Coronary artery disease is one of the most rampant non-communicable diseases in the world. It begins indolently as a fatty streak in the lining of the artery that soon progresses to narrow the coronary arteries and impair myocardial perfusion. Often the atherosclerotic plaque ruptures and causes sudden thrombotic occlusion and acute ST-elevation myocardial infarction (STEMI), non-ST-elevation MI (NSTEMI) or unstable angina (UA). This phenomenon is called acute coronary syndrome (ACS) and is the leading cause of death not only in Malaysia but also globally. In order for us to tackle this threat to the health of our nation we must arm ourselves with reliable and accurate information to assess current burden of disease resources available and success of current strategies. The acute coronary syndrome (ACS) registry is the flagship of the National Cardiovascular Disease Database (NCVD) and is the result of the dedicated and untiring efforts of doctors and nurses in both public and private medical institutions and hospitals around the country, ably guided and supported by the National Heart Association, the National Heart Foundation, the Clinical Research Centre and the Ministry of Health of Malaysia. Analyses of data collected throughout 2006 from 3422 patients with ACS admitted to the 12 tertiary cardiac centres and general hospitals spanning nine states in Malaysia in this first report has already revealed surprising results. Mean age of patients was 59 years while the most consistent risk factor for STEMI was active smoking. Utilization of medications was high generally. Thirty-day mortality for STEMI was 11%, for NSTEMI 8% and UA 4%. Thrombolysis (for STEMI only) reduced in-hospital and 30-day mortality by nearly 50%. Percutaneous coronary intervention or PCI also reduced 30-day mortality for patients with non-ST elevation MI and unstable angina. The strongest determinants of mortality appears to be Killip Class and age of the patient. Fewer women received thrombolysis or underwent PCI on same admission although women make up 25% of the cohort.
Colorectal cancer is emerging as one of the commonest cancers in Malaysia. Data on colorectal cancer from the National Cancer Registry is very limited. Comprehensive information on all aspects of colorectal cancer, including demographic details, pathology and treatment outcome are needed as the management of colorectal cancer has evolved rapidly over the years involving several disciplines including gastroenterology, surgery, radiology, pathology and oncology. This registry will be an important source of information that can help the development of guidelines to improve colorectal cancer care relevant to this country. The database will initially recruit all colorectal cancer cases from eight hospitals. The data will be stored on a customized web-based case report form. The database has begun collecting data from 1 October 2007 and will report on its first year findings at the end of 2008.
Matched MeSH terms: Databases, Factual/standards; Databases, Factual/statistics & numerical data
Cancer burden in Malaysia is increasing. Although there have been improvements in cancer treatment, these new therapies may potentially cause an exponential increase in the cost of cancer treatment. Therefore, justification for the use of these treatments is mandated. Availability of local data will enable us to evaluate and compare the outcome of our patients. This will help to support our clinical decision making and local policy, improve access to treatment and improve the provision and delivery of oncology services in Malaysia. The National Cancer Patient Registry was proposed as a database for cancer patients who seek treatment in Malaysia. It will be a valuable tool to provide timely and robust data on the actual setting in oncology practice, safety and cost effectiveness of treatment and most importantly the outcome of these patients.
Matched MeSH terms: Databases, Factual/statistics & numerical data
Software technology enables computerized analysis to offer second opinion in various screening and diagnostic tasks to assist the clinicians. Yet, the performance of these computerized methods for medical images is questioned by experts in CAD research, owing to the use of different databases and criteria for evaluating the computer results for comparison. This paper intends to substantiate this statement by illustrating the effects of such issues with the use of 1D physiologic data and multiple databases. For this purpose, the detection of desaturation events in Sp02 and spike events in EEG are used. This is the first time that comparison between different algorithms on a common basis is carried out on an individual effort. The appraisal for all the algorithms is made on the same databases and criteria. It is surprising to find that issues for 2/3D images concur with those found in 1D data here. In evaluating the accuracy of a new algorithm, a single independent database gives results fast. This paper reveals weaknesses of such an approach. It is hoped that the supportive evidence shown here is enough for researchers to innovate a better platform for credibility in reporting performance comparison of computerized analysis algorithms.
