Search

Abstract Background Thyroid disorders(TD) poses a significant health threat to Americans due to its high incidence rate. Obesity, a common factor linked to thyroid disorders, has garnered increasing attention. While Body mass index (BMI) is a widely used obesity index, it fails to account for the distribution of muscle and fat in the body. Recently, tMFR has emerged as a crucial obesity index in clinical research, warranting further investigation into its association with TD. Objective Exploring the association between tMFR and thyroid disorders. Method A comprehensive survey and data analysis were conducted using the NHANES database to investigate the relationship between tMFR and the risk of TD. This study utilized multiple logistic regression, smooth curve fitting, and subgroup analysis across four periods from 2011 to 2018. Result A total of 11,912 subjects were included in the study, showing a prevalence of 7.14% for TD. The research indicated that tMFR had an inverse correlation with the risk of TD in a comprehensive model (OR = 0.90, 95% CI 0.82 to 1.00). When tMFR was divided into quartiles (Q1-Q4), individuals in the highest quartile had a 28% lower risk of TD than those in Q1 (OR = 0.72, 95% CI 0.57 to 0.91). Analysis using smoothed curve fitting demonstrated a nonlinear relationship between tMFR and TD risk, with the inflection point for tMFR saturation effect identified as 1.5. Subgroup analysis further confirmed the strong association between tMFR and TD risk. Receiver operating characteristic (ROC) curve analysis indicated that tMFR exhibited superior predictive ability for TD relative to BMI. Conclusion The study found a negative association between tMFR and the risk of TD; however, additional prospective studies are required to validate these findings.

Abstract Some special manufacturing fields such as aerospace may encounter mixed production of multiple research and development projects and multiple batch production projects. Under these special production conditions resource conflicts are more severe, resulting in uncertain operating times that are difficult to predict. In addition, a single project may have tens of thousands of supporting products, making it difficult to effectively control the total construction process. To address these challenges this paper proposes new methods. A model, EMA-DCPM (dynamic critical path method) incorporating attention mechanisms in Enterprise Resource Planning and Mechanical Engineering Society) has been proposed. This model predicts product job time through machine learning methods and discovers the predictive advantage of the attention mechanism through data comparison. The CPM control algorithm was improved to enhance its robustness and an efficient modeling method, “5+X” was proposed. This new method is suitable for mixed line planning management in sophisticated manufacturing projects and has value for practical applications.

Abstract Background Previous research has revealed the potential impact of circadian rhythms on pulmonary diseases; however, the connection between circadian rhythm-associated Thyrotroph Embryonic Factor (TEF) and Pulmonary Arterial Hypertension (PAH) remains unclear. We aim to assess the genetic causal relationship between TEF and PAH by utilizing two sets of genetic instrumental variables (IV) and publicly available Pulmonary Arterial Hypertension Genome-Wide Association Studies (GWAS). Methods Total of 23 independent TEF genetic IVs from recent MR reports and PAH GWAS including 162,962 European individuals were used to perform this two-sample MR study. Gain- and loss-of-function experiments were used to demonstrate the role of TEF in PAH. Results Our analysis revealed that as TEF levels increased genetically, there was a corresponding increase in the risk of PAH, as evidenced by IVW (OR = 1.233, 95% CI: 1.054–1.441; P = 0.00871) and weighted median (OR = 1.292, 95% CI for OR: 1.064–1.568; P = 0.00964) methods. Additionally, the up-regulation of TEF expression was associated with a significantly higher likelihood of abnormal circadian rhythm (IVW: P = 0.0024733, β = 0.05239). However, we did not observe a significant positive correlation between circadian rhythm and PAH (IVW: P = 0.3454942, β = 1.4980398). In addition, our in vitro experiments demonstrated that TEF is significantly overexpressed in pulmonary artery smooth muscle cells (PASMCs). And overexpression of TEF promotes PASMC viability and migratory capacity, as well as upregulates the levels of inflammatory cytokines. Conclusion Our analysis suggests a causal relationship between genetically increased TEF levels and an elevated risk of both PAH and abnormal circadian rhythm. Consequently, higher TEF levels may represent a risk factor for individuals with PAH.

Abstract Patients with previous interatrial shunt device (IASD) implantation may face greater challenges during future left atrial interventional procedures. Herein, we report the first case of left atrial appendage closure (LAAC) in a patient with previous IASD implantation. The patient successfully underwent LAAC using the LAmbre device without complications.

Abstract Photothermal immunotherapy is regarded as the ideal cancer therapeutic modality to against malignant solid tumors; however, its therapeutic benefits are often modest and require improvement. In this study, a thermoresponsive nanoparticle (BTN@LND) composed of a photothermal agent (PTA) and pyroptosis inducer (lonidamine) were developed to enhance immunotherapy applications. Specifically, our “two-step” donor engineering strategy produced the strong NIR-II-absorbing organic small-molecule PTA (BTN) that exhibited high NIR-II photothermal performance (ε1064 = 1.51 × 104 M−1 cm−1, η = 75.8%), and this facilitates the diagnosis and treatment of deep tumor tissue. Moreover, the fabricated thermally responsive lipid nanoplatform based on BTN efficiently delivered lonidamine to the tumor site and achieved spatiotemporal release triggered by the NIR-II photothermal effect. In vitro and in vivo experiments demonstrated that the NIR-II photothermal therapy (PTT)-mediated on-demand release of cargo effectively faciliated tumor cell pyroptosis, thereby intensifying the immunogenic cell death (ICD) process to promote antitumor immunotherapy. As a result, this intelligent component bearing photothermal and chemotherapy can maximally suppress the growth of tumors, thus providing a promising approach for pyroptosis/NIR-II PTT synergistic therapy against tumors. Graphical Abstract

Abstract Motion blur can easily affect the quality of QR code image, making it difficult to recognize QR codes on moving objects. This paper proposes an algorithm for the recognition of motion-blurred QR codes based on generative adversarial network and attention mechanism. Firstly, a multi-scale feature extraction framework for motion defuzzification is designed using deep convolutional neural networks, and enhanced multi-scale residual blocks and multi-scale feature extraction modules are utilized to capture rich local and global features. Secondly, the efficient channel attention module is added to strengthen the weights of effective features and suppress invalid features by modeling the correlations between channels. In addition, training stability is achieved through the use of the WGAN-div loss function, leading to the generation of higher quality samples. Finally, the proposed algorithm is evaluated through qualitative and quantitative comparisons with several recent methods on both the GOPRO public dataset and a self-constructed QR code dataset, respectively. The experimental results demonstrate that, compared to the other methods, the proposed algorithm has shown significant improvements in both processing time and recognition accuracy when dealing with the task of severe motion-blurred QR code recognition.

