In a case-control study carried out in the Kuala Lumpur Maternity Hospital between 1st July 1995 and 31st January 1996 the objectives were (1) to determine the rate of breastfeeding in surviving very low birthweight (VLBW, < or = 1500 g) Malaysian infants following the introduction of the Baby Friendly Hospital Concept, and (2) to identify significant predictors associated with successful breastfeeding in these infants. During the study period, 201 (1.24 per cent) of live-born infants were VLBW infants, 192 (95.5 per cent) were Malaysians, and 141 (73.4 per cent) of them survived to go home. The breastfeeding rate among all surviving VLBW Malaysian infants at the time of discharge was 40.2 per cent (57/141). The mothers of 126 (89.4 per cent) VLBW Malaysian infants were interviewed before discharge. Logistic regression analysis showed that, after controlling for various confounders, the significant predictors associated with successful breastfeeding were: (a) Malay mothers (odds ratio: 6.0; 95 per cent CI: 1.9, 19.4), (b) mothers with educational levels of between 7 and 9 years (odds ratio: 3.6; 95 per cent CI: 1.0, 12.2), and (c) earlier age of commencement of enteral feeds in the VLBW infants (for each additional day delay in commencement of feeding, odds ratio of breastfeeding was 0.5; 95 per cent CI: 0.4, 0.8).
Immunology is a discipline that traverses all branches of clinical medicine. Thus since about ten years ago major hospitals in Malaysia established routine clinical immunology services particularly in the diagnosis of autoimmune/connective tissue disorders. More recently these laboratories have ventured into basic research in Dengue Haemorrhagic Fever, Leukaemia Immunology, Nasopharyngeal Cancer and Leprosy. The rationale for these projects together with early results from them are discussed.
In 1998, Malaysia opened its first hospital based on the "paperless and filmless" concept. Two are now in operation, with more to follow. Telemedicine is now being used in some hospitals and is slated to be the technology to watch. Future use of technology in health care will centre on the use of centralised patient databases and more effective use of artificial intelligence. Stumbling blocks include the enormous capital costs involved and difficulty in getting sufficient bandwidth to support applications on a national scale. Problems with the use of information technology in developing countries still remain; mainly inadequate skilled resources to operate and maintain the technology, lack of home-grown technology, insufficient experience in the use of information technology in health care and the attitudes of some health staff. The challenge for those involved in this field will not be in building new "paperless and filmless" institutions but in transforming current "paper and film-based" institutions to "paperless and filmless" ones and changing the mindset of health staff. Universities and medical schools must be prepared to respond to this new wave by incorporating elements of medical/health informatics in their curriculum and assisting governments in the planning and implementation of these projects. The experience of the UMMC is highlighted as an example of the difficulty of transforming a paper-based hospital to a "paperless and filmless" hospital.
Positron emission tomography (PET) combined with computed tomography (CT) is an established diagnostic modality that has become an essential imaging tool in oncological practice. However, thanks to its noninvasive nature and its capability to provide physiological information, the main applications of this technique have significantly expanded.(18)F-labelled fluorodeoxyglucose (FDG) is the most commonly used radiopharmaceutical for PET scanning and demonstrates metabolic activity in various tissues. Since activated inflammatory cells, like malignant cells, predominantly metabolise glucose as a source of energy and increase expression of glucose transporters when activated, FDG-PET/CT can be successfully used to detect and monitor a variety of lung diseases, such as infections and several inflammatory conditions.The added value of FDG-PET/CT as a molecular imaging technique relies on its capability to identify disease in very early stages, long before the appearance of structural changes detectable by conventional imaging. Furthermore, by detecting the active phase of infectious or inflammatory processes, disease progression and treatment efficacy can be monitored.This review will focus on the clinical use of FDG-PET/CT in nonmalignant pulmonary diseases.
In the rapidly growing economies of Asia and Oceania, food security has become a primary concern. With the rising population, growing more food at affordable prices is becoming even more important. In addition, the predicted climate change will lead to drastic changes in global surface temperature and changes in rainfall patterns that in turn will pose a serious threat to plant vegetation worldwide. As a result, understanding how plants will survive in a changing climate will be increasingly important. Such challenges require integrated approaches to increase agricultural production and cope with environmental threats. Proteomics can play a role in unraveling the underlying mechanisms for food production to address the growing demand for food. In this review, the current status of food crop proteomics is discussed, especially in regard to the Asia and Oceania regions. Furthermore, the future perspective in relation to proteomic techniques for the important food crops is highlighted.