BACKGROUND: Adverse drug reactions are most commonly cutaneous in nature. Patterns of cutaneous adverse drug reactions (ADRs) and their causative drugs vary among the different populations previously studied.
OBJECTIVE: Our aim is to determine the clinical pattern of drug eruptions and the common drugs implicated, particularly in severe cutaneous ADRs in our population.
MATERIALS AND METHODS: This study was done by analyzing the database established for all adverse cutaneous drug reactions seen from January 2001 until December 2008.
RESULTS: A total of 281 cutaneous ADRs were seen in 280 patients. The most common reaction pattern was maculopapular eruption (111 cases, 39.5%) followed by Stevens-Johnson Syndrome (SJS: 79 cases, 28.1%), drug reaction with eosinophilia and systemic symptoms (DRESS: 19 cases, 6.8%), toxic epidermal necrolysis (TEN: 16 cases, 5.7 %), urticaria/angioedema (15 cases, 5.3%) and fixed drug eruptions (15 cases, 5.3%). Antibiotics (38.8%) and anticonvulsants (23.8%) accounted for 62.6% of the 281 cutaneous ADRs seen. Allopurinol was implicated in 39 (13.9%), carbamazepine in 29 (10.3%), phenytoin in 27 (9.6%) and cotrimoxazole in 26 (9.3%) cases. Carbamazepine, allopurinol and cotrimoxazole were the three main causative drugs of SJS/TEN accounting for 24.0%, 18.8% and 12.5% respectively of the 96 cases seen whereas DRESS was mainly caused by allopurinol (10 cases, 52.6%) and phenytoin (3 cases, 15.8%).
DISCUSSION: The reaction patterns and drugs causing cutaneous ADRs in our population are similar to those seen in other countries although we have a much higher proportion of severe cutaneous ADRs probably due to referral bias, different prescribing habit and a higher prevalence of HLA-B*1502 and HLA-B*5801 which are genetic markers for carbamazepine-induced SJS/TEN and allopurinol-induced SJS/TEN/DRESS respectively.
CONCLUSION: The most common reaction pattern seen in our study population was maculopapular eruptions. Antibiotics, anticonvulsants and NSAIDs were the most frequently implicated drug groups. Carbamazepine and allopurinol were the two main causative drugs of severe ADRs in our population.
Matched MeSH terms: Databases, Factual/statistics & numerical data
This paper discusses the weighting of two-dimensional fingerprints for similarity-based virtual screening, specifically the use of weights that assign greatest importance to the substructural fragments that occur least frequently in the database that is being screened. Virtual screening experiments using the MDL Drug Data Report and World of Molecular Bioactivity databases show that the use of such inverse frequency weighting schemes can result, in some circumstances, in marked increases in screening effectiveness when compared with the use of conventional, unweighted fingerprints. Analysis of the characteristics of the various schemes demonstrates that such weights are best used to weight the fingerprint of the reference structure in a similarity search, with the database structures' fingerprints unweighted. However, the increases in performance resulting from such weights are only observed with structurally homogeneous sets of active molecules; when the actives are diverse, the best results are obtained using conventional, unweighted fingerprints for both the reference structure and the database structures.
Many well-known methods are available for estimating the number of species in a forest community. However, most existing methods result in considerable negative bias in applications, where field surveys typically represent only a small fraction of sampled communities. This article develops a new method based on sampling with replacement to estimate species richness via the generalized jackknife procedure. The proposed estimator yields small bias and reasonably accurate interval estimation even with small samples. The performance of the proposed estimator is compared with several typical estimators via simulation study using two complete census datasets from Panama and Malaysia.
