Acute and acute-on-chronic kidney injury of patients with decompensated heart failure: impact on outcomes

BACKGROUND: Acute worsening of renal function, an independent risk factor for adverse outcomes in acute decompensated heart failure (ADHF), occurs as a consequence of new onset kidney injury (AKI) or acute deterioration of pre-existed chronic kidney disease (CKD) (acute-on-chronic kidney injury, ACKI). However, the possible difference in prognostic implication between AKI and ACKI has not been well established. METHODS: We studied all consecutive patients hospitalized with ADHF from 2003 through 2010 in Nanfang Hospital. We classified patients as with or without pre-existed CKD based on the mean estimated glomerular filtration rate (eGFR) over a six-month period before hospitalization. AKI and ACKI were defined by RIFLE criteria according to the increase of the index serum creatinine. RESULTS: A total of 1,005 patients were enrolled. The incidence of ACKI was higher than that of AKI. The proportion of patients with diuretic resistance was higher among patients with pre-existed CKD than among those without CKD (16.9% vs. 9.9%, P = 0.002). Compared with AKI, ACKI was associated with higher risk for in-hospital mortality, long hospital stay, and failure in renal function recovery. Pre-existed CKD and development of acute worsening of renal function during hospitalization were the independent risk factors for in-hospital death after adjustment by the other risk factors. The RIFLE classification predicted all-cause and cardiac mortality in both AKI and ACKI. CONCLUSIONS: Patients with ACKI were at greatest risk of adverse short-term outcomes in ADHF. Monitoring eGFR and identifying CKD should not be ignored in patients with cardiovascular disease

Occurrence Regularity of Methane Gas Molecules in Composite Nanopores: A Molecular Simulation Study

AbstractTo understand the occurrence regularity of methane gas molecules in composite nanopores, the effects of temperature, pressure, size of nanopore, and burial depth on the occurrence state of methane were studied theoretically by using the grand canonical Monte Carlo and molecular dynamic simulation methods. By comparing the results available in the literature, the reasons for the difference in the occurrence states of methane molecules in nanopores were analyzed, and a reasonable occurrence regularity of methane was proposed, which provides corresponding suggestions for the actual exploitation of shale gas. The results indicated that the methane gas molecules existed in nanopore only in the adsorption and transition states under different environmental conditions. They were preferentially adsorbed at the strong adsorption sites on the nanopore surface to form a stable adsorption layer. After the adsorption layer reached saturation, a transition layer with higher density than that of bulk methane was formed at the nanopore center. The total adsorption capacity of methane decreased gradually with an increase in the internal temperature of shale reservoirs and increased with an increase in nanopore size. In addition, the average amount of methane stored in the nanopore increased at a deeper burial depth. The occurrence state of methane under different pressure ranges was controlled under different action mechanisms. Under low pressure (P<20 MPa), the adsorption of methane molecules was controlled by the number of strong adsorption sites on the nanopore surface, where the density peak intensity of the adsorption layer increased with the pressure. However, under high pressure (P>20 MPa), the adsorption was controlled by the diffusion process of methane molecules in the organic matter layer, where both the adsorption and transition layers reached the saturation state, and excessive methane molecules diffused deeper into the kerogen layer. The approach to effectively improve the recovery efficiency was to inject water or carbon dioxide into the shale reservoir where the water or carbon dioxide molecules occupy strong adsorption positions than the methane molecules adsorbed originally under the competitive adsorption effect, and the adsorbed methane molecules were transformed to a free state

Well-dispersed Pd–Sn nanocatalyst anchored on TiO 2 nanosheets with enhanced activity and durability for ethanol electarooxidation

Abstract(#br)Novel Pd 1 -Sn x /TiO 2 nanosheets catalyst with higher activity and durability for ethanol oxidation (EOR) was obtained by NaBH 4 co-reduction method in direct ethanol fuel cells (DEFCs). The electrochemical performance tested under alkaline conditions illustrates that the prepared Pd 1 –Sn 0.6 /TiO 2 NSs catalyst presents outstanding activity (3381 mA mg Pd − 1 ) and excellent CO anti-poisoning ability for EOR. Meanwhile, the residual current density of Pd 1 –Sn 0.6 /TiO 2 NSs nanocatalyst (1207 mA mg Pd − 1 ) is 8.5 times of the Pd/C (JM) catalyst (142 mA mg Pd − 1 ) after the durability test of 5000 s for EOR. Additionally, the Pd 1 -Sn x /TiO 2 nanosheets show prominent electrocatalytic activity in EOR comparison with Pd/TiO 2 nanosheets and Pd–Sn nanocatalysts. Thus, Pd and Sn doped in TiO 2 nanosheets not only display excellent electrocatalytic, but also reduce the cost of Pd, which have some reference value for DEFCs

