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PURPOSE To examine the definitions of acute respiratory failure, the characteristics of recruited patients, and the criteria for intubation used in randomized trials. METHODS We searched MEDLINE for randomized trials of noninvasive respiratory support modalities in patients with de novo respiratory failure. We included trials from 1995 to 2017 that enrolled 40 or more patients and used intubation as an outcome. RESULTS We examined the reports of 53 trials that enrolled 7225 patients. There was wide variation in the use of variables for defining acute respiratory failure. Dyspnea was rarely measured and the increase in breathing effort was poorly defined. The characteristics of patients enrolled in trials changed over time and differed by the cause of respiratory failure. Intubation was poorly characterized. The criteria for intubation had more variables than the criteria for respiratory failure. CONCLUSIONS We identified deficiencies in the design and reporting of randomized trials, some of which can be remedied by investigators. We also found that patient characteristics differ by the type of respiratory failure. This knowledge can help clinician identify patients at the right moment to benefit from the tested interventions and investigators in developing criteria for enrollment in future trials. PURPOSE Drug-drug interactions (DDIs) may cause adverse outcomes in patients admitted to the Intensive Care Unit (ICU). Computerized decision support systems (CDSSs) may help prevent DDIs by timely showing relevant warning alerts, but knowledge on which DDIs are clinically relevant in the ICU setting is limited. Therefore, the purpose of this study was to identify DDIs relevant for the ICU. MATERIALS AND METHODS We conducted a modified Delphi procedure with a Dutch multidisciplinary expert panel consisting of intensivists and hospital pharmacists to assess the clinical relevance of DDIs for the ICU. The procedure consisted of two rounds, each included a questionnaire followed by a live consensus meeting. RESULTS In total the clinical relevance of 148 DDIs was assessed, of which agreement regarding the relevance was reached for 139 DDIs (94%). Of these 139 DDIs, 53 (38%) were considered not clinically relevant for the ICU setting. CONCLUSIONS A list of clinically relevant DDIs for the ICU setting was established on a national level. The clinical value of CDSSs for medication safety could be improved by focusing on the identified clinically relevant DDIs, thereby avoiding alert fatigue. PURPOSE We evaluated the feasibility and impact of PCT-guided antibiotic duration combined with an established antibiotic stewardship program (ASP) in a community hospital intensive care unit (ICU). METHODS We implemented daily PCT levels for ICU patients receiving antibiotics. Our protocol recommended stopping antibiotic therapy if PCT met an absolute or relative stopping threshold. We evaluated the adherence to stopping criteria within 48 h, antibiotic use [days of therapy (DOT) per 1000 patient-days (PD)], length of stay and ICU-mortality. We performed interrupted time series analysis to compare 24 months before and 12 months after implementation. RESULTS A total of 297 antibiotic courses were monitored with PCT in 217 patients. Protocol adherence was 34% (absolute threshold 39%, relative threshold 12%). Antibiotic use pre-PCT was 935 DOTs/1000 PDs and post-PCT was 817 DOTs/1000 PDs (RRadj 0.73, 95% CI 0.62 to 0.86). No statistically significant changes in clinical outcomes were noted. CONCLUSION In the context of an established ASP in a community hospital ICU, PCT monitoring was feasible and associated with an adjusted overall decrease of 27% in antibiotic use with no adverse impact on clinical outcomes. Incorporating PCT testing to guide antibiotic duration can be successful if integrated into workflow and paired with ASP guidance. Crown All rights reserved.In this research, efforts were put to demonstrate synergistic interactions between bioenergy generation and wastewater treatment. The extent of such synergistic effect was assessed against wastewater effluents released from the beverage industry through the operation of a membrane-less truncated conical (TC) microbial fuel cell (MFC). A graphite-based reactor was operated for five cycles in batch mode using beverage industry wastewater as an organic substrate. Maximum bioelectricity produced on the fifth operating cycle corresponded to a voltage of 338 mV and a power of 1.14 mW at 100 Ω. The MFC recorded a higher substrate degradation rate (0.84 kg of chemical oxygen demand [COD]/m3-day) accompanied by the development of an electroactive biofilm and polarization behavior (e.g., a reduction in internal resistance from 323 Ω to 197 Ω over five operation cycles). Bobcat339 cost Cyclic voltammetry showed a maximum performance of the biofilm during the fifth cycle (through its enrichment) as interpreted by oxidation and reduction currents of 2.48 and -2.21 mA, respectively. The performance of the proposed MFC was superior to other designs reported previously in both effluent treatment and bioenergy generation. A maximum treatment efficiency of 84.4% (in 385 h) was seen at an organic load (COD) of 3500 mg/L with the specific power yield (0.504 W/Kg of substrate (COD) removal) and volumetric power yield (15.03 W/m3). Our experimental studies support that the proposed system could be upscaled to realize the commercial operation. It is desirable to unravel the correlation between the geometric and electronic structures and the activity and further prepare high-performance electrocatalysts. Here in this paper, trimetallic Ru@Au-Pt core-shell nanoparticles were prepared by sequential ethanol reduction method, and further subject to characterization of X-ray diffraction, high angle annular dark field transmission electron microscopy, X-ray photoelectron spectroscopy and electrochemical CO stripping. Further