Background: Patent foramen ovale (PFO) is associated with migraine; however, the mechanism of PFO-associated migraine is not well known; additionally, percutaneous closure is controversial. This study aimed to investigate in situ thrombi within the PFO and explore the possible predictors of the effectiveness of PFO closure in migraineurs. Methods: This prospective cohort study included 48 asymptomatic patients and 92 migraineurs with PFO. Optical coherence tomography (OCT) was used to evaluate the PFO microstructure. Only migraineurs underwent percutaneous closure. Migraineurs were divided into two cohorts based on the presence of a thrombus within the PFO. The symptoms were assessed at the 12-month follow-up visit. Predictors were evaluated employing multivariate logistic regression and receiver operating characteristic curve analyses. Results: In situ thrombi within PFO were identified in 69 migraineurs and in two asymptomatic patients (76.7 % vs. 4.3 %; P

Abstract Drug-resistant bacterial biofilm infections (BBIs) are refractory to elimination. Near-infrared-II photothermal therapy (NIR-II PTT) and chemodynamic therapy (CDT) are emerging antibiofilm approaches because of the heavy damage they inflict upon bacterial membrane structures and minimal drug-resistance. Hence, synergistic NIR-II PTT and CDT hold great promise for enhancing the therapeutic efficacy of BBIs. Herein, we propose a biofilm microenvironment (BME)-responsive nanoplatform, BTFB@Fe@Van, for use in the synergistic NIR-II PTT/CDT/antibiotic treatment of BBIs. BTFB@Fe@Van was prepared through the self-assembly of phenylboronic acid (PBA)-modified small-molecule BTFB, vancomycin, and the CDT catalyst Fe2+ ions in DSPE-PEG2000. Vancomycin was conjugated with BTFB through a pH-sensitive PBA-diol interaction, while the Fe2+ ions were bonded to the sulfur and nitrogen atoms of BTFB. The PBA-diol bonds decomposed in the acidic BME, simultaneously freeing the vancomycin and Fe2+ irons. Subsequently, the catalytic product hydroxyl radical was generated by the Fe2+ ions in the oxidative BME overexpressed with H2O2. Moreover, under 1064 nm laser, BTFB@Fe@Van exhibited outstanding hyperthermia and accelerated the release rate of vancomycin and the efficacy of CDT. Furthermore, the BTFB@Fe@Van nanoplatform enabled the precise NIR-II imaging of the infected sites. Both in-vitro and in-vivo experiments demonstrated that BTFB@Fe@Van possesses a synergistic NIR-II PTT/CDT/antibiotic mechanism against BBIs.

It is very difficult for manufacturing enterprises to achieve automatic coordination of multiproject and multilevel planning when they are unable to make large-scale resource adjustments. In addition, planning and coordination work mostly relies on human experience, and inaccurate planning often occurs. This article innovatively proposes the PERT-RP-DDPGAO algorithm, which effectively combines the program evaluation and review technique (PERT) and deep deterministic policy gradient (DDPG) technology. Innovatively using matrix computing, the resource plan (RP) itself is used for the first time as an intelligent agent for reinforcement learning, achieving automatic coordination of multilevel plans. Through experiments, this algorithm can achieve automatic planning and has interpretability in management theory. To solve the problem of continuous control, the second half of the new algorithm adopts the DDPG algorithm, which has advantages in convergence and response speed compared to traditional reinforcement learning algorithms and heuristic algorithms. The response time of this algorithm is 3.0% lower than the traditional deep Q-network (DQN) algorithm and more than 8.4% shorter than the heuristic algorithm.

The genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection, and it is known for its iterative optimization capabilities in both constrained and unconstrained environments. In this paper, a novel method for GA-based dual-automatic guided vehicle (AGV)-ganged path planning is proposed to address the problem of frequent steering collisions in dual-AGV-ganged autonomous navigation. This method successfully plans global paths that are safe, collision-free, and efficient for both leader and follower AGVs. Firstly, a new ganged turning cost function was introduced based on the safe turning radius of dual-AGV-ganged systems to effectively search for selectable safe paths. Then, a dual-AGV-ganged fitness function was designed that incorporates the pose information of starting and goal points to guide the GA toward iterative optimization for smooth, efficient, and safe movement of dual AGVs. Finally, to verify the feasibility and effectiveness of the proposed algorithm, simulation experiments were conducted, and its performance was compared with traditional genetic algorithms, Astra algorithms, and Dijkstra algorithms. The results show that the proposed algorithm effectively solves the problem of frequent steering collisions, significantly shortens the path length, and improves the smoothness and safety stability of the path. Moreover, the planned paths were validated in real environments, ensuring safe paths while making more efficient use of map resources. Compared to the Dijkstra algorithm, the path length was reduced by 30.1%, further confirming the effectiveness of the method. This provides crucial technical support for the safe autonomous navigation of dual-AGV-ganged systems.

In the sphere of urban renewal of historic districts, preserving and innovatively reinterpreting traditional architectural styles remains a primary research focus. However, the modernization and adaptive reuse of traditional buildings often necessitate changes in their functionality. To cater to the demands of tourism in historic districts, many traditional residential buildings require conversion to commercial use, resulting in a mismatch between their external form and their internal function. This study explored an automated approach to transform traditional residences into commercially viable designs, offering an efficient and scalable solution for the modernization of historic architecture. We developed a methodology based on diffusion models, focusing on a dataset of nighttime shopfront facades. By training a low-rank adaptation (LoRA) model and integrating the ControlNet model, we enhanced the accuracy and stability of the generated images. The methodology’s performance was validated through qualitative and quantitative assessments, optimizing the batch size, repetition, and learning rate configurations. These evaluations confirmed the method’s effectiveness. Our findings significantly advance the modern commercial style transformation of historical architectural facades, providing a novel solution that maintains the aesthetic and functional integrity, thereby fostering breakthroughs in traditional design thinking and exploring new possibilities for the preservation and commercial adaptation of historical buildings.

