Prediction of adsorption performance of ZIF-67 for malachite green based on artificial neural network using L-BFGS algorithm.

Given the necessity and urgency in removing organic pollutants such as malachite green (MG) from the environment, it is vital to screen high-capacity adsorbents using artificial neural network (ANN) methods quickly and accurately. In this study, a series of ZIF-67 were synthesized, which adsorption properties for organic pollutants, especially MG, were systematically evaluated and determined as 241.720 mg g-1 (25 â„ƒ, 2 h). The adsorption process was more consistent with pseudo-second-order kinetics and Langmuir adsorption isotherm, which correlation coefficients were 0.995 and 0.997, respectively. The chemisorption mechanism was considered to be π-π stacking interaction between imidazole and aromatic ring. Then, a Python-based neural network model using the Limited-memory BFGS algorithm was constructed by collecting the crucial structural parameters of ZIF-67 and the experimental data of batch adsorption. The model, optimized extensively, outperformed similar Matlab-based ANN with a coefficient of determination of 0.9882 and mean square error of 0.0009 in predicting ZIF-67 adsorption of MG. Furthermore, the model demonstrated a good generalization ability in the predictive training of other organic pollutants. In brief, ANN was successfully separated from the Matlab platform, providing a robust framework for high-precision prediction of organic pollutants and guiding the synthesis of adsorbents.

Optimizing Temperature Setting for Decomposition Furnace Based on Attention Mechanism and Neural Networks.

The temperature setting for a decomposition furnace is of great importance for maintaining the normal operation of the furnace and other equipment in a cement plant and ensuring the output of high-quality cement products. Based on the principles of deep convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and attention mechanisms, we propose a CNN-LSTM-A model to optimize the temperature settings for a decomposition furnace. The proposed model combines the features selected by Least Absolute Shrinkage and Selection Operator (Lasso) with others suggested by domain experts as inputs, and uses CNN to mine spatial features, LSTM to extract time series information, and an attention mechanism to optimize weights. We deploy sensors to collect production measurements at a real-life cement factory for experimentation and investigate the impact of hyperparameter changes on the performance of the proposed model. Experimental results show that CNN-LSTM-A achieves a superior performance in terms of prediction accuracy over existing models such as the basic LSTM model, deep-convolution-based LSTM model, and attention-mechanism-based LSTM model. The proposed model has potentials for wide deployment in cement plants to automate and optimize the operation of decomposition furnaces.

Incidence and Outcome of Reintubation in the Postanesthesia Care Unit: A Single-Center, Retrospective, Observational Matched Cohort Study in China.

PURPOSE: To investigate the incidence and outcome of reintubation after planned extubation (RAP) in the postanesthesia care unit (PACU) in China. DESIGN: A single-center, retrospective, 1:2 matched cohort study following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement. METHODS: Among 121,965 patients in the PACU, 14 patients with RAP were included in this study from January 1, 2017 to December 31, 2019. PACU length of stay, postoperative length of stay in hospital, inpatient healthcare costs, and outcomes were compared between the RAP and the matched groups. FINDINGS: The incidence of RAP was 0.0115%. After propensity score matching, there were no statistically significant differences in age, sex, body mass index (BMI), elective/nonelective procedure, surgical classification, American Society of Anesthesiologists physical status, the duration of anesthesia, or the duration of surgical procedure between the two groups. PACU length of stay, postoperative length of stay in hospital, and inpatient healthcare costs significantly differed between the RAP group and the matched group (P < .01 for all). The percentage of patients with longer PACU length of stay in the RAP group was significantly higher than that in the matched group (92.86% vs 7.14%), with an odds ratio of 29.87 (95% confidence interval = 14.00-2,040.54, P < .001). CONCLUSIONS: Despite its low incidence, RAP in the PACU may be associated with life-threatening and severe complications with longer PACU length of stay, unexpected intensive care unit admission, longer hospitalization length, longer postoperative length of stay in hospital, and increased inpatient health costs. Appropriate timing of extubation and monitoring in the PACU can effectively prevent the occurrence of RAP and improve patient prognosis.

CLIP-Driven Prototype Network for Few-Shot Semantic Segmentation.

