Insight into the bimetallic structure sensibility of catalytic nitrate reduction over Pd-Cu nanocrystals.

Catalytic reduction of nitrate over bimetallic catalysts has emerged as a technology for sustainable treatment of nitrate-containing groundwater. However, the structure of bimetallic has been much less investigated for catalyst optimization. Herein, two main types of Pd-Cu bimetallic nanocrystal structures, heterostructure and intermetallic, were prepared and characterized using high-resolution transmission electron microscopy (HRTEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The results show that two individual Pd and Cu nanocrystals with a mixed interface exist in the heterostructure nanocrystals, while Pd and Cu atoms are uniformly distributed across the intermetallic Pd-Cu nanocrystals. The catalytic nitrate reduction experiments were carried out in a semibatch reactor under constant hydrogen flow. The nitrate conversion rate of the heterostructure Pd-Cu nanocrystals supported on α-Al2O3, γ-Al2O3, SBA-15, and XC-72R exhibited 3.82-, 6.76-, 4.28-, 2.44-fold enhancements relative to the intermetallic nanocrystals, and the nitrogen and nitrite were the main products for the heterostructure and intermetallic Pd-Cu nanocrystals, respectively. This indicates that the catalytic nitrate reduction over Pd-Cu catalyst is sensitive to the bimetallic structures of the catalysts, and heterostructure bimetallic nanocrystals exhibit better catalytic performances on both the activity and selectivity, which may provide new insights into the design and optimization of catalysts to improve catalytic activity and selectivity for nitrate reduction in water.

Effects of a closed-loop system against SARS-CoV-2 at the Beijing 2022 Olympic Winter Games: a descriptive and modeling study.

BACKGROUND: To assess the efficacy of the epidemic prevention measures of the "closed-loop" system adopted by the Beijing 2022 Olympic Winter Games (BOWG). METHODS: We retrospectively collected and analyzed information, including age, sex, nationality, vaccination status, date of diagnosis, and date of entry, from 280 SARS-CoV-2-positive individuals identified during the BOWG. A susceptibility-exposed-infectious-remove model was employed to evaluate the effectiveness of epidemic prevention strategies on controlling the spread of SARS-CoV-2 under different scenarios during the BOWG. RESULTS: Regarding SARS-CoV-2-positive cases, 97.9% were imported, and 96.4% were asymptomatic. The median age was 37 years (range: 29-47 years), and 73.9% were male, with the majority of cases being broadcasters and European attendees. Regarding vaccination status, 93.5% were fully vaccinated, and six cases were considered to have been infected in the closed-loop system during the BOWG. Assuming that the BOWG adopted a semi-closed-loop management system, the cumulative number of confirmed cases would be 1,137 for quick quarantine measures (3 d later) implemented and 5,530 for delayed quarantine measures (9 d later) implemented. This modeling revealed that stringent pandemic prevention measures and closed-loop management effectively controlled the spread of SARS-CoV-2 during the BOWG. CONCLUSION: Imported cases are considered the main risk factor for SARS-CoV-2 transmission during mass gatherings, but a comprehensive closed-loop system could minimize transmission among attendees and general personnel.

Prevalence and Risk Factors of Mitral, Tricuspid, and Aortic Regurgitation: A Population-Based Study from Rural Northeast China.

The population-based studies on the epidemiologic features of valvular regurgitation in Northeast China are scarce. We aim to estimate the prevalence and risk factors of mitral regurgitation (MR), tricuspid regurgitation (TR), and aortic regurgitation (AR) in a general population from rural Northeast China. Valvular regurgitation was assessed by color flow Doppler echocardiography in a population-based survey of 11,278 participants aged ≥35 years in rural areas of Liaoning Province during 2012 to 2013. The prevalence of mild or greater MR and TR were 1.6% and 1.5%, respectively. Trace or greater AR was present in 4.1% of the participants. In the multivariable regression model, older age, left atrial dimension, low left ventricular (LV) ejection fraction, and fasting plasma glucose were associated with higher risk of MR in men, whereas only older age and left atrial dimension increased the risk in women. Body mass index was found to be a protective factor for MR in women (odds ratio 0.847, 95% confidence interval 0.741 to 0.969). TR was independently associated with age, heart rate, low LV ejection fraction, current drinking status, and high-density lipoprotein cholesterol. The risk for AR significantly increased with age in both genders. LV mass index and aortic dimension increased the risk of AR in males, and females with higher LV mass index and high-density lipoprotein cholesterol had an increased risk for AR. In both genders, systolic blood pressure presented as a risk factor for AR, while diastolic blood pressure as a protective factor. In this large Chinese population-based study, we found remarkably low prevalence of valvular regurgitation, adding evidence for estimating disease burden and making policy strategies in Northeast China.

