In this study, we describe the electrolyte gating and doping effects of transistors based on conducting polymer nanowire electrode junction arrays in buffered aqueous media. Conducting polymer nanowires including polyaniline, polypyrrole, and poly(ethylenedioxythiophene) were investigated. In the presence of a positive gate bias, the device exhibits a large on/off current ratio of 978 for polyaniline nanowire-based transistors; these values vary according to the acidity of the gate medium. We attribute these efficient electrolyte gating and doping effects to the electrochemically fabricated nanostructures of conducting polymer nanowires. This study demonstrates that two-terminal devices can be easily converted into three-terminal transistors by simply immersing the device into an electrolyte solution along with a gate electrode. Here, the field-induced modulation can be applied for signal amplification to enhance the device performance.

No abstract

Background Neurosurgical intensive care unit patients are at high risk for delirium. A risk prediction model could help the staff screen for patients at high risk for delirium. On the basis of this risk, preventive measures could be taken to reduce the undesired effects of delirium. Objectives To establish a delirium prediction model for neurosurgical intensive care unit patients and to verify the sensitivity and specificity of this model. Design A prospective, observational, single‐centre study. Methods Data were collected from a total of 310 patients admitted to the neurosurgery intensive care unit between January 2017 and February 2018. A risk factor prediction model was then created using multivariate logistic regression. Further data were collected from another 60 patients between March 2018 and June 2018 to validate the model. Results The model consisted of six predictors, namely, cognitive dysfunction on admission, fever, hypoalbuminaemia, abnormal liver function, sedative use four or more times, and physical restraint. The area under the curve of the model was 0.80, with sensitivity and specificity of 0.68 and 0.83, respectively. Conclusions This study established a delirium prediction model for neurosurgical intensive care unit patients, which we believe would help focused prevention of delirium in intensive care unit patients.

No abstract

No abstract