Coal has remained the primary component of China’s energy structure, and high-intensity extraction has continued in the central and western coal-producing regions of China. In contrast to the abundant coal resources, water resources have become extremely scarce in these regions, creating a conflict between coal resource extraction and water resource conservation. The coal mine underground reservoir (CMUR), as a typical technology for combined coal and water extraction and water-preserving coal mining, has been applied in numerous mines in central and western China. This effectively alleviates water resource shortages and achieves the goal of water resource conservation. The CMURs utilizes the goaf created by longwall mining as the water storage space. The reservoir dam structure comprises coal pillars, which serve as protective coal pillars in the mining area, and artificial dam structures that filled the gaps between these coal pillars. The stability of the dam structure under the complex stress effects of hydraulic coupling has been identified as the key to maintaining the safe operation of the CMUR. The mechanical properties, stress field, fracture field, and seepage field (“three fields”) change mechanisms, as well as the research results on size optimization of coal pillar dams and artificial dams in CMURs, were systematically reviewed. The core content included the instability and failure mechanisms of dam structures under the comprehensive coupling effects of factors such as dry–wet cycles of mine water, long-term immersion, chemical effects of high-salinity water, dynamic and static loads, and cyclic loads. This paper is considered to have certain reference value for the study of the stability of dam structures in CMURs and to provide some guidance for the safe operation of CMURs.

Abstract The visualization of bone imaging in vivo is of great significance for the understanding of some bone-related diseases or physiological processes. Herein, a bone-targeted NIR-II small molecule (TTQF-SO3), which was modified with multiple sulfonate groups, was successfully fabricated for the second near-infrared (NIR-II) bone imaging. In vitro studies revealed that TTQF-SO3 showed high affinity for hydroxyapatite and excellent macrophage accumulation ability. In in vivo assays, TTQF-SO3 displayed high bone uptake ability and high NIR-II bone imaging quality, demonstrating the specific bone-targeting ability of the sulfonate-containing probe. In addition, the noninvasive NIR-II imaging detection in bone calcium loss was successfully verified in osteoporosis mice models. Moreover, the negative charge characteristic of TTQF-SO3 showed efficient lymphoid enrichment in living mice by intravenous injection. Overall, these warrant that our TTQF-SO3 is an optimal bone-targeted diagnostic agent for high-quality NIR-II imaging, highlighting its potential promise for clinical translation. Graphical Abstract

BackgroundMild cognitive impairment (MCI) is a pre-clinical stage of Alzheimer's disease (AD). Understanding the transition probabilities across the disease continuum of AD, ranging from MCI to AD to Mortality is crucial for the economic modeling of AD and effective planning of future interventions and healthcare resource allocation decisions. This study uses the Multi-state Markov model to quantify the transition probabilities along the disease progression and specifically investigates medications as modifiable risk factors of AD associated with accelerated or decelerated transition times from MCI to AD, MCI to mortality, and AD to mortality.MethodsIndividuals with MCI were identified from the National Alzheimer's Coordinating Center between September 2005 and May 2021. A three-state Markov model was postulated to model the disease progression among three states: MCI, AD, and mortality with adjustment for demographics, genetic characteristics, comorbidities and medications. Transition probabilities, the total length of stay in each state, and the hazard ratios of the use of medications for diabetes, hypertension, and hypercholesterolemia (the known modifiable risk factors of AD) were evaluated for these transitions.Results3,324 individuals with MCI were identified. The probability of developing AD after one year since the initial diagnosis of MCI is 14.9%. After approximately 6 years from the initial diagnosis of MCI, the probability of transitioning to AD increases to nearly 41.7% before experiencing a subsequent decline. The expected total lengths of stay were 5.38 (95% CI: 0.002-6.03) years at MCI state and 7.61 (95%CI: 0.002-8.88) years at AD state. Patients with active use of lipid-lowering agents were associated with significantly lower hazards of transitioning from MCI to AD (HR: 0.83, 95%CI:0.71-0.96), MCI to mortality (HR: 0.51, 95%CI:0.34-0.77), and AD to mortality (HR: 0.81, 95%CI:0.66-0.99).ConclusionsResults suggest that lipid-lowering agents may confer a protective effect, delaying the onset of AD. Additionally, lipid-lowering agents indicate a favorable association with a longer survival time.

Traditional manufacturing enterprises cannot adjust their production line structure in the short term. They face significant challenges in adapting to the rapidly changing market environment and meeting various variable batch production requirements. Building a suitable and convenient multi-layer planning and scheduling model is an important goal to solve the efficient operation of manufacturing enterprises. This paper proposes a planning and scheduling design that meets the needs of enterprise and the production workshop using the APERT-VC model through a top-down design methodology. APERT is an enterprise-level plan that uses attention mechanisms to collect job plan time and decomposes project plans into workshop plans through PERT technology. virtual command is workshop level plan management, which converts workshop plans into time series vectors and achieves rapid and comprehensive guidance of workshop resource planning for enterprises through multiple classification and decision-making. Through experiments, the algorithm achieved production scheduling accuracy improvement of over 30% compared to previous algorithms and a decision accuracy rate of over 90%. The first half of the new model solves the problem of collecting work time for multi variety and variable batch products, and improves the accuracy of algorithm input. The second half of the new algorithm innovatively combines image recognition technology with dispatcher behavior, achieving efficient simulation results.