Predictive value of liver fibrosis scores in cardiovascular diseases among hypertensive population.

PURPOSE: To explore the predictive value of liver fibrosis scores [fibrosis-4, AST/platelet ratio index, BAAT score (BMI Age ALT TG), and BARD score (BMI AST/ALT Ratio Diabetes)] for the risk of cardiovascular disease (CVD) in a hypertensive population. METHODS: A total of 4164 hypertensive participants without history of CVD were enrolled in the follow-up. Four liver fibrosis scores (LFSs) were used, including the fibrosis-4 (FIB-4), APRI, BAAT score, and BARD score. The endpoint was CVD incidence which was defined as stroke or coronary heart disease (CHD) during the follow-up period. Cox regression analyses were used to calculate hazard ratios between LFSs and CVD. Kaplan-Meier curve was used to show the probability of CVD in different levels of LFSs. Restricted cubic spline further explored whether the relationship between LFSs and CVD was linear. Finally, we assessed the discriminatory ability of each LFS for CVD was assessed using C -statistics, net reclassification index (NRI), and integrated discrimination improvement (IDI). RESULTS: During a median follow-up time of 4.66 years, 282 hypertensive participants had CVD. Kaplan-Meier curve showed that four LFSs were associated with CVD and high levels of LFSs significantly increase the probability of CVD in hypertensive population. In the multivariate Cox regression analysis, the adjusted hazard ratios for four LFSs were 3.13 in FIB-4, 1.66 in APRI, 1.47 in BAAT score, and 1.36 in BARD score. Moreover, after adding LFSs to original risk prediction model, we find that all four new models have higher C -statistics of CVD than the traditional model. Furthermore, the results of both NRI and IDI were positive, indicating that LFSs enhanced the effect on the prediction of CVD. CONCLUSIONS: Our study showed that LFSs were associated with CVD in hypertensive populations in northeastern China. Furthermore, it suggested that LFSs could be a new tool for identifying patients at high risk of primary CVD in a hypertensive population.

Exploring underlying mechanism of artesunate in treatment of acute myeloid leukemia using network pharmacology and molecular docking.

BACKGROUND: Acute myeloid leukemia (AML) is a highly heterogeneous hematological cancer. The current diagnosis and therapy model of AML has gradually shifted to personalization and accuracy. Artesunate, a member of the artemisinin family, has anti-tumor impacts on AML. This research uses network pharmacology and molecular docking to anticipate artesunate potential mechanisms of action in the therapy of AML. METHODS: Screening the action targets of artesunate through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), PubChem, and Swiss Target Prediction databases; The databases of Online Mendelian Inheritance in Man (OMIM), Disgenet, GeneCards, and Drugbank were utilized to identify target genes of AML, and an effective target of artesunate for AML treatment was obtained through cross-analysis. Protein-protein interaction (PPI) networks are built on the Cytoscape platform. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on the relevant targets using R software. Finally, using molecular docking technology and Pymol, we performed verification of the effects of active components and essential targets. RESULTS: Artesunate 30 effective targets for treating AML include CASP3, EGFR, MAPK1, and STAT3, four targeted genes that may have a crucial function in disease management. The virus infection-related pathway (HeptatisB (HBV), Human papillomavirus (HPV), Epstein-Barr virus (EBV) infection and etc.), FoxO, viral carcinogenesis, and proteoglycans in cancer signaling pathways have all been hypothesized to be involved in the action mechanism of GO, which is enriched in 2044 biological processes, 125 molecular functions, 209 cellular components, and 106 KEGG pathways. Molecular docking findings revealed that artesunate was critically important in the therapy of AML due to its high affinity for the four primary disease targets. Molecular docking with a low binding energy yields helpful information for developing medicines against AML. CONCLUSIONS: Consequently, artesunate may play a role in multi-targeted, multi-signaling pathways in treating AML, suggesting that artesunate may have therapeutic potential for AML.