In this paper, application of Artificial Neural Network (ANN) for electrocardiogram (ECG) signal noise removal has been investigated. First, 100 number of ECG signals are selected from Physikalisch-Technische Bundesanstalt (PTB) database and Kalman filter is applied to remove their low pass noise. Then a suitable dataset based on denoised ECG signal is configured and used to a Multilayer Perceptron (MLP) neural network to be trained. Finally, results and experiences are discussed and the effect of changing different parameters for MLP training is shown.
The 2010 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation formed in 1998, was organized as the 9th International Conference on Bioinformatics (InCoB), Sept. 26-28, 2010 in Tokyo, Japan. Initially, APBioNet created InCoB as forum to foster bioinformatics in the Asia Pacific region. Given the growing importance of interdisciplinary research, InCoB2010 included topics targeting scientists in the fields of genomic medicine, immunology and chemoinformatics, supporting translational research. Peer-reviewed manuscripts that were accepted for publication in this supplement, represent key areas of research interests that have emerged in our region. We also highlight some of the current challenges bioinformatics is facing in the Asia Pacific region and conclude our report with the announcement of APBioNet's 100 BioDatabases (BioDB100) initiative. BioDB100 will comply with the database criteria set out earlier in our proposal for Minimum Information about a Bioinformatics and Investigation (MIABi), setting the standards for biocuration and bioinformatics research, on which we will report at the next InCoB, Nov. 27 - Dec. 2, 2011 at Kuala Lumpur, Malaysia.
Many of the conventional similarity methods assume that molecular fragments that do not relate to biological activity carry the same weight as the important ones. One possible approach to this problem is to use the Bayesian inference network (BIN), which models molecules and reference structures as probabilistic inference networks. The relationships between molecules and reference structures in the Bayesian network are encoded using a set of conditional probability distributions, which can be estimated by the fragment weighting function, a function of the frequencies of the fragments in the molecule or the reference structure as well as throughout the collection. The weighting function combines one or more fragment weighting schemes. In this paper, we have investigated five different weighting functions and present a new fragment weighting scheme. Later on, these functions were modified to combine the new weighting scheme. Simulated virtual screening experiments with the MDL Drug Data Report (23) and maximum unbiased validation data sets show that the use of new weighting scheme can provide significantly more effective screening when compared with the use of current weighting schemes.
The increasing number of diabetic retinopathy (DR) cases world wide demands the development of an automated decision support system for quick and cost-effective screening of DR. We present an automatic screening system for detecting the early stage of DR, which is known as non-proliferative diabetic retinopathy (NPDR). The proposed system involves processing of fundus images for extraction of abnormal signs, such as hard exudates, cotton wool spots, and large plaque of hard exudates. A rule based classifier is used for classifying the DR into two classes, namely, normal and abnormal. The abnormal NPDR is further classified into three levels, namely, mild, moderate, and severe. To evaluate the performance of the proposed decision support framework, the algorithms have been tested on the images of STARE database. The results obtained from this study show that the proposed system can detect the bright lesions with an average accuracy of about 97%. The study further shows promising results in classifying the bright lesions correctly according to NPDR severity levels.
The classification of the cancer tumors based on gene expression profiles has been extensively studied in numbers of studies. A wide variety of cancer datasets have been implemented by the various methods of gene selection and classification to identify the behavior of the genes in tumors and find the relationships between them and outcome of diseases. Interpretability of the model, which is developed by fuzzy rules and linguistic variables in this study, has been rarely considered. In addition, creating a fuzzy classifier with high performance in classification that uses a subset of significant genes which have been selected by different types of gene selection methods is another goal of this study. A new algorithm has been developed to identify the fuzzy rules and significant genes based on fuzzy association rule mining. At first, different subset of genes which have been selected by different methods, were used to generate primary fuzzy classifiers separately and then proposed algorithm was implemented to mix the genes which have been associated in the primary classifiers and generate a new classifier. The results show that fuzzy classifier can classify the tumors with high performance while presenting the relationships between the genes by linguistic variables.