Our study aimed to examine the interplay between clinical and social predictors of future suicide attempt and the transition from suicidal ideation to suicide attempt in depressive disorders. Sixty-six Malaysian inpatients with a depressive disorder were assessed at index admission and within 1 year for suicide attempt, suicidal ideation, depression severity, life event changes, treatment history and relevant clinical and socio-demographic factors. One-fifth of suicidal ideators transitioned to a future suicide attempt. All future attempters (12/66) had prior ideation and 83% of attempters had a prior attempt. The highest risk for transitioning from ideation to attempt was 5 months post-discharge. Single predictor models showed that previous psychiatric hospitalization and ideation severity were shared predictors of future attempt and ideation to attempt transition. Substance use disorders (especially alcohol) predicted future attempt and approached significance for the transition process. Low socio-economic status predicted the transition process while major personal injury/illness predicted future suicide attempt. Past suicide attempt, subjective depression severity and medication compliance predicted only future suicide attempt. The absence of prior suicide attempt did not eliminate the risk of future attempt. Given the limited sample, future larger studies on mechanisms underlying the interactions of such predictors are needed.
The motivation behind this research is to innovatively combine new methods like wavelet, principal component analysis (PCA), and artificial neural network (ANN) approaches to analyze trade in today's increasingly difficult and volatile financial futures markets. The main focus of this study is to facilitate forecasting by using an enhanced denoising process on market data, taken as a multivariate signal, in order to deduct the same noise from the open-high-low-close signal of a market. This research offers evidence on the predictive ability and the profitability of abnormal returns of a new hybrid forecasting model using Wavelet-PCA denoising and ANN (named WPCA-NN) on futures contracts of Hong Kong's Hang Seng futures, Japan's NIKKEI 225 futures, Singapore's MSCI futures, South Korea's KOSPI 200 futures, and Taiwan's TAIEX futures from 2005 to 2014. Using a host of technical analysis indicators consisting of RSI, MACD, MACD Signal, Stochastic Fast %K, Stochastic Slow %K, Stochastic %D, and Ultimate Oscillator, empirical results show that the annual mean returns of WPCA-NN are more than the threshold buy-and-hold for the validation, test, and evaluation periods; this is inconsistent with the traditional random walk hypothesis, which insists that mechanical rules cannot outperform the threshold buy-and-hold. The findings, however, are consistent with literature that advocates technical analysis.
BACKGROUND AND AIM:
There is currently no published study comparing prevalence of non-alcoholic fatty liver disease (NAFLD) and associated factors among diabetics of different ethnicity in the Asia-Pacific region.
METHODS:
Cross-sectional study of consecutive patients in the Diabetic Clinic in University of Malaya Medical Centre. The Global Physical Activity Questionnaire and a semiquantitative food-frequency questionnaire were used to assess physical activity and dietary intake, respectively. Diagnosis of NAFLD was ultrasound-based and following exclusion of significant alcohol intake.
RESULTS:
Data for 399 patients were analyzed (mean age 62.3 ± 10.5 years, 43.1% men). The racial distribution was Chinese 43.6%, Indian 33.1%, Malay 22.3%, and others 1.0%. The prevalence of NAFLD was 49.6%. On univariate analysis, factors associated with NAFLD were age < 65 years, race, obesity, central obesity, glycated hemoglobin ≥ 7.0%, and elevated serum alanine aminotransferase (ALT) and gamma-glutamyl transpeptidase levels. Patients with low physical activity were more likely to have NAFLD (odds ratio [OR] = 1.67, 95% confidence interval [CI] = 1.06-2.63, P = 0.020). The prevalence of NAFLD was highest among Malays (60.7%), followed by Indians (51.5%), and lowest among Chinese (42.0%) consistent with higher prevalence of central obesity and higher percentage calorie intake from fat in the former groups of patients. On multivariate analysis, independent factors associated with NAFLD were central obesity (OR = 2.20, 95% CI = 1.29-3.75, P = 0.004) and elevated serum ALT level (OR = 1.98, 95% CI = 1.21-3.25, P = 0.007).
CONCLUSIONS:
NAFLD was seen in half of a cohort of diabetic patients and was independently associated with central obesity and elevated serum ALT level. Prevalence of NAFLD was different and paralleled the difference in prevalence of central obesity and in percentage calorie intake from fat among the different ethnic groups.
The system of medical education has not changed much over the years. This article discusses the present method of teaching of medical students. Suggestions for change in the methods have been suggested in order to produce better doctors.