The association between liver fibrosis scores and chronic kidney disease.

Purpose: This study aimed to clarify the relationship between liver fibrosis scores (Fibrosis-4, BARD score, and BAAT score) and chronic kidney disease (CKD). Methods: We collected a range of data from 11,503 subjects (5,326 men and 6,177 women) from the rural regions of Northeastern China. Three liver fibrosis scores (LFSs) including fibrosis-4 (FIB-4), BARD score, and BAAT score were adopted. A logistic regression analysis was used to calculate odds ratios and the 95% confidence interval. A subgroup analysis showed the association between LFSs and CKD under different stratifications. Restricted cubic spline could further explore whether there is a linear relationship between LFSs and CKD. Finally, we used C-statistics, Net Reclassification Index (NRI), and Integrated Discrimination Improvement (IDI) to assess the effect of each LFS on CKD. Results: Through the baseline characteristics, we observed that LFSs were higher in the CKD population than in non-CKD. The proportion of participants with CKD also increased with LFSs. In a multivariate logistic regression analysis, the ORs of CKD were 6.71 (4.45-10.13) in FIB-4, 1.88 (1.29-2.75) in the BAAT score, and 1.72 (1.28-2.31) in the BARD score by comparing the high level with the low level in each LFSs. Moreover, after adding LFSs to the original risk prediction model, which consisted of age, sex, drinking, smoking, diabetes, low-density lipoprotein cholesterol, total cholesterol, triglycerides, and mean waist circumference, we found the new models have higher C-statistics. Furthermore, NRI and IDI both indicate LFSs had a positive effect on the model. Conclusions: Our study showed that LFSs are associated with CKD among middle-aged populations in rural areas of northeastern China.

Predictive Value of the Age, Creatinine, and Ejection Fraction (ACEF) Score in Cardiovascular Disease among Middle-Aged Population.

PURPOSE: To explore the predictive value of ACEF scores for identifying the risk of cardiovascular disease (CVD) in the general population. METHODS: A total of 8613 participants without a history of CVD were enrolled in the follow-up. The endpoint was CVD incidence, defined as stroke or coronary heart disease (CHD) diagnosed during the follow-up period. Cox regression analyses were used to calculate hazard ratios (HRs) with respect to the age, creatinine, and ejection fraction (ACEF) scores and CVD. A Kaplan-Meier curve was used to analyze the probability of CVD in different quartiles of ACEF. Restricted cubic spline was used to further explore whether the relationship between ACEF and CVD was linear. Finally, we assessed the discriminatory ability of ACEF for CVD using C-statistics, net reclassification index, and integrated discrimination improvement (IDI). RESULTS: During a median follow-up period of 4.66 years, 388 participants were diagnosed with CVD. The Kaplan-Meier curve showed that ACEF was associated with CVD, and participants with high ACEF scores were significantly more likely to be diagnosed with CVD compared to participants with low ACEF scores in the general population. In the multivariate Cox regression analysis, the adjusted HRs for four quartiles of ACEF were as follows: the first quartile was used as a reference; the second quartile: HR = 2.33; the third quartile: HR = 4.81; the fourth quartile: HR = 8.00. Moreover, after adding ACEF to the original risk prediction model, we observed that new models had higher C-statistic values of CVD than the traditional model. Furthermore, the results of both NRI and IDI were positive, indicating that ACEF enhanced the prediction of CVD. CONCLUSIONS: Our study showed that the ACEF score was associated with CVD in the general population in northeastern China. Furthermore, ACEF could be a new tool for identifying patients at high risk of primary CVD in the general population.

Impact of liver fibrosis score on the incidence of stroke: A cohort study.