Abstract Emerging evidence indicates the critical roles of N6-methyladenosine (m6A) modification in human cancers. Herein, our work reported that a novel m6A-modified circRNA from the MYO1C gene, circMYO1C, upregulated in the pancreatic ductal adenocarcinoma (PDAC). Our findings demonstrated that circMYO1C is highly expressed in PDAC tissues. Functionally, circMYO1C promoted the proliferation and migration of PDAC cells in vitro and its silencing reduced the tumor growth in vivo. Mechanistically, circMYO1C cyclization was mediated by m6A methyltransferase METTL3. Moreover, methylated RNA immunoprecipitation sequencing (MeRIP-seq) unveiled the remarkable m6A modification on PD-L1 mRNA. Moreover, circMYO1C targeted the m6A site of PD-L1 mRNA to enhance its stability by cooperating with IGF2BP2, thereby accelerating PDAC immune escape. In conclusion, these findings highlight the oncogenic role of METTL3-induced circMYO1C in PDAC tumorigenesis via an m6A-dependent manner, inspiring a novel strategy to explore PDAC epigenetic therapy.

Breast cancer is one of the most prevalent cancers in women. Reliable pathology identification can help histopathologists make accurate diagnosis of breast cancer but require specialized histopathological knowledge and a significant amount of manpower and medical resources. In this study, we fuse the coordinated attention mechanism to enhance the image texture analysis capability of the DenseNet, and build the CA-BreastNet model to classify microscopic histopathological images of specific types of breast cancers in the BreakHis dataset. More crucially, convolutional decision trees based on the specialized enhanced classifying strategy(SECS) are built to increase the overall accuracy of the network by reducing the model's accuracy restriction imposed by dataset structures. The related experimental results show that our network has strong performance and the SECS offers researchers reliable and effective performance enhancement guidelines. The accuracy of the convolutional decision trees reaches 99.75% for binary classification and 95.69% for eight-class classification, which means our model and strategy will be useful in the field of automatic diagnosis of breast cancer.

To ensure the precise dimensioning and effective testing of drivetrain components, it is crucial to have a thorough understanding of customer requirements, with a particular emphasis on customer stress on these components. An accurate interpretation of customer data is essential for determining representative customer requirements, such as load collectives. The automobile industry has faced challenges in analyzing large amounts of customer driving data to obtain representative load collectives as target values in durability design. However, due to technical limitations and cost constraints, collecting data from a large sample size is not feasible. The ongoing digitalization of the automotive industry, driven by an increasing number of connected vehicles, enhances data-based and customer-oriented development. This paper investigates representative customer load collectives using cloud data from over 40,000 customer vehicles to lay the groundwork for realizing robust requirement engineering. A systematic method for analyzing big data on the cloud was introduced. The derived component-specific damage distribution from these collectives adopts a unique approach, utilizing the 1% vehicle term instead of the common 1% customer term to represent typical customer stress. This study shows that the driven mileage and the number of vehicles are crucial factors in 1% vehicle analysis. An analysis of the characteristics of the 1% vehicle is conducted, followed by an exploration to determine the required vehicle quantity for obtaining stable results. The shape parameter of the damage distribution determines the necessary number of vehicles for a reliable conclusion. Additionally, a comparative analysis of market-specific customer requirements between the US and Europe is presented, and real usage differences in customer operations are explained using an operating point frequency heatmap. The information presented in this paper provides valuable input for optimizing durability design and conducting efficient, customer-oriented tests, resulting in significant reductions in development time and costs.

The digitalization of the automotive industry presents significant potential for technical advantages, such as the online collection of customer driving data. These data can be used for customer-oriented development to improve the durability of components or systems. However, due to current limitations in data transfer, the sampling frequency is typically lower than that of classic dataloggers. This paper examines the importance of low-frequency data in the development of drivetrain durability and investigates the extent to which these data can be utilized for a drivetrain durability analysis. Real driving data were utilized as a database to demonstrate the impact of downsampling on data significance, with the deviation in damage serving as the criteria. The findings suggest that low-frequency data, when available in sufficient quantities, can provide valuable information for predicting durability in rollover and time at level classification. The deviation in the damage prediction is less than 2% for distances exceeding 5000 km. However, low-frequency data are not suitable for rainflow analysis. Finally, the database size was adjusted to assess the statistical stability of the durability prediction. A larger dataset typically reduces variance. The paper presents evidence for the quality and usability of cloud data in drivetrain durability design. Cloud data from a significant number of customer vehicles can be used for certain analyses of representative customer load collectives, which can reduce development time and costs.

Solid Oxide Fuel Cells (SOFCs) are emerging as a leading solution in sustainable power generation, boasting high power-to-energy density and minimal emissions. With efficiencies potentially exceeding 60% for electricity generation alone and up to 85% when in cogeneration applications, SOFCs significantly outperform traditional combustion-based technologies, which typically achieve efficiencies of around 35–40%. Operating effectively at elevated temperatures (600 °C to 1000 °C), SOFCs not only offer superior efficiency but also generate high-grade waste heat, making them ideal for cogeneration applications. However, these high operational temperatures pose significant thermal management challenges, necessitating innovative solutions to maintain system stability and longevity. This review aims to address these challenges by offering an exhaustive analysis of the latest advancements in SOFC thermal management. We begin by contextualizing the significance of thermal management in SOFC performance, focusing on its role in enhancing operational stability and minimizing thermal stresses. The core of this review delves into various thermal management subsystems such as afterburners, heat exchangers, and advanced thermal regulation strategies. A comprehensive examination of the recent literature is presented, highlighting innovations in subsystem design, fuel management, flow channel configuration, heat pipe integration, and efficient waste heat recovery techniques. In conclusion, we provide a forward-looking perspective on the state of research in SOFC thermal management, identifying potential avenues for future advancements and their implications for the broader field of sustainable energy technologies.

In modern industrial manufacturing, there are uncertain dynamic disturbances between processing machines and jobs which will disrupt the original production plan. This research focuses on dynamic multi-objective flexible scheduling problems such as the multi-constraint relationship among machines, jobs, and uncertain disturbance events. The possible disturbance events include job insertion, machine breakdown, and processing time change. The paper proposes a conv-dueling network model, a multidimensional state representation of the job processing information, and multiple scheduling objectives for minimizing makespan and delay time, while maximizing the completion punctuality rate. We design a multidimensional state space that includes job and machine processing information, an efficient and complete intelligent agent scheduling action space, and a compound scheduling reward function that combines the main task and the branch task. The unsupervised training of the network model utilizes the dueling-double-deep Q-network (D3QN) algorithm. Finally, based on the multi-constraint and multi-disturbance production environment information, the multidimensional state representation matrix of the job is used as input and the optimal scheduling rules are output after the feature extraction of the conv-dueling network model and decision making. This study carries out simulation experiments on 50 test cases. The results show the proposed conv-dueling network model can quickly converge for DQN, DDQN, and D3QN algorithms, and has good stability and universality. The experimental results indicate that the scheduling algorithm proposed in this paper outperforms DQN, DDQN, and single scheduling algorithms in all three scheduling objectives. It also demonstrates high robustness and excellent comprehensive scheduling performance.

