Recent studies indicate high density lipoproteins (HDL) and their major structural protein, apolipoprotein A1 (apoA1), recovered from human atheroma, are dysfunctional and extensively oxidized by myeloperoxidase (MPO), while in vitro oxidation of apoA1/HDL by MPO impairs its cholesterol acceptor function. We developed a high affinity monoclonal antibody (mAb) that specifically recognizes apoA1/HDL modified by the MPO/H2O2/Cl-system using phage display affinity maturation. An oxindolyl alanine (2-OH-Trp) moiety at tryptophan 72 of apoA1 is the immunogenic epitope. Mutagenesis studies confirm a critical role for apoA1 Trp72 in MPO-mediated inhibition of ABCA1-dependent cholesterol acceptor activity of apoA1 in vitro and in vivo. ApoA1 containing a 2-OH-Trp72 group (oxTrp72-apoA1) is in low abundance within the circulation, but accounts for 20% of the apoA1 in atherosclerotic plaque. OxTrp72-apoA1 recovered from human atheroma or plasma was lipid-poor, virtually devoid of cholesterol acceptor activity, and demonstrated both potent pro-inflammatory activities on endothelial cells and impaired HDL biogenesis activity in vivo. Elevated oxTrp72-apoA1 levels in subjects presenting to a cardiology clinic (n=627) were associated with increased cardiovascular disease risk. Circulating oxTrp72-apoA1 levels may serve as a way to monitor a pro-atherogenic process in the artery wall.

BackgroundChikungunya and dengue infections are spatio-temporally related. The current review aims to determine the geographic limits of chikungunya, dengue and the principal mosquito vectors for both viruses and to synthesise current epidemiological understanding of their co-distribution.MethodsThree biomedical databases (PubMed, Scopus and Web of Science) were searched from their inception until May 2015 for studies that reported concurrent detection of chikungunya and dengue viruses in the same patient. Additionally, data from WHO, CDC and Healthmap alerts were extracted to create up-to-date global distribution maps for both dengue and chikungunya.ResultsEvidence for chikungunya-dengue co-infection has been found in Angola, Gabon, India, Madagascar, Malaysia, Myanmar, Nigeria, Saint Martin, Singapore, Sri Lanka, Tanzania, Thailand and Yemen; these constitute only 13 out of the 98 countries/territories where both chikungunya and dengue epidemic/endemic transmission have been reported.ConclusionsUnderstanding the true extent of chikungunya-dengue co-infection is hampered by current diagnosis largely based on their similar symptoms. Heightened awareness of chikungunya among the public and public health practitioners in the advent of the ongoing outbreak in the Americas can be expected to improve diagnostic rigour. Maps generated from the newly compiled lists of the geographic distribution of both pathogens and vectors represent the current geographical limits of chikungunya and dengue, as well as the countries/territories at risk of future incursion by both viruses. These describe regions of co-endemicity in which lab-based diagnosis of suspected cases is of higher priority.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-016-1417-2) contains supplementary material, which is available to authorized users.

Background:With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention.Objectives:We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak.Methods:We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore.Results:Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore’s dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response.Conclusions:Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue.Citation:Shi Y, Liu X, Kok SY, Rajarethinam J, Liang S, Yap G, Chong CS, Lee KS, Tan SS, Chin CK, Lo A, Kong W, Ng LC, Cook AR. 2016. Three-month real-time dengue forecast models: an early warning system for outbreak alerts and policy decision support in Singapore. Environ Health Perspect 124:1369–1375; http://dx.doi.org/10.1289/ehp.1509981

BackgroundSingapore experiences endemic dengue, with 2013 being the largest outbreak year known to date, culminating in 22,170 cases. Given the limited resources available, and that vector control is the key approach for prevention in Singapore, it is important that public health professionals know where resources should be invested in. This study aims to stratify the spatial risk of dengue transmission in Singapore for effective deployment of resources.Methodology/principal findingsRandom Forest was used to predict the risk rank of dengue transmission in 1km2 grids, with dengue, population, entomological and environmental data. The predicted risk ranks are categorized and mapped to four color-coded risk groups for easy operation application. The risk maps were evaluated with dengue case and cluster data. Risk maps produced by Random Forest have high accuracy. More than 80% of the observed risk ranks fell within the 80% prediction interval. The observed and predicted risk ranks were highly correlated (≥0.86, P <0.01). Furthermore, the predicted risk levels were in excellent agreement with case density, a weighted Kappa coefficient of more than 0.80 (P <0.01). Close to 90% of the dengue clusters occur in high risk areas, and the odds of cluster forming in high risk areas were higher than in low risk areas.ConclusionsThis study demonstrates the potential of Random Forest and its strong predictive capability in stratifying the spatial risk of dengue transmission in Singapore. Dengue risk map produced using Random Forest has high accuracy, and is a good surveillance tool to guide vector control operations.

BackgroundIn 2013 and 2014, Singapore experienced its worst dengue outbreak known-to-date. Mosquito breeding in construction sites stood out as a probable risk factor due to its association with major dengue clusters in both years. We, therefore, investigated the contribution of construction sites to dengue transmission in Singapore, highlighting three case studies of large construction site-associated dengue clusters recorded during 2013–16.MethodsThe study included two components; a statistical analysis of cluster records from 2013 to 2016, and case studies of three biggest construction site-associated clusters. We explored the odds of construction site-associated clusters growing into major clusters and determined whether clusters seeded in construction sites demonstrated a higher tendency to expand into major clusters. DENV strains obtained from dengue patients residing in three major clusters were genotyped to determine whether the same strains expanded into the surroundings of construction sites.ResultsDespite less than 5% of total recorded clusters being construction site-associated, the odds of such clusters expanding into major clusters were 17.4 (2013), 9.2 (2014), 3.3 (2015) and 4.3 (2016) times higher than non-construction site clusters. Aedes premise index and average larvae count per habitat were also higher in construction sites than residential premises during the study period. The majority of cases in clusters associated with construction sites were residents living in the surroundings. Virus genotype data from three case study sites revealed a transmission link between the construction sites and the surrounding residential areas.ConclusionsSignificantly high case burden and the probability of cluster expansion due to virus spill-over into surrounding areas suggested that construction sites play an important role as a driver of sustained dengue transmission. Our results emphasise that the management of construction-site associated dengue clusters should not be limited to the implicated construction sites, but be extended to the surrounding premises to prevent further transmission.Electronic supplementary materialThe online version of this article (10.1186/s12879-018-3311-6) contains supplementary material, which is available to authorized users.