Polyalthia is one of the largest genera in the Annonaceae family, and has been widely used in folk medicine for the treatment of rheumatic fever, gastrointestinal ulcer, and generalized body pain. The present investigation reports on the extraction by hydrodistillation and the composition of the essential oils of four Polyalthia species (P. sumatrana, P. stenopetalla, P. cauliflora, and P. rumphii) growing in Malaysia. The chemical composition of these essential oils was determined by gas chromatography (GC-FID) and gas chromatography-mass spectrometry (GC-MS). The multivariate analysis was determined using principal component analysis (PCA) and hierarchical clustering analysis (HCA) methods. The results revealed that the studied essential oils are made up principally of bicyclogermacrene (18.8%), cis-calamenene (14.6%) and β-elemene (11.9%) for P. sumatrana; α-cadinol (13.0%) and δ-cadinene (10.2%) for P. stenopetalla; δ-elemene (38.1%) and β-cubebene (33.1%) for P. cauliflora; and finally germacrene D (33.3%) and bicyclogermacrene for P. rumphii. PCA score and HCA plots revealed that the essential oils were classified into three separated clusters of P. cauliflora (Cluster I), P. sumatrana (Cluster II), and P. stenopetalla, and P. rumphii (Cluster III) based on their characteristic chemical compositions. Our findings demonstrate that the essential oil could be useful for the characterization, pharmaceutical, and therapeutic applications of Polyalthia essential oil.
Novel coronavirus (COVID-19) was discovered in Wuhan, China in December 2019, and has affected millions of lives worldwide. On 29th April 2020, Malaysia reported more than 5,000 COVID-19 cases; the second highest in the Southeast Asian region after Singapore. Recently, a forecasting model was developed to measure and predict COVID-19 cases in Malaysia on daily basis for the next 10 days using previously-confirmed cases. A Recurrent Forecasting-Singular Spectrum Analysis (RF-SSA) is proposed by establishing L and ET parameters via several tests. The advantage of using this forecasting model is it would discriminate noise in a time series trend and produce significant forecasting results. The RF-SSA model assessment was based on the official COVID-19 data released by the World Health Organization (WHO) to predict daily confirmed cases between 30th April and 31st May, 2020. These results revealed that parameter L = 5 (T/20) for the RF-SSA model was indeed suitable for short-time series outbreak data, while the appropriate number of eigentriples was integral as it influenced the forecasting results. Evidently, the RF-SSA had over-forecasted the cases by 0.36%. This signifies the competence of RF-SSA in predicting the impending number of COVID-19 cases. Nonetheless, an enhanced RF-SSA algorithm should be developed for higher effectivity of capturing any extreme data changes.
This study is an attempt to investigate climate-induced increases in morbidity rates of food poisoning cases. Monthly food poisoning cases, average monthly meteorological data, and population data from 2004 to 2014 were obtained from the Malaysian Ministry of Health, Malaysian Meteorological Department, and Department of Statistics Malaysia, respectively. Poisson generalised linear models were developed to assess the association between climatic parameters and the number of reported food poisoning cases. The findings revealed that the food poisoning incidence in Malaysia during the 11 years study period was 561 cases per 100 000 population for the whole country. Among the cases, females and the ethnic Malays most frequently experienced food poisoning with incidence rates of 313 cases per 100,000 and 438 cases per 100,000 population over the period of 11 years, respectively. Most of the cases occurred within the active age of 13 to 35 years old. Temperature gave a significant impact on the incidence of food poisoning cases in Selangor (95% CI: 1.033-1.479; p = 0.020), Melaka (95% CI: 1.046-2.080; p = 0.027), Kelantan (95% CI: 1.129-1.958; p = 0.005), and Sabah (95% CI: 1.127-2.690; p = 0.012) while rainfall was a protective factor in Terengganu (95% CI: 0.996-0.999; p = 0.034) at lag 0 month. For a 1.0°C increase in temperature, the excess risk of food poisoning in each state can increase up to 74.1%, whereas for every 50 mm increase in rainfall, the risk of getting food poisoning decreased by almost 10%. The study concludes that climate does affect the distribution of food poisoning cases in Selangor, Melaka, Kelantan, Sabah, and Terengganu. Food poisoning cases in other states are not directly associated with temperature but related to monthly trends and seasonality.