This paper presents the development of kidney TeleUltrasound consultation system. The TeleUltrasound system provides an innovative design that aids the acquisition, archiving, and dissemination of medical data and information over the internet as its backbone. The system provides data sharing to allow remote collaboration, viewing, consultation, and diagnosis of medical data. The design is layered upon a standard known as Digital Imaging and Communication in Medicine (DICOM). The DICOM standard defines protocols for exchanging medical images and their associated data. The TeleUltrasound system is an integrated solution for retrieving, processing, and archiving images and providing data storage management using Structured Query Language (SQL) database. Creating a web-based interface is an additional advantage to achieve global accessibility of experts that will widely open the opportunity of greater examination and multiple consultations. This system is equipped with a high level of data security and its performance has been tested with white, black, and gray box techniques. And the result was satisfactory. The overall system has been evaluated by several radiologists in Malaysia, United Arab Emirates, and Sudan, the result is shown within this paper.
Health-Related Quality of Life (HRQOL) in pediatric leukemia patients in Malaysia has not been studied before. This was mainly due to a lack of databases on patients in the past. Many patients abandoned treatment or were lost to follow up. With more children now fully compliant and completing treatment nowadays, with higher cure rate, HRQOL has become important for our patients. The purpose of the current study was to determine the HRQOL scores in children with acute leukemia and to compare the scores for those on maintenance chemotherapy with those off-treatment as well as to determine factors which might affect HRQOL.
BACKGROUND: The prescription of contraindicated drugs is a preventable medication error, which can cause morbidity and mortality. Recent data on the factors associated with drug contraindications (DCIs) is limited world-wide, especially in Malaysia.
AIMS: The objectives of this study are 1) to quantify the prevalence of DCIs in a primary care setting at a Malaysian University; 2) to identify patient characteristics associated with increased DCI episodes, and 3) to identify associated factors for these DCIs.
METHODS: We retrospectively collected data from 1 academic year using computerized databases at the Universiti Sains Malaysia (USM) from patients of USM's primary care. Descriptive and comparative statistics were used to characterize DCIs.
RESULTS: There were 1,317 DCIs during the study period. These were observed in a cohort of 923 patients, out of a total of 17,288 patients, representing 5,339 DCIs per 100,000 patients, or 5.3% of all patients over a 1-year period. Of the 923 exposed patients, 745 (80.7%) were exposed to 1 DCI event, 92 (10%) to 2 DCI events, 35 (3.8%) to 3 DCI events, 18 (2%) to 4 DCI events, and 33 patients (3.6%) were exposed to 5 or more DCI events. The average age of the exposed patients was 30.7 ± 15 y, and 51.5% were male. Multivariate logistic regression analysis revealed that being male (OR = 1.3; 95% CI = 1.1 - 1.5; p < 0.001), being a member of the staff (OR = 3; 95% CI = 2.5 - 3.7; p < 0.001), having 4 or more prescribers (OR = 2.8; 95% CI = 2.2 - 3.6; p < 0.001), and having 4 or more longterm therapeutic groups (OR = 2.3; 95%CI = 1.7 - 3.1; p < 0.001), were significantly associated with increased chance of exposure to DCIs.
DISCUSSION AND CONCLUSIONS: This is the first study in Malaysia that presents data on the prevalence of DCIs. The prescription of contraindicated drugs was found to be frequent in this primary care setting. Exposure to DCI events was associated with specific socio-demographic and health status factors. Further research is needed to evaluate the relationship between health outcomes and the exposure to DCIs.
This paper focuses on the detection of retinal blood vessels which play a vital role in reducing the proliferative diabetic retinopathy and for preventing the loss of visual capability. The proposed algorithm which takes advantage of the powerful preprocessing techniques such as the contrast enhancement and thresholding offers an automated segmentation procedure for retinal blood vessels. To evaluate the performance of the new algorithm, experiments are conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm performs better than the other known algorithms in terms of accuracy. Furthermore, the proposed algorithm being simple and easy to implement, is best suited for fast processing applications.