Recent research has shown that visual-text pretrained models perform well in traditional vision tasks. CLIP, as the most influential work, has garnered significant attention from researchers. Thanks to its excellent visual representation capabilities, many recent studies have used CLIP for pixel-level tasks. We explore the potential abilities of CLIP in the field of few-shot segmentation. The current mainstream approach is to utilize support and query features to generate class prototypes and then use the prototype features to match image features. We propose a new method that utilizes CLIP to extract text features for a specific class. These text features are then used as training samples to participate in the model's training process. The addition of text features enables model to extract features that contain richer semantic information, thus making it easier to capture potential class information. To better match the query image features, we also propose a new prototype generation method that incorporates multi-modal fusion features of text and images in the prototype generation process. Adaptive query prototypes were generated by combining foreground and background information from the images with the multi-modal support prototype, thereby allowing for a better matching of image features and improved segmentation accuracy. We provide a new perspective to the task of few-shot segmentation in multi-modal scenarios. Experiments demonstrate that our proposed method achieves excellent results on two common datasets, PASCAL-5i and COCO-20i.

Neural-Network-Based Adaptive Fault-Tolerant Cooperative Control of Heterogeneous Multiagent Systems With Multiple Faults and DoS Attacks.

In this article, the issue of adaptive fault-tolerant cooperative control is addressed for heterogeneous multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with actuator faults and sensor faults under denial-of-service (DoS) attacks. First, a unified control model with actuator faults and sensor faults is developed based on the dynamic models of the UAVs and UGVs. To handle the difficulty introduced by the nonlinear term, a neural-network-based switching-type observer is established to obtain the unmeasured state variables when DoS attacks are active. Then, the fault-tolerant cooperative control scheme is presented by utilizing an adaptive backstepping control algorithm under DoS attacks. According to Lyapunov stability theory and improved average dwell time method by integrating the duration and frequency characteristics of DoS attacks, the stability of the closed-loop system is proved. In addition, all vehicles can track their individual references, while the synchronized tracking errors among vehicles are uniformly ultimately bounded. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.

Automatic Analysis of Transverse Musculoskeletal Ultrasound Images Based on the Multi-Task Learning Model.

Musculoskeletal ultrasound imaging is an important basis for the early screening and accurate treatment of muscle disorders. It allows the observation of muscle status to screen for underlying neuromuscular diseases including myasthenia gravis, myotonic dystrophy, and ankylosing muscular dystrophy. Due to the complexity of skeletal muscle ultrasound image noise, it is a tedious and time-consuming process to analyze. Therefore, we proposed a multi-task learning-based approach to automatically segment and initially diagnose transverse musculoskeletal ultrasound images. The method implements muscle cross-sectional area (CSA) segmentation and abnormal muscle classification by constructing a multi-task model based on multi-scale fusion and attention mechanisms (MMA-Net). The model exploits the correlation between tasks by sharing a part of the shallow network and adding connections to exchange information in the deep network. The multi-scale feature fusion module and attention mechanism were added to MMA-Net to increase the receptive field and enhance the feature extraction ability. Experiments were conducted using a total of 1827 medial gastrocnemius ultrasound images from multiple subjects. Ten percent of the samples were randomly selected for testing, 10% as the validation set, and the remaining 80% as the training set. The results show that the proposed network structure and the added modules are effective. Compared with advanced single-task models and existing analysis methods, our method has a better performance at classification and segmentation. The mean Dice coefficients and IoU of muscle cross-sectional area segmentation were 96.74% and 94.10%, respectively. The accuracy and recall of abnormal muscle classification were 95.60% and 94.96%. The proposed method achieves convenient and accurate analysis of transverse musculoskeletal ultrasound images, which can assist physicians in the diagnosis and treatment of muscle diseases from multiple perspectives.

Secondary preventing effect of lung cancer in non-high-risk population: A retrospective investigation of opportunistic screening with low-dose computed tomography in Wuhan.