A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method

Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i) data discretization, (ii) feature extraction using the ReliefF algorithm, and (iii) feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart) dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques

Dual hydrophobic modifications toward anion exchange membranes with both high ion conductivity and excellent dimensional stability

Abstract(#br)Anion exchange membrane (AEMs) as a kind of important functional material are widely used in many fields including fuel cell, electrodialysis and water treatment. However, synthetic AEMs generally suffer a pernicious trade-off: high ion-conductive AEMs lack dimensional stability and vice versa. Herein we demonstrate a versatile strategy to prepare the AEMs with both high ion conductivity and excellent dimensional stability ( i.e. , low swelling ratio) via hydrophobic crosslinking and introducing hydrophobic chains. The hydrophobic length of crosslinkers has great influence on construction of highly efficient ion channels in the AEMs. Amazingly, the hydrophilic poly (phenylene oxide) (PPO) AEM crosslinked by 1,8-diaminooctane has the highest hydroxide conductivity that is further improved to 157.2 mS cm −1 (10% increases) with a low swelling ratio of 12.9% at 80 °C by introducing hydrophobic PPO backbone. This AEM not only overcomes the trade-off between the ion conductivity and the dimensional stability of crosslinked AEMs, but also breaks the upper bound between the ion conductivity and the water uptake. The newly developed strategy of hydrophobic dual-modifications promises to be an effective approach to develop the high-performance AEMs

A Fully Flexible Potential Model for Carbon Dioxide

A fully flexible potential model for carbon dioxide has been developed to predict the vapor-liquid coexistence properties using the NVT-Gibbs ensemble Monte Carlo technique (GEMC). The average absolute deviation between our simulation and the literature experimental data for saturated liquid and vapor densities is 0.3% and 2.0%, respectively. Compared with the experimental data, our calculated results of critical properties (7.39 Mpa, 304.04 K, and 0.4679 g.cm(-3)) are acceptable and are better than those from the resealing the potential parameters of elementary physical model (EPM2). The agreement of our simulated densities of supercritical carbon dioxide with the experimental data is acceptable in a wide range of pressure and temperature. The radial distribution function estimated at the supercritical conditions suggests that the carbon dioxide is a nonlinear molecule with the C=O bond length of 0.117 nm and the O=C=O bond angle of 176.4 degrees, which are consistent with Car-Parrinello molecular-dynamics (CPMD), whereas the EPM2 model shows large deviation at supercritical state. The predicted self-diffusion coefficients are in agreement with the experiments.National Natural Science Foundation of China [50573063]; Program for New Century Excellent Talents in University of the State Ministry of Education [NCET-05-0566]; Specialized Research Fund for the Doctoral Program of Higher Education of China [2005038401

HRLAIF: Improvements in Helpfulness and Harmlessness in Open-domain Reinforcement Learning From AI Feedback

Reinforcement Learning from AI Feedback (RLAIF) has the advantages of shorter annotation cycles and lower costs over Reinforcement Learning from Human Feedback (RLHF), making it highly efficient during the rapid strategy iteration periods of large language model (LLM) training. Using ChatGPT as a labeler to provide feedback on open-domain prompts in RLAIF training, we observe an increase in human evaluators' preference win ratio for model responses, but a decrease in evaluators' satisfaction rate. Analysis suggests that the decrease in satisfaction rate is mainly due to some responses becoming less helpful, particularly in terms of correctness and truthfulness, highlighting practical limitations of basic RLAIF. In this paper, we propose Hybrid Reinforcement Learning from AI Feedback (HRLAIF). This method enhances the accuracy of AI annotations for responses, making the model's helpfulness more robust in training process. Additionally, it employs AI for Red Teaming, further improving the model's harmlessness. Human evaluation results show that HRLAIF inherits the ability of RLAIF to enhance human preference for outcomes at a low cost while also improving the satisfaction rate of responses. Compared to the policy model before Reinforcement Learning (RL), it achieves an increase of 2.08\% in satisfaction rate, effectively addressing the issue of a decrease of 4.58\% in satisfaction rate after basic RLAIF.Comment: 18 pages, 7 figure