Energy budget of Drosophila embryogenesis.

Eggs of oviparous animals must be prepared to develop rapidly and robustly until hatching. The balance between sugars, fats, and other macromolecules must therefore be carefully considered when loading the egg with nutrients. Clearly, packing too much or too little fuel would lead to suboptimal conditions for development. While many studies have measured the overall energy utilization of embryos, little is known of the identity of the molecular-level processes that contribute to the energy budget in the first place [1]. Here, we introduce Drosophila embryos as a platform to study the energy budget of embryogenesis. We demonstrate through three orthogonal measurements - respiration, calorimetry, and biochemical assays - that Drosophila melanogaster embryogenesis utilizes 10 mJ of energy generated by the oxidation of the maternal glycogen and triacylglycerol (TAG) stores (Figure 1). Normalized for mass, this is comparable to the resting metabolic rates of insects [2]. Interestingly, alongside data from earlier studies, our results imply that protein, RNA, and DNA polymerization require less than 10% of the total ATPs produced in the early embryo.

Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness.

BACKGROUND: Dengue is re-emerging throughout the tropical world, causing frequent recurrent epidemics. The initial clinical manifestation of dengue often is confused with other febrile states confounding both clinical management and disease surveillance. Evidence-based triage strategies that identify individuals likely to be in the early stages of dengue illness can direct patient stratification for clinical investigations, management, and virological surveillance. Here we report the identification of algorithms that differentiate dengue from other febrile illnesses in the primary care setting and predict severe disease in adults. METHODS AND FINDINGS: A total of 1,200 patients presenting in the first 72 hours of acute febrile illness were recruited and followed up for up to a 4-week period prospectively; 1,012 of these were recruited from Singapore and 188 from Vietnam. Of these, 364 were dengue RT-PCR positive; 173 had dengue fever, 171 had dengue hemorrhagic fever, and 20 had dengue shock syndrome as final diagnosis. Using a C4.5 decision tree classifier for analysis of all clinical, haematological, and virological data, we obtained a diagnostic algorithm that differentiates dengue from non-dengue febrile illness with an accuracy of 84.7%. The algorithm can be used differently in different disease prevalence to yield clinically useful positive and negative predictive values. Furthermore, an algorithm using platelet count, crossover threshold value of a real-time RT-PCR for dengue viral RNA, and presence of pre-existing anti-dengue IgG antibodies in sequential order identified cases with sensitivity and specificity of 78.2% and 80.2%, respectively, that eventually developed thrombocytopenia of 50,000 platelet/mm(3) or less, a level previously shown to be associated with haemorrhage and shock in adults with dengue fever. CONCLUSION: This study shows a proof-of-concept that decision algorithms using simple clinical and haematological parameters can predict diagnosis and prognosis of dengue disease, a finding that could prove useful in disease management and surveillance.