Citrobacter freundii infection after acute necrotizing pancreatitis in a patient with a pancreatic pseudocyst: a case report

<p>Abstract</p> <p>Introduction</p> <p>Infections are the most frequent and severe complications of acute necrotizing pancreatitis with a mortality rate of up to 80 percent. Although experimental and clinical studies suggest that the microbiologic source of pancreatic infection could be enteric, information in this regard is controversial.</p> <p>Case presentation</p> <p>We describe a <it>Citrobacter freundii </it>isolation by endoscopy ultrasound fine needle aspiration in a 80-year-old Caucasian man with pancreatic pseudocyst after acute necrotizing pancreatitis.</p> <p>Conclusion</p> <p>Our case report confirms that this organism can be recovered in patients with a pancreatic pseudocyst. On-site cytology feedback was crucial to the successful outcome of this case as immediate interpretation of the fine needle aspiration sample directed the appropriate cultures and, ultimately, the curative therapy. To the best of our knowledge, this is the first reported case of isolated pancreatic <it>C. freundii </it>diagnosed by endoscopy ultrasound fine needle aspiration.</p

Processing two line element sets to facilitate re-entry prediction of spent rocket bodies from geostationary transfer orbit

Predicting the re-entry of space objects enables the risk they pose to the ground population to be managed. The more accurate the re-entry forecast, the more cost-efficient risk mitigation measures can be put in place. However, at present, the only publicly available ephemerides (two line element sets, TLEs) should not be used for accurate re-entry prediction directly. They may contain erroneous state vectors, which need to be filtered out. Also, the object’s physical parameters (ballistic and solar radiation pressure coefficients) need to be estimated to enable accurate propagation. These estimates are only valid between events that change object’s physical properties, e.g. collisions and fragmentations. Thus, these events need to be identified amongst the TLEs. This paper presents the TLE analysis methodology, which enables outlying TLEs and space events to be identified. It is then demonstrated how various TLE filtering stages improve the accuracy of the TLE-based re-entry prediction