PURPOSE: The purpose was to explore the value of liver fibrosis scores (fibrosis-4, BAAT score and BARD score) for incidence risk of stroke in a cohort study. METHODS: A total of 9088 participants without stroke history enrolled the follow-up. Three liver fibrosis scores (LFSs) including FIB-4, BARD score and BAAT score were adopted. The end point was stroke. Cox regression analysis was used to calculate hazard ratios and 95% confidence interval. Kaplan-Meier curve was used to show the probability of stoke in different levels of LFSs. Subgroup analysis showed the association between LFSs and stroke under different stratification. Restricted cubic spline could further explore whether there is a linear relationship between LFSs and stroke. Finally, we used C-statistics, Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI) to assess the discriminatory power of each LFS for stroke. RESULTS: During a median follow-up time of 4.66 years, 272 participants had a stroke. Through the baseline characteristics, we observed that the stroke incidence population tends to be male and older. It was shown by Kaplan-Meier that three LFSs were associated with stroke and high levels of LFSs significantly increase the probability of stroke. In the univariate Cox regression analysis, the HR of stroke risk was 6.04 (4.14-8.18) in FIB-4, 2.10 (1.45-3.04) in BAAT score and 1.81 (1.38-2.38) in BARD score by comparing the high level with the low level at each LFSs. The adjusted HRs for three LFSs were 2.05 (1.33-3.15) in FIB-4, 1.61 (1.10-2.35) in BAAT score and 1.54 (1.17-2.04) in BARD score by comparing the high group with low group. We found that multivariable-adjusted HRs of three LFSs still increased for stroke when stratified by various factors in subgroup analysis. Moreover, after adding LFSs to original risk prediction model which consist of age, sex, drinking, smoking, hypertension, diabetes, low-density lipoprotein cholesterol, total cholesterol and triglycerides, we found that new models have higher C-statistics of stroke. Furthermore, we calculated Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI) to show the ability of LFSs to predict stroke. CONCLUSIONS: Our study showed that three LFSs were associated with stroke amongst middle-aged populations in rural areas of Northeast China. Furthermore, it suggests that LFSs can be used as a risk stratification tool to predict stroke.

Ferulic Acid Combined With Bone Marrow Mesenchymal Stem Cells Attenuates the Activation of Hepatic Stellate Cells and Alleviates Liver Fibrosis.

Bone marrow mesenchymal stem cells (BMSCs) can effectively alleviate liver fibrosis, but the efficacy of cell therapy alone is insufficient. In recent years, a combination of traditional Chinese medicine (TCM) and cell therapy has been increasingly used to treat diseases in clinical trials. Ferulic acid (FA) is highly effective in treating liver fibrosis, and a combination of cells and drugs is being tested in clinical trials. Therefore, we combined BMSCs and Ferulic acid to treat CCl4-induced fibrosis and determine whether this combination was more effective than single treatment. We used BMSCs and FA to treat CCl4-induced fibrosis in rat models, observed their therapeutic effects, and investigated the specific mechanism of this combination therapy in liver fibrosis. We created a BMSC/hepatic stellate cell (HSC) coculture system and used FA to treat activated HSCs to verify the specific mechanism. Then, we used cytochalasin D and angiotensin II to investigate whether BMSCs and FA inactivate HSCs through cytoskeletal rearrangement. MiR-19b-3p was enriched in BMSCs and targeted TGF-ß receptor II (TGF-ßR2). We separately transfected miR-19b-3p into HSCs and BMSCs and detected hepatic stellate cell activation. We found that the expression of the profibrotic markers α-SMA and COL1-A1 was significantly decreased in the combination group of rats. α-SMA and COL1-A1 levels were also significantly decreased in the HSCs with the combination treatment. Cytoskeletal rearrangement of HSCs was inhibited in the combination group, and RhoA/ROCK pathway gene expression was decreased. Following angiotensin II treatment, COL1-A1 and α-SMA expression increased, while with cytochalasin D treatment, profibrotic gene expression decreased in HSCs. The expression of COL1-A1, α-SMA and RhoA/ROCK pathway genes was decreased in the activated HSCs treated with a miR-19b-3p mimic, indicating that miR-19b-3p inactivated HSCs by suppressing RhoA/ROCK signalling. In contrast, profibrotic gene expression was significantly decreased in the BMSCs treated with the miR-19b-3p mimic and FA or a miR-19b-3p inhibitor and FA compared with the BMSCs treated with the miR-19b-3p mimic alone. In conclusion, the combination therapy had better effects than FA or BMSCs alone. BMSC and FA treatment attenuated HSC activation and liver fibrosis by inhibiting cytoskeletal rearrangement and delivering miR-19b-3p to activated HSCs, inactivating RhoA/ROCK signalling. FA-based combination therapy showed better inhibitory effects on HSC activation.