Abstract Background There is a lack of available data on specific prognostic comparisons between transcatheter aortic valve replacement (TAVR) using self-expandable valves (SEV) in patients with stenotic Type 0, Type 1 bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV). Objectives To evaluate the association between aortic valve morphology and outcomes following self-expandable TAVR. Methods Consecutive patients with aortic stenosis(AS) undergoing self-expandable TAVR were enrolled and categorized into three groups (Type 0/Type 1 BAV or TAV) according to the Sievers classification. The primary endpoint was a composite of all-cause mortality and rehospitalization for heart failure (HF) within 2 years. Secondary outcomes included procedural complications and major cardiovascular events observed in clinical follow-ups. Clinical outcomes at 2 years following TAVR were compared among three groups using Kaplan-Meier curve and multivariable Cox proportional hazards regression models. Results A total of 344 AS patients (Type 0: 86; Type 1: 109; TAV: 149) were enrolled. The presence of moderate or severe paravalvular leak (PVL) was significantly higher in patients with Type 0 and Type 1 BAV versus TAV (10.47% vs. 16.51% vs. 6.71%, p = 0.043). All-cause 30-day mortality (2.33% vs. 0.92% vs. 2.68%, p = 0.626) and 2-year mortality (3.49% vs. 5.50% vs. 6.71%, p = 0.657) was comparable among the three groups. However, rehospitalization for HF within 2 years was significantly higher in Type 1 BAV (11.63% vs. 20.18% vs. 8.72%, p = 0.020). Multivariate Cox analysis showed that a higher STS score, Type 1 BAV morphology and excess leaflet calcification (≥ median calcium volume (CV) of the entire population) were independent predictors for HF rehospitalization. Additional intragroup Kaplan‒Meier analysis showed that excess leaflet calcification could predict higher long-term mortality and rehospitalization risk for HF(HR (95% CI): 3.430 (1.166–10.090), log rank p = 0.017) in Type 1 BAV patients. Conclusion Outcomes of self-expandable TAVR in BAV-AS patients might vary depending on valve subtypes. BAV patients with excess leaflet calcification and a raphe, especially calcified, had an increased risk of moderate PVL and HF readmission in mid-to-long term follow-ups.

Abstract Bicuspid aortic valve (BAV) is the most common congenital heart defect in human beings, with an estimated prevalence in the general population of between 0.5 and 2%. Moreover, BAV is the most common cause of aortic stenosis in the pediatric population. Patients with BAV may have no symptoms for life, and some of them may progress to aortic stenosis. Genetic factors increase the susceptibility and development of BAV. However, the pathogenesis and BAV are still unclear, and more genetic variants are still needed for elucidating the molecular mechanism and stratification of patients. The present study carried out screening of variants implicated in disease in BAV patients. The whole-exome sequencing (WES) was performed in 20 BAV patients and identified 40 different heterozygous missense mutations in 36 genes (MIB2, FAAH, S100A1, RGS16, MAP3K19, NEB, TTN, TNS1, CAND2, CCK, KALRN, ATP10D, SLIT3, ROS1, FABP7, NUP205, IL11RA, NPR2, COL5A1, CUBN, JMJD1C, ANXA7, TRIM8, LGR4, TPCN2, APOA5, GPR84, LRP1, NCOR2, AKAP11, ESRRB, NGB, AKAP13, WWOX, KCNJ12, ARHGEF1). The mutations in these genes were identified as recurrent variants implicated in disease by in silico prediction tool analysis. Nine genes (MIB2, S100A1, TTN, CCK, NUP205, LGR4, NCOR2, ESRRB, and WWOX) among the 36 genes were identified as variants implicated in disease via unanimous agreement of in silico prediction tool analysis and sequenced in an independent cohort of 137 BAV patients to validate the results of WES. BAV patients carrying these variants demonstrated reduced left ventricular ejection fractions (LVEF) (63.8 ± 7.5% vs. 58.4 ± 5.2%, P

Abstract Background Timothy's law to reduce mental health care disparities was enacted in January 2007 in New York state (NY). According to Timothy's law, "if a patient is suffering from a Biologically Based Mental Illness, or is a Child with Serious Emotional Disturbances, the Inpatient mental health benefit will be the same as for any other illness". An assessment of its impact on inpatient mental health care is lacking. We provide a rigorous study of this policy intervention’s effect over the first year of its implementation. Methods We used a quasi-experimental design to combine the difference-in-difference method and propensity score weighting. Data are from inpatient records in NY and California (CA) (as a control) between January 2006 to December 2006 (the pre-enactment year in NY) and January to December 2007 (the enactment year) for non-Medicare/Medicaid patients hospitalized in both years with specific illnesses covered by Timothy's Law. Change in length of stay from 2006 to 2007 was measured for each patient, and the differences observed in NY and California were compared to each other (Difference-in-Difference), with differences in the characteristics of patients in NY and California addressed through Propensity Score Weighting (PSW). Results Before Timothy's Law was enacted (2006), length of stay (LOS) in NY was 16.3 days on average, and length of stay per hospitalization (LOSPH) was 11.72 days on average for the 1237 patients under study in 2006. In 2007, LOS increased by 4.91 days in NY (95% CI (2.89, 7.01)) compared with similar patients in California, and LOSPH by 3.25 days (95% CI (1.96, 4.57)). Among patients with serious mental illness diagnoses, LOS in NY increased by 7.07 days (95% CI (4.15, 10.17)), and LOSPH by 4.04 days (95% CI (1.93, 6.03)) compared to California. Conclusions Our study strongly suggests that, within the time frame of just a single year, Timothy's Law significantly increased inpatient mental healthcare utilization in NY. Our study raises the possibility that similar laws in other locations could have similar effects.

