Detoxification of Pyrodinium-generated paralytic shellfish poisoning toxin in Perna viridis from Western Samar, Philippines

The results are presented of procedures for the detoxification of paralytic shellfish poisoning toxin using ozone, chlorine and PVP-iodine. Findings indicate ozone and PVP-iodine to effectively inactivate the toxins isolated from Perna viridis; however, further investigations are recommended

A Collaborative Epidemiological Investigation into the Criminal Fake Artesunate Trade in South East Asia

Paul Newton and colleagues' international, collaborative study found evidence that counterfeit artesunate was being manufactured in China, which prompted a criminal investigation

Pollen of Southeast Asian Alchornea (Euphorbiaceae), with an overview of the pollen fossil record

In order to evaluate pollen morphological descriptions of Alchornea in the literature, which are almost completely based on African and American species, the pollen of eight Southeast Asian species of Alchornea was investigated, using light and scanning electron microscopy. Very little variation appeared to be present in the Asian material. Slightly deviating from the scabrate ornamentation type are A. kelungensis (psilate) and A. rugosa (striate-rugulate). The scabrate type is also found in A. castaneaefolia (Brazil), A. hirtella (Liberia) and A. obovata (Colombia). The operculate Alchornea pollen type, which can be easily recognised using light microscopy, seems to represent a diagnostic character for the tribe Alchornieae (pollen of Bossera unknown). Its characteristic appearance resulted in a relatively extensive fossil record. The earliest records are from the Middle Eocene of Venezuela and Nigeria, while records for Australia and Borneo date from the mid-Tertiary and the Neogene (Miocene–Pliocene), respectively. These records suggest that the tribe Alchornieae has an African– American Gondwanic origin, and reached its pantropic distribution at least in the mid-Tertiary

Effects of storage on the microbial quality of slipper oysters, Crassostrea iredalei

The effects of storage on the microbial quality of slipper oysters, Crassostrea iredalei, were examined. Oysters were stored at room temperature (24°C), under a blanket of ice (3-4 C), chilled (4-C) and frozen (-25°C) until they spoiled. The shelf life of oysters stored at room temperature was only two days. Oysters held under a blanket of ice had a shelf life of 14 days and chilled oysters, 22 days. Frozen oysters remained in good condition over the 64 day storage period. The initial total aerobic bacterial count of oysters was 105cfu/g. Counts for frozen oysters decreased by 1 log (104) while counts for oysters stored at other temperatures increased by 2-4 log (107-109). Bacterial typing of 50 randomly-picked colonies made every four days showed Pseudomonas to be the predominant spoilage organism. Total and fecal coliform counts did not increase even for oysters held at room temperature. Typical Staphylococcus aureaus colonies were isolated but were shown to be non-pathogenic by the coagulase test. Analyses for the presence of other organisms of public health concern revealed that Salmonella, Shigella, Vibrio cholerae, V. parahaemolyticus, Lactose + Vibrios (V. vulnificus) and fecal streptococci were present in very low or undetectable levels. Thus, hazards or risks associated with these organisms may be considered minimal

Depuration of molluscs

The experimental system put up at SEAFDEC [Southeast Asian Fisheries Development Center; Philippines] consisted of six rectangular (96 x 196 x 42 cm) fiberglass-coated tanks made of marine plywood. It can depurate about 230 to 310 kg bivalves in two days. Initial findings showed that under normal seawater conditions (salinity 29-32 ppt, temperature 27-30 degrees Celsius; oxygen content 3-6.2 mg/L; and pH 7.4-8.3) and moderate rate of flow (7-10 L/min), highly contaminated oysters (MPN 1.0 x 10 to the fifth power to 2.0 x 10 to the sixth power/100 g meat) can be depurated within 48 hr or less. A short flume type of tank with a volume of about 250 L was designed, tested and showed to cleanse oysters under normal conditions in only 24 hr with a flow rate of 7L/min and with very little resulting mortality. More important, the tank can be lifted and moved by only two men of average body built

Bacterial depuration of grossly-contaminated oysters, Crassostrea iredalei.

Oysters (Crassostrea iredalei ) from a commercial growing area in Capiz, Iloilo, Philippines, were purchased from the Iloilo City Central Market and used in a depuration trial within 24 hours of collection. Total coliform (TC) and fecal coliform (FC) levels were determined using the five-tube, most probable number (MPN) technique. Samplings were carried out in three areas in the tank: (a) near the water trickle are, (b) at the middle and (c) near the water outflow area. FC proved to be a better and more consistent indicator of depuration efficiency than TC which gave erratic levels in the first 24 hours. The oysters with initial FC MPN of 2.2 x 10 super(5)/100 g meat depurated to acceptable levels (< 230 MPN/100 g meat) after 48 hours except those in the middle of the tank (490 MPN/100 g). This suggests the presence of an "indifferent" or "dead" spot. Nevertheless, the same oysters depurated successfully within 72 hours. Ranges of chemical and physical parameters in the depuration water were: temperature, 27.0-29.5 degree C; salinity, 30.5-32.0 ppt; and dissolved oxygen, 4.0-6.2 mg/l

Machine learning approach to single nucleotide polymorphism-based asthma prediction

Machine learning (ML) is poised as a transformational approach uniquely positioned to discover the hidden biological interactions for better prediction and diagnosis of complex diseases. In this work, we integrated ML-based models for feature selection and classification to quantify the risk of individual susceptibility to asthma using single nucleotide polymorphism (SNP). Random forest (RF) and recursive feature elimination (RFE) algorithm were implemented to identify the SNPs with high implication to asthma. K-nearest neighbor (kNN) and support vector machine (SVM) algorithms were trained to classify the identified SNPs whether associated with non-asthmatic or asthmatic samples. Feature selection step showed that RF outperformed RFE and the feature importance score derived from RF was consistently high for a subset of SNPs, indicating the robustness of RF in selecting relevant features associated with asthma. Model comparison showed that the integration of RF-SVM obtained the highest model performance with an accuracy, precision, and sensitivity of 62.5%, 65.3%, and 69%, respectively, when compared to the baseline, RF-kNN, and an external MeanDiff-kNN models. Furthermore, results show that the occurrence of asthma can be predicted with an Area under the Curve (AUC) of 0.62 and 0.64 for RF-SVM and RF-kNN models, respectively. This study demonstrates the integration of ML models to augment traditional methods in predicting genetic predisposition to multifactorial diseases such as asthma