Investigation of the impacts of climate change and rising temperature on food poisoning cases in Malaysia.

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.

Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA Model.

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.

Multivariate statistical analysis of the essential oils of five Beilschmiedia species from Peninsular Malaysia / Análisis estadístico multivariado de los aceites esenciales de especies de Beilschmiedia de Malasia peninsular

Identification of the chemical compositionof essential oils is very important for ensuring the quality of finished herbal products. The objective of the study was to analyze the chemical components present in the essential oils of five Beilschmiediaspecies (i.e. B. kunstleri, B. maingayi, B. penangiana, B. madang, and B. glabra) by multivariate data analysis using principal component analysis (PCA) and hierarchical clustering analysis (HCA) methods. The essential oils were obtained by hydrodistillation and fully characterized by gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS). A total of 108 chemical components were successfully identified from the essential oils of five Beilschmiediaspecies. The essential oils were characterized by high proportions of ß-caryophyllene (B.kunstleri), δ-cadinene (B. penangianaand B. madang), and ß-eudesmol (B. maingayiand B. glabra). Principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed that chemical similarity was highest for all samples, except for B. madang. The multivariate data analysis may be used for the identification and characterization of essential oils from different Beilschmiediaspecies that are to be used as raw materials of traditional herbal products.

La identificación de la composición química de los aceites esenciales es muy importante para garantizar la calidad de los productos herbales terminados. El objetivo del estudio fue analizar los componentes químicos presentes en los aceites esenciales de cinco especies de Beilschmiedia (B. kunstleri, B. maingayi, B. penangiana, B. madangy B. glabra) mediante análisis de datos multivariados utilizando los métodos de análisis de componente principal (PCA) y análisis de agrupamiento jerárquico (HCA). Los aceites esenciales se obtuvieron por hidrodestilación y se caracterizaron completamente por cromatografía de gases (GC) y cromatografía de gases-espectrometría de masas (GC-MS). Se identificaron con éxito un total de 108 componentes químicos a partir de los aceites esenciales de las cinco especies de Beilschmiedia. Los aceites esenciales se caracterizaron por altas proporciones de ß-cariofileno (B. kunstleri), δ-cadineno (B. penangianay B. madang) y ß-eudesmol (B. maingayiy B. glabra). El análisis de componentes principales (PCA) y el análisis de conglomerados jerárquicos (HCA) revelaron que la similitud química fue más alta para todas las muestras, excepto para B. madang. El análisis de datos multivariados puede usarse para la identificación y caracterización de aceites esenciales de diferentes especies de Beilschmiedia que se utilizan como materias primas de productos herbales tradicionales.

Chemical composition of the essential oils of four Polyalthia species from Malaysia.

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 ß-elemene (11.9%) for P. sumatrana; α-cadinol (13.0%) and δ-cadinene (10.2%) for P. stenopetalla; δ-elemene (38.1%) and ß-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.