Background: Given the mortality benefit of low-dose computed tomography (LDCT) screening on high-risk populations, the retrospective investigation intended to identify the benefits of LDCT on lung cancer screening among the general demographic cohorts. Methods: We used an opportunistic screening with LDCT implemented during the pandemic in Wuhan to study the impact on subsequent thoracic surgeries, especially surgeries for lung cancer. Patients who received LDCT from October 1, 2019, to July 31, 2020, in three Triple-A accredited hospitals in Wuhan were included in the study. Relative week volumes of both surgeries before and after the chest LDCT screening were compared pairwise. The counts of surgeries for pulmonary nodules or masses, and corresponding pathological results among different gender and age groups were also compared. Result: The relative weekly volumes of thoracic surgery were significantly greater than those of stomach surgery after the opportunistic screening with LDCT. They were 33% (95% CI, 0.20-0.46; p<0. 001) higher than those of stomach surgery. For every 1,000 chest LDCT scans conducted in a given week, on average, 3.52(95% CI,0.56-6.49, p =0.03) thoracic surgeries were performed in the following week. After the implementation of opportunistic screening with LDCT, there was a higher percentage of young females with pulmonary nodule or mass (64.4% vs. 45.8%, p = 0.032). The fraction of lung cancer surgery in the treatment period was significantly greater than that in the control period (74.09% vs. 68.79%, p=0.007). There was a higher percentage of stage I lung cancer surgery in young and mid-age females than in the senior age group (64% vs. 53%, p= 0.05). Interpretation: Opportunistic screening with LDCT can advance the early diagnosis window of lung cancer in non-high-risk populations, especially young women who are easy to be ignored.

Distributed Event-Triggered Quantized Fault-Tolerant Control of Linear Multiagent Systems With External Disturbances and Parameter Uncertainties.

In this article, the issue of fault-tolerant leader-following consensus under a distributed dynamic event-triggered mechanism is addressed for linear multiagent systems (MASs) in the presence of unknown parameter uncertainties, external disturbances, and actuator faults, including loss of effectiveness and bias, in which the mechanism is with quantized state measurements. Due to the fact that information is transmitted via a bandwidth-limited communication network, a quantized control scheme with a uniform quantizer is introduced for leader-following consensus. In order to decrease the communication load and save the limited communication network resources, a distributed event-triggered mechanism is studied for leader-following consensus problem of linear MASs with quantized state measurements. In the presence of actuator faults, external disturbances, and unknown parameter uncertainties, an adaptive coupling gain for the controller is presented. Based on the Lyapunov function approach, the stability of the closed-loop system and the convergence of consensus errors are proved. Furthermore, the Zeno behavior is excluded for the triggering time sequences. Finally, simulation studies are given to verify the effectiveness of the proposed event-triggered fault-tolerant control scheme.

Adaptive Fault-Tolerant Formation Control of Heterogeneous Multi-Agent Systems under Directed Communication Topology.

This paper investigates the adaptive fault-tolerant formation control scheme for heterogeneous multi-agent systems consisting of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) with actuator faults, parameter uncertainties and external disturbances under directed communication topology. Firstly, the dynamic models of UAVs and USVs are introduced, and a unified heterogeneous multi-agent system model with actuator faults is established. Then, a distributed fault-tolerant formation controller is proposed for the unified model of UAVs and USVs in the XY plane by using adaptive updating laws and radial basis function neural network. After that, a decentralized formation-tracking controller is designed for the altitude control system of UAVs. Based on the Lyapunov stability theory, it can be proved that the formation errors and tracking errors are uniformly ultimately bounded which means that the expected time-varying formation is achieved. Finally, a simulation study is given to demonstrate the effectiveness of the proposed scheme.

Gaussian-Based Adaptive Fish Migration Optimization Applied to Optimization Localization Error of Mobile Sensor Networks.

Location information is the primary feature of wireless sensor networks, and it is more critical for Mobile Wireless Sensor Networks (MWSN) to monitor specific targets. How to improve the localization accuracy is a challenging problem for researchers. In this paper, the Gaussian probability distribution model is applied to randomize the individual during the migration of the Adaptive Fish Migration Optimization (AFMO) algorithm. The performance of the novel algorithm is verified by the CEC 2013 test suit, and the result is compared with other famous heuristic algorithms. Compared to other well-known heuristics, the new algorithm achieves the best results in almost 21 of all 28 test functions. In addition, the novel algorithm significantly reduces the localization error of MWSN, the simulation results show that the accuracy of the new algorithm is more than 5% higher than that of other heuristic algorithms in terms of mobile sensor node positioning, and more than 100% higher than that without the heuristic algorithm.