The influx of medicines from different sources into healthcare systems of developing countries presents a challenge to monitor their origin and quality. The absence of a repository of reference samples or spectra prevents the analysis of tablets by direct comparison. A set of paracetamol tablets purchased in Malaysian pharmacies were compared to a similar set of sample purchased in the UK using near-infrared spectroscopy (NIRS). Additional samples of products containing ibuprofen or paracetamol in combination with other actives were added to the study as negative controls. NIR spectra of the samples were acquired and compared by using multivariate modeling and classification algorithms (PCA/SIMCA) and stored in a spectral database. All analysed paracetamol samples contained the purported active ingredient with only 1 out of 20 batches excluded from the 95% confidence interval, while the negative controls were clearly classified as outliers of the set. Although the substandard products were not detected in the purchased sample set, our results indicated variability in the quality of the Malaysian tablets. A database of spectra was created and search methods were evaluated for correct identification of tablets. The approach presented here can be further developed as a method for identifying substandard pharmaceutical products.
In 2009 the International Society for Computational Biology (ISCB) started to roll out regional bioinformatics conferences in Africa, Latin America and Asia. The open and competitive bid for the first meeting in Asia (ISCB-Asia) was awarded to Asia-Pacific Bioinformatics Network (APBioNet) which has been running the International Conference on Bioinformatics (InCoB) in the Asia-Pacific region since 2002. InCoB/ISCB-Asia 2011 is held from November 30 to December 2, 2011 in Kuala Lumpur, Malaysia. Of 104 manuscripts submitted to BMC Genomics and BMC Bioinformatics conference supplements, 49 (47.1%) were accepted. The strong showing of Asia among submissions (82.7%) and acceptances (81.6%) signals the success of this tenth InCoB anniversary meeting, and bodes well for the future of ISCB-Asia.
Generalized adaptive resonance theory (GART) is a neural network model that is capable of online learning and is effective in tackling pattern classification tasks. In this paper, we propose an improved GART model (IGART), and demonstrate its applicability to power systems. IGART enhances the dynamics of GART in several aspects, which include the use of the Laplacian likelihood function, a new vigilance function, a new match-tracking mechanism, an ordering algorithm for determining the sequence of training data, and a rule extraction capability to elicit if-then rules from the network. To assess the effectiveness of IGART and to compare its performances with those from other methods, three datasets that are related to power systems are employed. The experimental results demonstrate the usefulness of IGART with the rule extraction capability in undertaking classification problems in power systems engineering.
WHAT IS KNOWN AND OBJECTIVE: Drug-drug interactions (DDIs) cause considerable morbidity and mortality worldwide and may lead to hospital admission. Sophisticated computerized drug information and monitoring systems, more recently established in many of the emerging economies, including Malaysia, are capturing useful information on prescribing. Our aim is to report on an investigation of potentially serious DDIs, using a university primary care-based system capturing prescription records from its primary care services.
METHODS: We retrospectively collected data from two academic years over 20 months from computerized databases at the Universiti Sains Malaysia (USM) from users of the USM primary care services.
RESULTS AND DISCUSSION: Three hundred and eighty-six DDI events were observed in a cohort of 208 exposed patients from a total of 23,733 patients, representing a 2-year period prevalence of 876·4 per 100,000 patients. Of the 208 exposed patients, 138 (66·3%) were exposed to one DDI event, 29 (13·9%) to two DDI events, 15 (7·2%) to three DDI events, 6 (2·9%) to four DDI events and 20 (9·6%) to more than five DDI events. Overall, an increasing mean number of episodes of DDIs was noted among exposed patients within the age category ≥70 years (P=0·01), an increasing trend in the number of medications prescribed (P<0·001) and an increasing trend in the number of long-term therapeutic groups (P<0·001).
WHAT IS NEW AND CONCLUSION: We describe the prevalence of clinically important DDIs in an emerging economy setting and identify the more common potentially serious DDIs. In line with the observations in developed economies, a higher number of episodes of DDIs were seen in patients aged ≥70 years and with more medications prescribed. The easiest method to reduce the frequency of DDIs is to reduce the number of medications prescribed. Therapeutic alternatives should be selected cautiously.