Road Profile Estimation Using a 3D Sensor and Intelligent Vehicle.

Autonomous vehicles can achieve accurate localization and real-time road information perception using sensors such as global navigation satellite systems (GNSSs), light detection and ranging (LiDAR), and inertial measurement units (IMUs). With road information, vehicles can navigate autonomously to a given position without traffic accidents. However, most of the research on autonomous vehicles has paid little attention to road profile information, which is a significant reference for vehicles driving on uneven terrain. Most vehicles experience violent vibrations when driving on uneven terrain, which reduce the accuracy and stability of data obtained by LiDAR and IMUs. Vehicles with an active suspension system, on the other hand, can maintain stability on uneven roads, which further guarantees sensor accuracy. In this paper, we propose a novel method for road profile estimation using LiDAR and vehicles with an active suspension system. In the former, 3D laser scanners, IMU, and GPS were used to obtain accurate pose information and real-time cloud data points, which were added to an elevation map. In the latter, the elevation map was further processed by a Kalman filter algorithm to fuse multiple cloud data points at the same cell of the map. The model predictive control (MPC) method is proposed to control the active suspension system to maintain vehicle stability, thus further reducing drifts of LiDAR and IMU data. The proposed method was carried out in outdoor environments, and the experiment results demonstrated its accuracy and effectiveness.

Physical Characteristics of Coupled Plasma and Its Influence on Weld Formation in Hybrid Laser-Double-Arc Welding.

Hybrid laser-double-arc welding (HLDAW) is an efficient welding method with multi-heat sources comprised of two arcs and a laser beam, which is an intricate process with much randomness and uncertainty due to the mutual effect between multi-plasmas. Compared with double-arc welding (DAW), HLDAW can generally obtain a more stable welding process and deeper weld penetration, which is directly affected by the synergistic effect of multiple heat sources; however, the mechanism has not been systematically studied. In this study, the multi-information analysis method is adopted to study the distribution of electron temperatures, electron densities and electrical conductivity of double-arc welding (DAW) and HLDAW by utilizing synchronous radiation spectrum, high-speed photography and electrical signal sensing technology. The results indicated that the high concentration of charged particles provided a conductive channel for the two arcs to achieve a more stable welding process in HLDAW. The synergy between the laser and the arc changed the flow mode of the weld pool, which facilitated the molten metal flowing toward the bottom of the molten pool. Obtaining the same melting depth, the line energy input of HLDAW was 13% lower than that of DAW; the heat affected zone is narrower and the grain size is smaller. The weld penetration depth was improved in HLDAW, which was 1.8 times that of DAW and 1.5 times that of pure laser welding under the laser power of 1.5 kW. The weld penetration of HLDAW increased with laser power.

Pose Prediction of Autonomous Full Tracked Vehicle Based on 3D Sensor.

Autonomous vehicles can obtain real-time road information using 3D sensors. With road information, vehicles avoid obstacles through real-time path planning to improve their safety and stability. However, most of the research on driverless vehicles have been carried out on urban even driveways, with little consideration of uneven terrain. For an autonomous full tracked vehicle (FTV), the uneven terrain has a great impact on the stability and safety. In this paper, we proposed a method to predict the pose of the FTV based on accurate road elevation information obtained by 3D sensors. If we could predict the pose of the FTV traveling on uneven terrain, we would not only control the active suspension system but also change the driving trajectory to improve the safety and stability. In the first, 3D laser scanners were used to get real-time cloud data points of the terrain for extracting the elevation information of the terrain. Inertial measurement units (IMUs) and GPS are essential to get accurate attitude angle and position information. Then, the dynamics model of the FTV was established to calculate the vehicle's pose. Finally, the Kalman filter was used to improve the accuracy of the predicted pose. Compared to the traditional method of driverless vehicles, the proposed approach was more suitable for autonomous FTV. The real-world experimental result demonstrated the accuracy and effectiveness of our approach.