Perioperative risk factors for recovery room delirium after elective non-cardiovascular surgery under general anaesthesia.

BACKGROUND: Although postoperative delirium is a frequent complication of surgery, little is known about risk factors for delirium occurring in the post-anaesthesia care unit (PACU). The aim of this study was to determine pre- and intraoperative risk factors for the development of recovery room delirium (RRD) in patients undergoing elective non-cardiovascular surgery. METHODS: RRD was diagnosed according to the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). We collected perioperative data in 228 patients undergoing elective non-cardiovascular surgery under general anaesthesia and performed univariate and multivariate logistic regression to identify risk factors related to RRD. PACU and postoperative events were recorded to assess the outcome of RRD. RESULTS: Fifty-seven patients (25%) developed RRD. On multivariate analysis, maintenance of anaesthesia with inhalation anaesthetic agents (OR = 6.294, 95% CI 1.4-28.8, corrected p = 0.03), malignant primary disease (OR = 3.464, 95% CI = 1.396-8.592, corrected p = 0.018), American Society of Anaesthesiologists Physical Status (ASA-PS) III-V (OR = 3.389, 95% CI = 1.401-8.201, corrected p = 0.018), elevated serum total or direct bilirubin (OR = 2.535, 95% CI = 1.006-6.388, corrected p = 0.049), and invasive surgery (OR = 2.431, 95% CI = 1.103-5.357, corrected p = 0.035) were identified as independent risk factors for RRD. RRD was associated with higher healthcare costs (31,428 yuan [17,872-43,674] versus 16,555 yuan [12,618-27,788], corrected p = 0.002), a longer median hospital stay (17 days [12-23.5] versus 11 days [9-17], corrected p = 0.002), and a longer postoperative stay (11 days [7-15] versus 7 days [5-10], corrected p = 0.002]). CONCLUSIONS: Identifying patients at high odds for RRD preoperatively would enable the formation of more timely postoperative delirium management programmes.

Automatic Measurement of Pennation Angle from Ultrasound Images using Resnets.

In this study, an automatic pennation angle measuring approach based on deep learning is proposed. Firstly, the Local Radon Transform (LRT) is used to detect the superficial and deep aponeuroses on the ultrasound image. Secondly, a reference line are introduced between the deep and superficial aponeuroses to assist the detection of the orientation of muscle fibers. The Deep Residual Networks (Resnets) are used to judge the relative orientation of the reference line and muscle fibers. Then, reference line is revised until the line is parallel to the orientation of the muscle fibers. Finally, the pennation angle is obtained according to the direction of the detected aponeuroses and the muscle fibers. The angle detected by our proposed method differs by about 1° from the angle manually labeled. With a CPU, the average inference time for a single image of the muscle fibers with the proposed method is around 1.6 s, compared to 0.47 s for one of the image of a sequential image sequence. Experimental results show that the proposed method can achieve accurate and robust measurements of pennation angle.

COVID-19: a risk factor for fatal outcomes in patients with comorbid cardiovascular disease.

OBJECTIVES: To evaluate the fatal impact of COVID-19 on patients with comorbid cardiovascular disease (CVD). RESULTS: Overall, the 28-day mortality of patients with comorbid CVD was 3.25 times of that of patients without comorbid CVD (40.63% vs 12.50%, P=0.011). Clinic symptoms on admission were similar for the two groups. However, patients with comorbid CVD had higher levels of Interleukin-10 (22.22% vs 0%, P=0.034), procalcitonin (22.6% vs 3.13%, P<0.001), high-sensitivity troponin I (20 pg/mL vs 16.05 pg/mL, P=0.019), and lactic dehydrogenase (437 U/L vs 310 U/L, P=0.015). In addition, patients with comorbid CVD experienced a high incidence of acute respiratory distress syndrome (59.38% vs 15.63%, P<0.001), and required more invasive mechanical ventilation (40.63% vs 12.50%, P=0.011). Methylprednisolone was found to improve the survival of patients without comorbid CVD (p = 0.05). CONCLUSIONS: Comorbid CVD resulted in a higher mortality rate for COVID-19 patients. Acute respiratory distress syndrome was the primary reason of death for COVID-19 patients with comorbid CVD, followed by acute myocardial infarction. METHODS: This retrospective study used propensity score matching to divide 64 COVID-19 patients into two groups with and without comorbid CVD. Clinic symptoms, laboratory features, treatments, and 28-day mortality were compared between the two groups.

