Objective Shewanella bacteremia is an uncommon but potentially fatal disease. Although hepatobiliary diseases have been proposed to be risk factors for various Shewanella infections, little is known about the features of Shewanella bacteremia in patients with hepatobiliary diseases. This study aims to characterize the presentation and risk factors of Shewanella bacteremia in patients with hepatobiliary diseases. Methods We retrospectively investigated the clinical features, microbiology and outcomes of patients with Shewanella bacteremia who were admitted to a tertiary medical center between January 2001 and December 2010. All isolates were confirmed to the species level using 16S rRNA sequencing analyses. The English language medical literature was searched for previously published reports. Results Fifty-nine cases of Shewanella bacteremia, including nine at the hospital, were identified, 28 (47.4%) of which involved underlying hepatobiliary diseases, representing an important risk factor. In 12 of the 28 cases, the infections involved the hepatobiliary system; with a tendency towards an Asian origin. In our case series of nine patients, Shewanella haliotis was isolated in five patients. The majority of our patients lived in coastal areas, consumed seafood regularly and developed bacteremia during the summer season. Conclusion It is recommended that the possibility for Shewanella infection be considered in patients with bacteremia and also underlying hepatobiliary diseases, particularly if patients present with hepatobiliary infections, a history of seafood, or development of the disease during the summer.

A 56-day treatment with ferric citrate effectively controlled hyperphosphatemia and was well tolerated in maintenance hemodialysis patients. There were also moderate increases in serum ferritin and transferrin saturation.

Type 2 diabetes mellitus (DM) is the most common single cause of end-stage renal disease. Albuminuria is the most commonly used marker to predict onset of diabetic nephropathy (DN) without enough sensitivity and specificity to detect early DN. This is the first study to identify urinary cyclophilin A (CypA) as a new biomarker for early DN.We recruited DM outpatients and healthy control subjects from January 2014 to December 2014. In this cross-sectional study, patients’ urine samples were collected to determine the expression of urinary CypA. We also treated mesangial (MES-13) and tubular (HK-2) cells with glucose or free radicals to observe the expression of secreted CypA in Western blot analysis.A total of 100 DN patients and 20 healthy control subjects were enrolled. All variables were matched. In univariate analysis, the concentration of urinary CypA correlated well with the progression of renal function. A significant increase in urinary CypA was noted in stage 2 DN and persisted in later stages. We could diagnose stage 2 DN using urinary CypA with a sensitivity of 90.0% and specificity of 72.7%. The area under curve was up to 0.85, indicating a good discriminatory power. In cellular models, MES-13 and HK-2 cells can both release CypA.Urinary CypA is a good biomarker for early DN detection in humans and it can be released from either mesangial or tubular cells. The underlying molecular mechanisms still need further clarification in cellular and animal studies.

Wafer maps can exhibit specific failure patterns that provide crucial details for assisting engineers in identifying the cause of wafer pattern failures. Conventional approaches of wafer map failure pattern recognition (WMFPR) and wafer map similarity ranking (WMSR) generally involve applying raw wafer map data (i.e., without performing feature extraction). However, because increasingly more sensor data are analyzed during semiconductor fabrication, currently used approaches can be inadequate in processing large-scale data sets. Therefore, a set of novel rotation-and scale-invariant features is proposed for obtaining a reduced representation of wafer maps. Such features are crucial when employing WMFPR and WMSR to analyze large-scale data sets. To validate the performance of the proposed system, the world's largest publicly accessible data set of wafer maps was built, comprising 811 457 real-world wafer maps. The experimental results show that the proposed features and overall system can process large-scale data sets effectively and efficiently, thereby meeting the requirements of current semiconductor fabrication.

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