Hexagonal molybdenum diselenide (MoSe2) multilayers were grown by chemical vapor deposition (CVD). A relatively high pressure (>760 Torr) was used during the CVD growth to achieve multilayers by creating multiple nuclei based on the two-dimensional crystal growth model. Our CVD-grown multilayer MoSe2 thin-film transistors (TFTs) show p-type-dominant ambipolar behaviors, which are attributed to the formation of Se vacancies generated at the decomposition temperature (650 °C) after the CVD growth for 10 min. Our MoSe2 TFT with a reasonably high field-effect mobility (10 cm2/V · s) exhibits a high photoresponsivity (93.7 A/W) and a fast photoresponse time (τrise ~ 0.4 s) under the illumination of light, which demonstrates the practical feasibility of multilayer MoSe2 TFTs for photodetector applications.

No abstract

Background and objectivesHigh BP variability may cause AKI because of inappropriate kidney perfusion. This study aimed to investigate the association between intraoperative BP variability and postoperative AKI in patients who underwent noncardiac surgery.Design, setting, participants, & measurementsWe performed a cohort study of adults undergoing noncardiac surgery in hospitals in South Korea. We studied three cohorts using the following recording windows for intraoperative BP: discovery cohort, 1-minute intervals; first validation cohort, 5-minute intervals; and second validation cohort, 2-second intervals. We calculated four variability parameters (SD, coefficient of variation, variation independent of mean, and average real variability) based on the measured mean arterial pressure values. The primary outcomes were postoperative AKI (defined by the Kidney Disease Improving Global Outcomes serum creatinine cutoffs) and critical AKI (consisting of stage 2 or higher AKI and post-AKI death or dialysis within 90 days).ResultsIn the three cohorts, 45,520, 29,704, and 7435 patients were analyzed, each with 2230 (443 critical), 1552 (444 critical), and 300 (91 critical) postoperative AKI events, respectively. In the discovery cohort, all variability parameters were significantly associated with risk of AKI, even after adjusting for intraoperative hypotension. For example, average real variability was associated with higher risks of postoperative AKI (adjusted odds ratio, 1.13 per 1 SD increment; 95% CI, 1.07 to 1.19) and critical AKI (adjusted odds ratio, 1.13 per 1 SD increment; 95% CI, 1.02 to 1.26). Associations were evident predominantly among patients who also experienced intraoperative hypotension. In the validation analysis with 5-minute-interval BP records, all four variability parameters were associated with the risk of postoperative AKI or critical AKI. In the validation cohort with 2-second-interval BP records, average real variability was the only significant variability parameter.ConclusionsHigher intraoperative BP variability is associated with higher risks of postoperative AKI after noncardiac surgery, independent of hypotension and other clinical characteristics.

Background The discrepancy between predicted effect-site concentration and measured bispectral index is problematic during intravenous anesthesia with target-controlled infusion of propofol and remifentanil. We hypothesized that bispectral index during total intravenous anesthesia would be more accurately predicted by a deep learning approach. Methods Long short-term memory and the feed-forward neural network were sequenced to simulate the pharmacokinetic and pharmacodynamic parts of an empirical model, respectively, to predict intraoperative bispectral index during combined use of propofol and remifentanil. Inputs of long short-term memory were infusion histories of propofol and remifentanil, which were retrieved from target-controlled infusion pumps for 1,800 s at 10-s intervals. Inputs of the feed-forward network were the outputs of long short-term memory and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the bispectral index. The performance of bispectral index prediction was compared between the deep learning model and previously reported response surface model. Results The model hyperparameters comprised 8 memory cells in the long short-term memory layer and 16 nodes in the hidden layer of the feed-forward network. The model training and testing were performed with separate data sets of 131 and 100 cases. The concordance correlation coefficient (95% CI) were 0.561 (0.560 to 0.562) in the deep learning model, which was significantly larger than that in the response surface model (0.265 [0.263 to 0.266], P < 0.001). Conclusions The deep learning model–predicted bispectral index during target-controlled infusion of propofol and remifentanil more accurately compared to the traditional model. The deep learning approach in anesthetic pharmacology seems promising because of its excellent performance and extensibility.

No abstract

During central venous catheterization via the right IJV, landmark guidance was comparable with ECG guidance with regard to CVC tip positioning in the superior vena cava.

BACKGROUND AND PURPOSE:We analyzed the angiographic architecture of intraosseous dural arteriovenous fistulas (DAVFs) and evaluated the use of transvenous embolization for curative treatment.

Centers with higher case volume (>50 liver transplantations/year) had better outcomes after living donor liver transplantation, including in-hospital mortality and long-term mortality, compared to centers with lower case volume (≤50 liver transplantations/year).