 K
    Affandi MM, Tripathy M, Majeed AB
    J Adv Pharm Technol Res, 2016 Jul-Sep;7(3):80-6.
    PMID: 27429926 DOI: 10.4103/2231-4040.184589
    Categorized as a Biopharmaceutics Classification System Class II drugs, atorvastatin (ATV) exhibits low aqueous solubility and bioavailability thus presenting an obstacle and great challenge to formulation researchers. Numerous studies are available in regard to the solubility enhancement of ATV, but very few actually describe this phenomenon in terms of thermodynamics and the solute-solvent interaction. Arginine (ARG) is an amino acid that has been reported to enhance the solubility of the highly insoluble wheat protein gluten through hydrogen bonding and π electron-cation interaction. To our knowledge, ARG has never been investigated as a solubility enhancement agent of aqueous insoluble drugs. Thus, this study aimed to elucidate the solute-solvent and solute-cosolute interactions and derive thermodynamic parameters that bolstered the solubility of ATV in the presence of ARG. We examined the electrolytic conductance and densities of ATV-ARG binary system covering the temperature ranging from 298.15 K to 313.15 K. Conductometric and volumetric parameters such as limiting molar conductance, association constants, limiting partial molar volumes, and expansibility values were calculated. Additionally, thermodynamic parameters (ΔG(0), ΔH(0), ΔS(0), and Es) involved in the association process of the solute in the aqueous solution of ARG were also determined.
  20. Fulltext Improving medication safety in UK care homes: challenges and current perspective
    Fahrni ML, Franklin BD, Rawaf S, Majeed A
    JRSM Open, 2014 Feb;5(2):2042533313515475.
    PMID: 25057369 DOI: 10.1177/2042533313515475
    In the UK, there are policy and regulatory concerns regarding the governance of care homes and healthcare provision within these homes. From a public health perspective, these issues can pose significant challenges to the provision of safe and quality medication use services to care home residents. The objective of this paper is to highlight an important and neglected issue for the growing population of institutionalized older adults. We reviewed relevant literature for the years 2000 to present and identified recent efforts undertaken to improve medication safety standards in UK care homes. We consider the limitations and reasons for the National Health Service's restricted role and lack of leadership in providing medical services for this institutionalized population. The efforts taken by the Department of Health and other healthcare authorities targeting medication safety in care homes are also highlighted. In order to improve the quality of healthcare, specifically in areas related to medication safety and quality use of medicines, interventions need to be taken by the national government and similarly by local authorities and NHS commissioners.
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