analysis based on Williamson-Hall method revealed that the Au/Pt atomic ratio and shell thickness result in apparent variation of micro-strain and CO binding energy of Ru@AuPt nanoparticles, where the CO oxidation peak potential showed an inverted volcano-shape dependence on the microstrain of the metal nanoparticles while the catalytic activity towards electrooxidation of formic acid is linearly dependent on the micro-strain. The best Ru@Au-Pt catalyst delivers a specific activity of 4.14 mA cm-2, which is 52 times that of Pt/C, respectively. This study indicated that the microstrain and stacking fault of metal nanoparticles might be a good descriptor for the catalytic activity and may shed light the rational design, synthesis and surface engineering towards the high-performance electrocatalyst. The introduction of heteroatoms and functional groups in g-C3N4 generally has great advantages in enhancing the photocatalytic performance. In this work, the heteroatoms (Zn + C) and cyano (CN) group co-decorated porous g-C3N4 nanosheets (DCNNS) photocatalysts were successfully synthesized through direct calcination of the mixed urea and metal-organic frameworks. The optimized DCNNS displayed a maximum H2 evolution rate of ~484.09 μmol/h with a quantum efficiency of ~3.43% at 420 nm, and the photocatalytic U(VI) reduction activity was improved by ~6.09 times. The enhanced photocatalytic performance could be ascribed to following benefits (1) the modified DCNNS shared the two-dimensional layered structure of g-C3N4, and the massive nanopores in the nanosheets provided more reaction sites and diffusion channels for accelerated mass transfer; (2) the formation of cyano group greatly broadened the light response range and also acted as strong electron-withdrawing group for improving the carrier separation rate; (3) heteroatoms doping modulated the band gap, increased the electric conductivity, promoted the carrier separation and transport, and prolonged the electron lifetime to enhance photocatalytic performance. This work suggested that the heteroatoms and functional groups co-decoration could significantly improve the performance of g-C3N4-based photocatalysts and hold great potential to be further explored for energy and environmental applications. 3,3-Dithiodipropionic acid (DDA) as a potential corrosion inhibitor for Q235 steel in 0.5 M H2SO4 solution was examined. A variety of research approaches including electrochemical impedance spectroscopy (EIS), potentiodynamic polarization (PDP), scanning electron microscopy (SEM), atomic force microscopy (AFM), and computational techniques were employed. The toxicity and solubility of DAA were reasonably assessed. Its inhibition efficiency can reach approximately 93% when the optimal concentration is 5 mM. The results of PDP curves manifest that DDA is a mixed type corrosion inhibitor. EIS data indicate that the charge transfer resistance increases with increasing concentration of DDA. Gibbs free energy obtained from the Langmuir isotherm model suggests that DDA molecules hinder the acid attack mainly by chemisorption. Surface topography analysis strongly confirmed the electrochemical findings. Moreover, the simulation results based on density functional theory (DFT) calculation and molecular dynamics (MD) simulations supported the successful interfacial adsorption of DDA on Fe(1 1 0) surface. HYPOTHESIS Catalysts, chemical, gas, and bio- sensing devices fabricated from porous nanoparticle films show better performance and sensitivity than their bulk material counterparts because of their high specific surface area. Electrophoretic deposition (EPD) technique is a cost-effective, fast, versatile, and easy to perform method to fabricate porous nanoparticle films. However, conventional EPD is currently limited by the fact that the deposition rate decreases with time, resulting in an eventual plateau in the deposit yield. Here, we sought to overcome this limitation by establishing and leveraging the critical role of the particle's electrophoretic mobility in EPD kinetics. EXPERIMENTS To identify the impact of electrophoretic mobility on EPD yield we used alumina nanoparticles suspended in ethanol as a model system. Changes in particle mobility were monitored via changes in the effective pH (pHe) of the suspension during EPD. We also developed a new suspension replenish EPD approach that allows us to maintain near-constant particle mobility and particle concentration with time thereby increasing yield. FINDINGS We observed that in conventional EPD the particle mobility of the alumina nanoparticles decreased with time, resulting in a halting of deposition. Further, using the suspension replenish EPD, we observed a linear increase in the mass of the deposited film with time, overcoming the plateau limitation of conventional EPD. Deep neural networks (DNNs) have been very successful for supervised learning. However, their high generalization performance often comes with the high cost of annotating data manually. Collecting low-quality labeled dataset is relatively cheap, e.g., using web search engines, while DNNs tend to overfit to corrupted labels easily. In this paper, we propose a collaborative learning (co-learning) approach to improve the robustness and generalization performance of DNNs on datasets with corrupted labels. This is achieved by designing a deep network with two separate branches, coupled with a relabeling mechanism. Co-learning could safely recover the true labels of most mislabeled samples, not only preventing the model from overfitting the noise, but also exploiting useful information from all the samples. Although being very simple, the proposed algorithm is able to achieve high generalization performance even a large portion of the labels are corrupted. Experiments show that co-learning consistently outperforms existing state-of-the-art methods on three widely used benchmark datasets.
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