Continuous Analysis of Critical Incidents for 92,136 Postanesthesia Care Unit Patients of a Chinese University Hospital.

PURPOSE: To determine the spectrum of critical incidents in postanesthesia care unit (PACU) and the possible prediction and prevention of the worse scenario-associated critical incidents. DESIGN: A retrospective observational study. METHODS: The critical incidents in PACU comprising 92,136 patients were recorded. The incidents included the following disorders: delayed recovery, pain, bleeding, hypothermia, unplanned transfer to intensive care unit, shivering, agitation, nausea and vomiting, and respiratory or cardiovascular-related critical incidents. We then performed descriptive analyses and t test or χ2 test on the collected data. FINDINGS: A total of 1,760 critical incidents were recorded in 1,417 patients among 92,136 patients. Most critical incidents were associated with the patients after general anesthesia and general or gynecologic surgery. The most common critical incidents noted in the present study were pain, followed by cardiovascular-related and respiratory-related incidents. The average length of stay in PACU was 61.50 ± 44.40 minutes for the patients with critical incidents and 28.50 ± 19.40 minutes for the patients without critical incidents. CONCLUSIONS: Critical incidents lead to longer length of stay in the PACU. Regular inspection and immediate response for critical incidents in the PACU is essential for the maintenance of the quality of the immediate postoperative care.

Monitoring and Management of Home-Quarantined Patients With COVID-19 Using a WeChat-Based Telemedicine System: Retrospective Cohort Study.

BACKGROUND: Most patients with coronavirus disease (COVID-19) who show mild symptoms are sent home by physicians to recover. However, the condition of some of these patients becomes severe or critical as the disease progresses. OBJECTIVE: The aim of this study was to evaluate a telemedicine model that was developed to address the challenges of treating patients with progressive COVID-19 who are home-quarantined and shortages in the medical workforce. METHODS: A telemedicine system was developed to continuously monitor the progression of home-quarantined patients with COVID-19. The system was built based on a popular social media smartphone app called WeChat; the app was used to establish two-way communication between a multidisciplinary team consisting of 7 medical workers and 188 home-quarantined individuals (including 74 confirmed patients with COVID-19). The system helped patients self-assess their conditions and update the multidisciplinary team through a telemedicine form stored on a cloud service, based on which the multidisciplinary team made treatment decisions. We evaluated this telemedicine system via a single-center retrospective study conducted at Tongji Hospital in Wuhan, China, in January 2020. RESULTS: Among 188 individuals using the telemedicine system, 114 (60.6%) were not infected with COVID-19 and were dismissed. Of the 74 confirmed patients with COVID-19, 26 (35%) recovered during the study period and voluntarily stopped using the system. The remaining 48/76 confirmed patients with COVID-19 (63%) used the system until the end of the study, including 6 patients whose conditions progressed to severe or critical. These 6 patients were admitted to hospital and were stabilized (one received extracorporeal membrane oxygenation support for 17 days). All 74 patients with COVID-19 eventually recovered. Through a comparison of the monitored symptoms between hospitalized and nonhospitalized patients, we found prolonged persistence and deterioration of fever, dyspnea, lack of strength, and muscle soreness to be diagnostic of need for hospitalization. CONCLUSIONS: By continuously monitoring the changes in several key symptoms, the telemedicine system reduces the risks of delayed hospitalization due to disease progression for patients with COVID-19 quarantined at home. The system uses a set of scales for quarantine management assessment that enables patients to self-assess their conditions. The results are useful for medical staff to identify disease progression and, hence, make appropriate and timely treatment decisions. The system requires few staff to manage a large cohort of patients. In addition, the system can solicit help from recovered but self-quarantined medical workers to alleviate shortages in the medical workforce and free healthy medical workers to fight COVID-19 on the front line. Thus, it optimizes the usage of local medical resources and prevents cross-infections among medical workers and patients.