Abstract Introduction Chronic obstructive pulmonary disease (COPD) is associated with chronic inflammation that predominantly affects the lung and peripheral airways. Previous investigation has underlined the efficacy of luteolin in the treatment of inflammation‐related symptoms. Accordingly, our study concentrates on unveiling the effect of luteolin on COPD. Methods Mice or A549 cells were treated with cigarette smoke (CS) to establish COPD models in vivo and in vitro. Then, the serum and bronchoalveolar lavage fluid of mice were harvested. The lung tissues of mice were subjected to hematoxylin‐eosin staining to observe the degree of damage. The inflammation and oxidative stress factors level were calculated via enzyme‐linked immunosorbent assay and quantitative real‐time polymerase chain reaction. The expressions of nuclear factor‐kappa B (NF‐κB) pathway‐related factors were detected by Western blot. Results In in vivo experiments, CS treatment reduced the weight of mice and promoted lung tissue damage, while luteolin attenuated the effect of CS on the mice. Moreover, luteolin inhibited the inflammation factors level, oxidative stress, and NADPH oxidase 4 (NOX4)‐mediated NF‐κB signaling pathway in CS‐induced COPD mice. Similar results were obtained in in vitro experiments that luteolin alleviated CS‐induced inflammation, oxidative stress, and NOX4‐mediated NF‐κB signaling pathway activation in CS‐treated A549 cells. Besides, NOX4 overexpression offset the impacts of luteolin on the CS‐induced A549 cells. Conclusion Luteolin alleviates inflammation and oxidative stress in COPD via NOX4‐mediated NF‐κB signaling pathway, which provides a theoretical basis for the treatment of COPD with luteolin.

The Solid Oxide Fuel Cell (SOFC) system is a highly intricate system characterized by multiple variables and couplings. Developing an accurate model for the SOFC independent power generation system is of paramount importance. Conducting experimental studies on the SOFC system is costly, and it carries certain risks due to the requirements for pure hydrogen, high-temperature environments, and other factors. To address these challenges, a high-performing model that precisely reflects the inherent characteristics of the SOFC is essential for dynamic static analysis and the identification of optimal operating points. This paper presents a SOFC system model based on current controls, which was implemented in the MATLAB/Simulink environment, and it utilized a nodal approach for modeling. The model incorporated a cold air bypass, which enabled the more precise control of the SOFC reactor’s inlet and outlet temperatures. Furthermore, a 3D test and verification method are proposed in order to focus on the influence of input parameters on the four electrical characteristics, and four thermal characteristics, of output parameters. By conducting one-dimensional, two-dimensional, and three-dimensional studies of these output parameters, a more intuitive understanding of the system’s response to changes in input parameters was obtained. Under conditions wherein all other variables were kept constant, the entire system attained its maximum efficiency at approximately FU = 0.8, BP = 0, and AR = 6. The outcomes of this study have significant implications for exploring the optimal operating point in the SOFC independent power generation system in an in-depth manner. It provides valuable insights for enhancing the system’s efficiency and performance.

Treading on the heels of the spread of the coronavirus, the “loneliness virus” has been capturing territories globally. Consumers are not immune to loneliness. Although academics and the general public have recognized the devastating effects of loneliness, the academic attention given to consumer loneliness (CL) is scattered and fragmentary. The purpose of this article is to systematically review the antecedents (predictors and alleviators) and consequences (consumer behaviors, emotions, preferences, attitudes, and cognition) of CL in various consumption contexts. This review also presents findings on CL as a mediator and moderator in consumer studies. This work adds to the growing body of CL literature by synthesizing the existing findings and knowledge. More importantly, we present a future research agenda by linking CL to significant research lines and detailed implications for practitioners in the marketplace.

Aims: The study aimed to compare paravalvular leak (PVL) changes after a transcatheter aortic valve replacement (TAVR) with self-expandable prosthesis between different aortic valve morphologies and evaluate the impact of paravalvular leak regression on clinical prognosis.Methods: Patients with aortic stenosis (AS) successfully treated with a self-expandable TAVR who were followed up for at least 1 year at our centre were consecutively enrolled from January 2016 to August 2019. Paired serial changes in paravalvular leak and other haemodynamic parameters by echocardiography were collected and compared between the bicuspid valve (BAV) and tricuspid aortic valve (TAV). A logistic regression model was used to explore the predictors of paravalvular leak regression (

Abstract Determining the sustainability of antibodies targeting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for predicting immune response against the Coronavirus disease 2019 (COVID-19). To quantify the antibody decay rates among the varying levels of anti-nucleocapsid (anti-N) Immunoglobulin G (IgG) in convalescent COVID-19 patients and estimate the length of time they maintained SARS-CoV-2 specific antibodies, we have collected longitudinal blood samples from 943 patients over the course of seven months after their initial detection of SARS-CoV-2 virus by RT-PCR. Anti-N IgG levels were then quantified in these blood samples. The primary study outcome was the comparison of antibody decay rates from convalescent patients with high or low initial levels of antibodies using a mixed linear model. Additional measures include the length of time that patients maintain sustainable levels of anti-N IgG. Antibody quantification of blood samples donated by the same subject multiple times shows a gradual decrease of IgG levels to the cutoff index level of 1.4 signal/cut-off (S/C) on the Abbott Architect SARS-CoV-2 IgG test. In addition, this study shows that antibody reduction rate is dependent on initial IgG levels, and patients with initial IgG levels above 3 S/C show a significant 1.68-fold faster reduction rate compared to those with initial IgG levels below 3 S/C. For a majority of the donors naturally occurring anti-N antibodies were detected above the threshold for only four months after infection with SARS-CoV-2. This study is clinically important for the prediction of immune response capacity in COVID-19 patients.

