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Introduction: Although there have been several phylogenetic studies on Plasmodium knowlesi (P. knowlesi), only cytochrome c oxidase subunit 1 (COX1) gene analysis has shown some geographical differentiation between the isolates of different countries.Methods: Phylogenetic analysis of locally acquired P. knowlesi infections, based on circumsporozoite, small subunit ribosomal ribonucleic acid (SSU rRNA), merozoite surface protein 1 and COX1 gene targets, was performed. The results were compared with the published sequences of regional isolates from Malaysia and Thailand.Results: Phylogenetic analysis of the circumsporozoite, SSU rRNA and merozoite surface protein 1 gene sequences for regional P. knowlesi isolates showed no obvious differentiation that could be attributed to their geographical origin. However, COX1 gene analysis showed that it was possible to differentiate between Singapore-acquired P. knowlesi infections and P. knowlesi infections from Peninsular Malaysia and Sarawak, Borneo, Malaysia.Conclusion: The ability to differentiate between locally acquired P. knowlesi infections and imported P. knowlesi infections has important utility for the monitoring of P. knowlesi malaria control programmes in Singapore. [ABSTRACT FROM AUTHOR]

Introduction: Influenza infections present with wide-ranging clinical features. We aim to compare the differences in presentation between influenza and non-influenza cases among those with febrile respiratory illness (FRI) to determine predictors of influenza infection. Methods: Personnel with FRI (defined as fever⩾37.5°C, with cough or sore throat) were recruited from the sentinel surveillance system in the Singapore military. Nasal washes were collected, and tested using the Resplex II and additional PCR assays for etiological determination. Interviewer-administered questionnaires collected information on patient demographics and clinical features. Univariate comparison of the various parameters was conducted, with statistically significant parameters entered into a multivariate logistic regression model. The final multivariate model for influenza versus non-influenza cases was used to build a predictive probability clinical diagnostic model. Results: 821 out of 2858 subjects recruited from 11 May 2009 to 25 Jun 2010 had influenza, of which 434 (52.9%) had 2009 influenza A (H1N1), 58 (7.1%) seasonal influenza A (H3N2) and 269 (32.8%) influenza B. Influenza-positive cases were significantly more likely to present with running nose, chills and rigors, ocular symptoms and higher temperature, and less likely with sore throat, photophobia, injected pharynx, and nausea/vomiting. Our clinical diagnostic model had a sensitivity of 65% (95% CI: 58%, 72%), specificity of 69% (95% CI: 62%, 75%), and overall accuracy of 68% (95% CI: 64%, 71%), performing significantly better than conventional influenza-like illness (ILI) criteria. Conclusions: Use of a clinical diagnostic model may help predict influenza better than the conventional ILI definition among young adults with FRI. [ABSTRACT FROM AUTHOR]

Background: Few studies have comprehensively described tropical respiratory disease surveillance in military populations. There is also a lack of studies comparing clinical characteristics of the non-influenza pathogens with influenza and amongst themselves.Methods: From May 2009 through October 2012, 7733 consenting cases of febrile respiratory illness (FRI) (temperature [greater than or equal to]37.5 degrees C with cough or sorethroat) and controls in the Singapore military had clinical data and nasal washes collected prospectively. Nasal washes underwent multiplex PCR, and the analysis was limited to viral mono-infections.Results: 49% of cases tested positive for at least one virus, of whom 10% had multiple infections. 53% of the FRI cases fulfilled the definition of influenza-like illness (ILI), of whom 52% were positive for at least one virus. The most frequent etiologies for mono-infections among FRI cases were Influenza A(H1N1)pdm09 (13%), Influenza B (13%) and coxsackevirus (9%). The sensitivity, specificity, positive predictive value and negative predictive value of ILI for influenza among FRI cases were 72%, 48%, 40% and 69% respectively. On logistic regression, there were marked differences in the prevalence of different symptoms and signs between viruses with fever more prevalent amongst influenza and adenovirus infections than other viruses.Conclusion: There are multiple viral etiologies for FRI and ILI with differing clinical symptoms in the Singapore military. Influenza and coxsackevirus were the most common etiology for FRI, while influenza and adenoviruses displayed the most febrile symptoms. Further studies should explore these differences and possible interventions. [ABSTRACT FROM AUTHOR]