Adverse Respiratory Events Increase Post-anesthesia Care Unit Stay in China: A 2-year Retrospective Matched Cohort Study.

Despite growing attention to patients' safety worldwide, no data were available on the impact of adverse respiratory events (AREs) on post-anesthesia care and post-operation care in China. This study evaluated the occurrence of AREs, the impact of AREs on length of stay (LOS) in post-anesthesia care unit (PACU) and postoperative time in hospital, and PACU cost and inpatient healthcare costs. A retrospective, matched-cohort study was conducted by prospectively collecting the data of 159 AREs in PACU during 2016-2017 in an university hospital in China. Records were reviewed by pre-trained, qualified nurses and/or anesthesiologists. The incidence and the impact of AREs were analyzed. The LOS in PACU and postoperative time in hospital and the costs in PACU and inpatient healthcare costs were also obtained. Results showed that there were 253 AREs involving 156 patients. Hypoxia (n=141, 55.73%) and respiratory depression (n=70, 27.67%) were the most common AREs. Measurement data including body mass index (BMI) (22.85±4.36 vs. 22.32±3.83), duration of procedure (138.47±77.33 min vs. 137.44±72.33 min), duration of anesthesia (176.35±82.66 min vs. 174.61±78.08 min), LOS (16.53±10.65 days vs. 16.57±9.56 days), inpatient healthcare costs ($9465.57±9416.33 vs. $8166.51±5762.01), and postoperative LOS (11.26±8.77 days vs. 11.19±8.30 days) showed no significant differences between ARE and matched groups (P>0.05). Duration (81.65±54.79 min vs. 38.89±26.09 min) and costs ($31.99±17.80 vs. $18.72±8.39) in PACU were significantly different in ARE group from those in matched group (P<0.001). Proportion of patients with prolonged stay in PACU was significantly higher in ARE group than in matched group (18.59% vs. 1.28%), with an odds ratio (after matching) of 17.58 (95% CI=4.11 to 75.10; P<0.001). The AREs that occurred during the immediate postoperative period in PACU increased the incidence rate of prolonged stay, delayed the PACU stay, and increased the costs in PACU, resulting in the need of higher levels of postoperative care than anticipated, but the postoperative LOS and inpatient healthcare costs were unchanged.

Arsenic trioxide and angiotensin II have inhibitory effects on HERG protein expression: Evidence for the role of PML SUMOylation.

The human ether-a-go-go-related gene (HERG) channel is a novel target for the treatment of drug-induced long QT syndrome, which causes lethal cardiotoxicity. This study is designed to explore the possible role of PML SUMOylation and its associated nuclear bodies (NBs) in the regulation of HERG protein expression. Both arsenic trioxide (ATO) and angiotensin II (Ang II) were able to significantly reduce HERG protein expression, while also increasing PML SUMOylation and accelerating the formation of PML-NBs. Pre-exposure of cardiomyocytes to a SUMOylation chemical inhibitor, ginkgolic acid, or the silencing of UBC9 suppressed PML SUMOylation, subsequently preventing the downregulation of HERG induced by ATO or Ang II. Conversely, knockdown of RNF4 led to a remarkable increase in PML SUMOylation and the function of PML-NBs, further promoting ATO- or Ang II-induced HERG protein downregulation. Mechanistically, an increase in PML SUMOylation by ATO or Ang II dramatically enhanced the formation of PML and Pin1 complexes in PML-NBs, leading to the upregulation of TGF-ß1 protein, eventually inhibiting HERG expression through activation of protein kinase A. The present work uncovered a novel molecular mechanism underlying HERG protein expression and indicated that PML SUMOylation is a critical step in the development of drug-acquired arrhythmia.