Abstract Increasing evidence has indicated the great diagnostic and therapeutic potentials of circular RNAs (circRNAs) in human cancers. Although the biological roles of circRNAs in pancreatic ductal adenocarcinoma (PDAC) have been partially annotated, the potential regulatory mechanism of circRNAs in PDAC tumorigenesis remains poorly understood. Here, our study found that the novel circRNA circSLIT2 was significantly upregulated in PDAC tissues and cells. Clinically, ectopic high-expression of circSLIT2 was correlated with unfavorable prognosis of PDAC patients. Functional experiments demonstrated that circSLIT2 promoted the aerobic glycolysis and proliferation of PDAC cells in vitro, and circSLIT2 knockdown inhibited tumor growth in vivo. Mechanistically, circSLIT2 acted as miRNA sponge to target miR-510-5p/c-Myc axis. Furthermore, c-Myc bound with the promoter region of lactate dehydrogenase A (LDHA) to activate the transcription. Collectively, present findings reveal that circSLIT2/miR-510-5p/c-Myc/LDHA axis participates in the aerobic glycolysis and carcinogenesis of PDAC, and may act as a promising therapeutic target.

BackgroundPeripheral artery disease (PAD) is a common atherosclerotic vascular disease. The use of drug-coated balloon (DCB) for the treatment of femoropopliteal artery disease has gradually increased. A certain percentage of patients developed target lesion restenosis after DCB treatment of the femoral popliteal artery. The neutrophil-to-lymphocyte ratio (NLR) is closely related to the level of inflammatory activity and has predictive value for atherosclerotic vascular disease. This study aimed to analyze the relationship between NLR and 1-year restenosis after DCB for femoropopliteal artery disease.MethodsPatients with femoropopliteal artery disease who were treated with DCBs at our hospital from May 2016 to December 2020 were retrospectively included. Baseline data during the patient’s first hospital stay and data during follow-up were collected. Demographic data, laboratory test results, lesion examination results, and major adverse events during the follow-up period were collected. Logistic regression was used to analyze the factors associated with restenosis after DCB.ResultsA total of 117 patients were included. During 1-year follow-up, 19 cases (16.2%) of restenosis were detected. Five of these patients (4.3% of total included patients) were readmitted for symptomatic ischemia. No deaths or amputations occurred. Baseline NLR in patients with restenosis was higher than that in patients without restenosis (2.4 (2.1, 3.4) vs. 1.8 (1.3, 2.3), P < 0.001). Logistic univariate and multivariate analysis showed that baseline hs-CRP level (OR = 1.10, 95%CI: 1.05–1.34), lesion length (OR = 1.04, 95%CI: 1.02–1.27), use of rivaroxaban (OR = 1.08, 95%CI: 1.05–1.39), NLR (OR = 1.47, 95%CI: 1.13–2.48), LDL-C level (OR = 1.25, 95%CI: 1.05–1.52), and diabetes (OR = 1.25, 95%CI: 1.05–1.52) = 1.18, 95%CI: 1.06–1.66) were predictors of restenosis.ConclusionBaseline NLR before DCB can predict the risk of restenosis after surgery.

Trucks have the problems of frequent braking and long-term braking during long-slope transportation. As the temperature of the traditional eddy current retarder is as high as 500 °C during braking, the braking torque of the retarder is seriously degraded. According to the principle of eddy current braking and current thermal effect, this paper proposes an eddy current retarder in which both the stator and the excitation coil are water-cooled. The multi-field coupling and bidirectional data transmission model of stator temperature field, coil temperature field and transient electromagnetic field are established. The relationship between the braking torque and the working time is analyzed under continuous braking conditions, considering the stator temperature and the temperature of the excitation coil. It provides theoretical support for the optimal design of the retarder. A test prototype of a water-cooled eddy current retarder was manufactured, and a bench drag test was carried out. The calculation results show that the numerical simulation method of coupling between the stator temperature field, the coil temperature field and the transient electromagnetic field is adopted, and the simulation values of the braking torque is in good agreement with the experimental values.

Abstract Background Pathological analysis of brain tissue from animals and humans with a history of traumatic brain injury (TBI) suggests that TBI could be one of the risk factors facilitating onset of dementia with possible Alzheimer’s disease (AD), but medications to prevent or delay AD onset are not yet available. Methods This study explores four medication classes (angiotensin-converting enzyme inhibitors (ACEI), beta blockers, metformin, and statins) approved by the Food and Drug Administration (FDA) for other indications and evaluates their influence when used in combination on the risk of possible AD development for patients with a history of TBI. We identified patients with history of TBI from an existing Department of Veterans Affairs (VA) national database. Among 1,660,151 veterans who used VA services between the ages of 50 to 89 years old, we analyzed 733,920 patients, including 15,450 patients with a history of TBI and 718,470 non-TBI patients. The TBI patients were followed for up to 18.5 years, with an average of 7.7 ± 4.7 years, and onset of dementia with possible AD was recorded based on International Statistical Classification of Diseases (ICD) 9 or 10 codes. The effect of TBI on possible AD development was evaluated by multivariable logistic regression models adjusted by age, gender, race, and other comorbidities. The association of ACEI, beta blockers, metformin, statins, and combinations of these agents over time from the first occurrence of TBI to possible AD onset was assessed using Cox proportional hazard models adjusted for demographics and comorbidities. Results Veterans with at least two TBI occurrences by claims data were 25% (odds ratio (OR) = 1.25, 95% confidence intervals (CI) (1.13, 1.37)) more likely to develop dementia with possible AD, compared to those with no record of TBI. In multivariable logistic regression models (propensity score weighted or adjusted), veterans taking a combination of ACEI and statins had reduced risk in developing possible AD after suffering TBI, and use of this medication class combination was associated with a longer period between TBI occurring and dementia with possible AD onset, compared to patients who took statins alone or did not take any of the four target drugs after TBI. Conclusions The combination of ACEI and statins significantly lowered the risk of development of dementia with possible AD in a national cohort of people with a history of TBI, thus supporting a clinical approach to lowering the risk of dementia with possible AD.