Blood is conventionally thought to be sterile. However, emerging evidence on the blood microbiome has started to challenge this notion. Recent reports have revealed the presence of genetic materials of microbes or pathogens in the blood circulation, leading to the conceptualization of a blood microbiome that is vital for physical wellbeing. Dysbiosis of the blood microbial profile has been implicated in a wide range of health conditions. Our review aims to consolidate recent findings about the blood microbiome in human health and to highlight the existing controversies, prospects, and challenges around this topic. Current evidence does not seem to support the presence of a core healthy blood microbiome. Common microbial taxa have been identified in some diseases, for instance, Legionella and Devosia in kidney impairment, Bacteroides in cirrhosis, Escherichia/Shigella and Staphylococcus in inflammatory diseases, and Janthinobacterium in mood disorders. While the presence of culturable blood microbes remains debatable, their genetic materials in the blood could potentially be exploited to improve precision medicine for cancers, pregnancy-related complications, and asthma by augmenting patient stratification. Key controversies in blood microbiome research are the susceptibility of low-biomass samples to exogenous contamination and undetermined microbial viability from NGS-based microbial profiling, however, ongoing initiatives are attempting to mitigate these issues. We also envisage future blood microbiome research to adopt more robust and standardized approaches, to delve into the origins of these multibiome genetic materials and to focus on host-microbe interactions through the elaboration of causative and mechanistic relationships with the aid of more accurate and powerful analytical tools.
The accuracy of financial time series forecasts often rely on the model precision and the availability of actual observations for forecast evaluations. This study aimed to tackle these issues in order to obtain a suitable asymmetric time-varying volatility model that outperformed in the forecast evaluations based on interday and intraday data. The model precision was examined based on the most appropriate time-varying volatility representation under the autoregressive conditional heteroscedascity framework. For forecast precision, the evaluations were conducted under three loss functions using the volatility proxies and realized volatility. The empirical studies were implemented on two major financial markets and the estimated results are applied in quantifying their market risks. Empirical results indicated that Zakoian model provided the best in-sample forecasts whereas DGE on the other hand indicated better out-of-sample forecasts. For the type of volatility proxy selection, the implementation of intraday data in the latent volatility indicated significant improvement in all the time horizon forecasts.
The heterogeneous autoregressive (HAR) models are used in modeling high frequency multipower realized volatility of the
S&P 500 index. Extended from the standard realized volatility, the multipower realized volatility representations have
the advantage of handling the possible abrupt jumps by smoothing the consecutive volatility. In order to accommodate
clustering volatility and asymmetric of multipower realized volatility, the HAR model is extended by the threshold
autoregressive conditional heteroscedastic (GJR-GARCH) component. In addition, the innovations of the multipower realized
volatility are characterized by the skewed student-t distributions. The extended model provides the best performing insample
and out-of-sample forecast evaluations.
A common phenomenon in South-East Asia is ageing of the population. This article describes the various stages of development of psychogeriatrics in Hong Kong, Singapore, Malaysia, Thailand, Indonesia and the Philippines. It is only in the last few years that more systematic development of psychogeriatric services has begun under the pressure of an ageing population. The model of service delivery in Hong Kong can serve as an example of development of psychogeriatric services in South-East Asia.
This study aims to provide baseline data to support the implementation of generic substitution policy in Malaysia by evaluating the community pharmacists' perceptions and opinions on generic substitution and current substitution practices.
The drug-eluting balloon (DEB) is an exciting new technology that holds much promise. As an evolving technology undergoing intensive research, the device is being constantly refined and its numerous potential applications studied. Though initially created to fulfil specific needs in the coronary vasculature, there is great potential for its use in other vascular territories and structures including the management of valvular, congenital heart and neuro-interventional pathologies. In addition, the application of this device in conjunction with other existing technologies may enhance the clinical results.
Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers' future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2-13.6% and 10.2-12.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9-12.7% and 6.9-8.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account-namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.
The development of effluent removal prediction is crucial in providing a planning tool necessary for the future development and the construction of a septic sludge treatment plant (SSTP), especially in the developing countries. In order to investigate the expected functionality of the required standard, the prediction of the effluent quality, namely biological oxygen demand, chemical oxygen demand and total suspended solid of an SSTP was modelled using an artificial intelligence approach. In this paper, we adopt the clonal selection algorithm (CSA) to set up a prediction model, with a well-established method - namely the least-square support vector machine (LS-SVM) as a baseline model. The test results of the case study showed that the prediction of the CSA-based SSTP model worked well and provided model performance as satisfactory as the LS-SVM model. The CSA approach shows that fewer control and training parameters are required for model simulation as compared with the LS-SVM approach. The ability of a CSA approach in resolving limited data samples, non-linear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP.