Fever is a hallmark of inflammatory and infectious diseases. The febrile response is triggered by prostaglandin E 2 synthesis mediated by induced expression of the enzymes cyclooxygenase-2 (COX-2) and microsomal prostaglandin E synthase 1 (mPGES-1). The cellular source for pyrogenic PGE 2 remains a subject of debate; several hypotheses have been forwarded, including immune cells in the periphery and in the brain, as well as the brain endothelium. Here we generated mice with selective deletion of COX-2 and mPGES1 in brain endothelial cells. These mice displayed strongly attenuated febrile responses to peripheral immune challenge. In contrast, inflammationinduced hypoactivity was unaffected, demonstrating the physiological selectivity of the response to the targeted gene deletions. These findings demonstrate that PGE 2 synthesis in brain endothelial cells is critical for inflammation-induced fever.

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Sepsis is a major health concern with global estimates of 31.5 million cases per year. Case fatality rates are still unacceptably high, and early detection and treatment is vital since it significantly reduces mortality rates for this condition. Appropriately designed automated detection tools have the potential to reduce the morbidity and mortality of sepsis by providing early and accurate identification of patients who are at risk of developing sepsis. In this paper, we present “LiSep LSTM”; a Long Short-Term Memory neural network designed for early identification of septic shock. LSTM networks are typically well-suited for detecting long-term dependencies in time series data. LiSep LSTM was developed using the machine learning framework Keras with a Google TensorFlow back end. The model was trained with data from the Medical Information Mart for Intensive Care database which contains vital signs, laboratory data, and journal entries from approximately 59,000 ICU patients. We show that LiSep LSTM can outperform a less complex model, using the same features and targets, with an AUROC 0.8306 (95% confidence interval: 0.8236, 0.8376) and median offsets between prediction and septic shock onset up to 40 hours (interquartile range, 20 to 135 hours). Moreover, we discuss how our classifier performs at specific offsets before septic shock onset, and compare it with five state-of-the-art machine learning algorithms for early detection of sepsis.

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Acetaminophen is one of the world's most commonly used drugs to treat fever and pain, yet its mechanism of action has remained unclear. Here we tested the hypothesis that acetaminophen blocks fever through inhibition of cyclooxygenase-2 (Cox-2), by monitoring lipopolysaccharide induced fever in mice with genetic manipulations of enzymes in the prostaglandin cascade. We exploited the fact that lowered levels of a specific enzyme make the system more sensitive to any further inhibition of the same enzyme. Mice were immune challenged by an intraperitoneal injection of bacterial wall lipopolysaccharide and their body temperature recorded by telemetry. We found that mice heterozygous for Cox-2, but not for microsomal prostaglandin E synthase-1 (mPGES-1), displayed attenuated fever, indicating a rate limiting role of Cox-2. We then titrated a dose of acetaminophen that did not inhibit the lipopolysaccharide-induced fever in wild-type mice. However, when the same dose of acetaminophen was given to Cox-2 heterozygous mice, the febrile response to lipopolysaccharide was strongly attenuated, resulting in an almost normalized temperature curve, whereas no difference was seen between wild-type and heterozygous mPGES-1 mice.Furthermore, the fever to intracerebrally injected prostaglandin E 2 was unaffected by acetaminophen treatment. These findings reveal that acetaminophen, similar to aspirin and other non-steroidal anti-inflammatory drugs, is antipyretic by inhibiting cyclooxygenase-2, and not by inhibiting mPGES-1 or signaling cascades downstream of prostaglandin E 2 .