RESULTS: Xylanase was successfully expressed in Lactococcus lactis. Recombinant xylanase fused to either signal peptide Usp45 or Spk1 showed halo zone on Remazol Brilliant Blue-Xylan plates. This indicated that the xylanase was successfully secreted from the cell. The culture supernatants of strains secreting the xylanase with help of the Spk1 and Usp45 signal peptides contained 49.7 U/ml and 34.4 U/ml of xylanase activity, respectively.
CONCLUSION: Although Usp45 is the most commonly used signal peptide when secreting heterologous proteins in Lactococcus lactis, this study shows that Spk1 isolated from Pediococcus pentosaceus was superior to Usp45 in regard to xylanase protein secretion.
METHODS: We conducted a two year study in a high human density dengue-endemic urban area in Selangor, where Gravid Ovipositing Sticky (GOS) traps were set up to capture adult Aedes spp. mosquitoes. All Aedes mosquitoes were tested using the NS1 dengue antigen test kit. All dengue cases from the study site notified to the State Health Department were recorded. Weekly microclimatic temperature, relative humidity (RH) and rainfall were monitored.
RESULTS: Aedes aegypti was the predominant mosquito (95.6%) caught in GOS traps and 23% (43/187 pools of 5 mosquitoes each) were found to be positive for dengue using the NS1 antigen kit. Confirmed cases of dengue were observed with a lag of one week after positive Ae. aegypti were detected. Aedes aegypti density as analysed by distributed lag non-linear models, will increase lag of 2-3 weeks for temperature increase from 28 to 30 °C; and lag of three weeks for increased rainfall.
CONCLUSION: Proactive strategy is needed for dengue vector surveillance programme. One method would be to use the GOS trap which is simple to setup, cost effective (below USD 1 per trap) and environmental friendly (i.e. use recyclable plastic materials) to capture Ae. aegypti followed by a rapid method of detecting of dengue virus using the NS1 dengue antigen kit. Control measures should be initiated when positive mosquitoes are detected.
OBJECTIVE: This paper presents a rescue framework for the transfusion of the best CP to the most critical patients with COVID-19 on the basis of biological requirements by using machine learning and novel MCDM methods.
METHOD: The proposed framework is illustrated on the basis of two distinct and consecutive phases (i.e. testing and development). In testing, ABO compatibility is assessed after classifying donors into the four blood types, namely, A, B, AB and O, to indicate the suitability and safety of plasma for administration in order to refine the CP tested list repository. The development phase includes patient and donor sides. In the patient side, prioritisation is performed using a contracted patient decision matrix constructed between 'serological/protein biomarkers and the ratio of the partial pressure of oxygen in arterial blood to fractional inspired oxygen criteria' and 'patient list based on novel MCDM method known as subjective and objective decision by opinion score method'. Then, the patients with the most urgent need are classified into the four blood types and matched with a tested CP list from the test phase in the donor side. Thereafter, the prioritisation of CP tested list is performed using the contracted CP decision matrix.
RESULT: An intelligence-integrated concept is proposed to identify the most appropriate CP for corresponding prioritised patients with COVID-19 to help doctors hasten treatments.
DISCUSSION: The proposed framework implies the benefits of providing effective care and prevention of the extremely rapidly spreading COVID-19 from affecting patients and the medical sector.