Manipulating PML SUMOylation via Silencing UBC9 and RNF4 Regulates Cardiac Fibrosis.

The promyelocytic leukemia protein (PML) is essential in the assembly of dynamic subnuclear structures called PML nuclear bodies (PML-NBs), which are involved in regulating diverse cellular functions. However, the possibility of PML being involved in cardiac disease has not been examined. In mice undergoing transverse aortic constriction (TAC) and arsenic trioxide (ATO) injection, transforming growth factor ß1 (TGF-ß1) was upregulated along with dynamic alteration of PML SUMOylation. In cultured neonatal mouse cardiac fibroblasts (NMCFs), ATO, angiotensin II (Ang II), and fetal bovine serum (FBS) significantly triggered PML SUMOylation and the assembly of PML-NBs. Inhibition of SUMOylated PML by silencing UBC9, the unique SUMO E2-conjugating enzyme, reduced the development of cardiac fibrosis and partially improved cardiac function in TAC mice. In contrast, enhancing SUMOylated PML accumulation, by silencing RNF4, a poly-SUMO-specific E3 ubiquitin ligase, accelerated the induction of cardiac fibrosis and promoted cardiac function injury. PML colocalized with Pin1 (a positive regulator for TGF-ß1 mRNA expression in PML-NBs) and increased TGF-ß1 activity. These findings suggest that the UBC9/PML/RNF4 axis plays a critical role as an important SUMO pathway in cardiac fibrosis. Modulating the protein levels of the pathway provides an attractive therapeutic target for the treatment of cardiac fibrosis and heart failure.

Enhanced dewaterability of sewage sludge with zero-valent iron-activated persulfate oxidation system.

The potential benefits of zero-valent iron-activated persulfate (Na2S2O8) oxidation in enhanced dewaterability of sludge, along with the associated mechanisms were investigated in this study. The sludge dewaterability was evaluated in terms of specific resistance to filtration (SRF) and water content. Based on these indexes, it was observed that ZVI-S2O8(2) oxidation effectively improved sludge dewaterability. The optimal conditions to give preferable dewaterability were found when the molar ratio of ZVI/S2O8(2-) was 5:1 and pH value was 3.0. The most important mechanism was proposed to be the degradation of extracellular polymeric substances (EPS) incorporated in sludge flocs and rupture of microbial cells. Three-dimensional excitation-emission matrix fluorescence spectra revealed that the powerful SO4- and ·OH generated from ZVI-S2O8(2-) system destroyed the particular functional groups of fluorescing substances (aromatic protein-like and tryptophan protein-like substances), resulting in the release of bound water and the subsequent enhancement of dewaterability. Therefore, ZVI/S2O8(2-) oxidation is an alternative approach showing great potential to be applied in sludge treatment plants.

Downregulation of miR-21 is involved in direct actions of ursolic acid on the heart: implications for cardiac fibrosis and hypertrophy.

PURPOSE: Myocardial fibrosis contributes to cardiac remodeling and loss of cardiac function in myocardial infarction and heart failure. This study used in vitro and in vivo models to examine the effects of ursolic acid (UA) on myocardial fibrosis and to explore its potential mechanism. METHODS: Transverse aortic constriction (TAC) surgery was performed in mice to induce cardiac hypertrophy and fibrosis. UA was orally administered 1 week prior to TAC. Two weeks after TAC, myocardial pathology was detected using Masson's trichrome staining and transmission electron microscopy, and heart-to-body weight ratio was measured. For in vitro studies, cultured cardiac fibroblasts were treated with serum in the presence or absence of UA. The relative levels of miR-21 and p-ERK/ERK, collagen content and cell viability were measured. RESULTS: Ursolic acid attenuated pathological cardiac hypertrophy and myocardial fibrosis in vivo induced by TAC. Downregulation of miR-21 and p-ERK/ERK were observed in myocardial fibroblasts treated with UA in a dose-dependent manner compared with the control group both in vitro and in vivo. CONCLUSIONS: Our study demonstrates that UA can inhibit myocardial fibrosis both in vitro and in vivo, and the effects of UA on myocardial fibrosis may be due to the inhibition of miR-21/ERK signaling pathways.