Objective: Intracranial hemorrhage (ICH) is a common complication of traumatic brain, in which tranexamic acid has been recommended as an additional therapy to prevent a second bleeding. However, the effect of early administration of tranexamic acid for ICH patients remains controversial.Methods: A systematic search was performed in Cochrane Library, Medline, Embase, and Web of Science. Poor outcome refers to significant hemorrhage growth, new intracranial hemorrhage, new focal cerebral ischaemic lesions, the need for neurosurgery, or death. Study heterogeneity and publication bias were estimated.Results: Seven randomized controlled trials involving 3,192 participants were included in our meta-analysis. Tranexamic acid administration in ICH patients was associated with better outcomes of hematoma expansion (odd ratios [OR] 0.79; 95% confidence interval (CI) CI, 0.67–0.93; I2 = 0%; P = 0.006) and growth of hemorrhagic lesions (weighted mean difference [WMD], −1.97 ml; 95% CI, −2.94 to −1.00; I2 = 14%; P < 0.001) than the placebo. No difference was found between the mortality, poor outcome, neurosurgical intervention, new bleeding, and the duration of hospital stay. Moreover, no publication bias was found.Conclusion: Our analysis reveals that the early treatment with tranexamic acid can significantly reduce the incidence of hematoma expansion and the volume of hemorrhagic lesion, but does not exert considerable effects on mortality, poor outcome, neurosurgery, rebleeding, and the duration of stay.

Direct ethanol solid oxide fuel cells (SOFCs) are the most energy-efficient and low-carbon technology for renewable power generation from biomass fuels, but they are hindered by carbon deposition on the Ni-based cermet anode. In this work, excessive Na+ dopant into La0.75Sr0.25Cr0.5Fe0.4Cu0.1O3-δ (LSCFC) perovskite was used as an additional internal reforming catalyst for direct carbon dioxide-ethanol SOFCs. Excessive Na+-doped LSCFC (N-LSCFC) demonstrated great potential in promoting electrochemical performance and internal reforming process fueled by carbon dioxide-ethanol mixture, because more oxygen vacancies and the precipitated Cu nano catalyst were helpful for the improvement of internal reforming and carbon tolerance. Electrochemical investigations proved that the vertical-microchannel anode supported the single cells using the N-LSCFC-Gd0.1Ce0.9O2-δ (GDC) internal reforming catalyst, showing a peak power density of 1044.41 and 855.56 mW/cm2 at 800 °C fueled by H2 and 50% CO2-50% C2H5OH, respectively. The preceding results indicate that excessive Na+ doping strategy into LSCFC as the additional internal reforming catalyst can improve the electrochemical performance and internal reforming process of direct carbon dioxide-ethanol SOFCs.

Objectives: To analyze the predictors of pericardial effusion (PE) during the perioperative period of the left atrial appendage closure procedure in our center.Methods: A total of 624 consecutive patients with non-valvular AF undergoing LAAC from May 2014 to October 2019 were involved in this study. Patients were divided into groups depending on whether they showed no PE, intraoperative PE or postoperative PE. We analyzed the predictors of PE during the perioperative period of the LAAC procedure.Results: (1) Of the 624 patients in our population (age 68.2 ± 9.1 years, 63% male, CHA2DS2-VASc score 3.4 ± 1.6), 30 patients experienced PE in the perioperative period, including 10 intraoperative PE and 20 postoperative PE. (2) A total of 26 (86.6%) patients had mild PE. 4 (13.4%) patients had pericardial tamponade, 2 (6.7%) of which were intraoperative, and the other 2 (6.7%) postoperative. (3) Significant differences were measured in relation to female sex, intraoperative time, combined procedures, changes in sinus rhythm, device retrieval times and duration of hospitalization between 2 groups (no PE occurred, intraoperative PE), P values were 0.039, 0.024, 0.004, 0.015, 0.003 and 0.039.Conclusions: Female sex, paroxysmal AF, changing in sinus rhythm, device retrieval times and intraoperative time all had a positive association with PE during the perioperative period.

A solid oxide fuel cell (SOFC) is an innovative power generation system that is green, efficient, and promising for a wide range of applications. The prediction and evaluation of the operation state of a solid oxide fuel cell system is of great significance for the stable and long-term operation of the power generation system. Prognostics and Health Management (PHM) technology is widely used to perform preventive and predictive maintenance on equipment. Unlike prediction based on the SOFC mechanistic model, the combination of PHM and deep learning has shown wide application prospects. Therefore, this study first obtains an experimental dataset through short-term degradation experiments of a 1 kW SOFC system, and then proposes an encoder-decoder RNN-based SOFC state prediction model. Based on the experimental dataset, the model can accurately predict the voltage variation of the SOFC system. The prediction results of the four different prediction models developed are compared and analyzed, namely, long short-term memory (LSTM), gated recurrent unit (GRU), encoder–decoder LSTM, and encoder–decoder GRU. The results show that for the SOFC test set, the mean square error of encoder–decoder LSTM and encoder–decoder GRU are 0.015121 and 0.014966, respectively, whereas the corresponding error results of LSTM and GRU are 0.017050 and 0.017456, respectively. The encoder–decoder RNN model displays high prediction precision, which proves that it can improve the accuracy of prediction, which is expected to be combined with control strategies and further help the implementation of PHM in fuel cells.

Pulmonary arterial hypertension (PAH) is an immune-mediated disease with poor prognosis and associated with various inflammatory immune diseases. In fact, its pathogenesis is far from clear. Although long non-coding RNAs (lncRNAs) have been implicated in PAH, the molecular mechanisms remain largely unknown. For the first time, in lungs of monocrotaline-induced PAH rat models, we simultaneously detected the expression profiles of lncRNAs and mRNAs by high-throughput sequencing, and explored their roles with bioinformatics analysis and cell assay to discover more potential pathogenesis about PAH. Our data identified that a total of 559 lncRNAs and 691 mRNAs were differentially expressed in lungs during the pathogenesis of PAH. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses demonstrated that these dysregulated lncRNAs and mRNAs participated in important biological processes and pathways of PAH, among which inflammatory and immune responses represented the chief enriched pathway. The lncRNA-mRNA co-expression network was developed to uncover the hidden interactions between lncRNAs and mRNAs. Further, the expression levels of lncRNAs (NONRATT018084.2, NONRATT009275.2, NONRATT007865.2, and NONRATT026300.2) and mRNAs (LGALS3, PDGFC, SERPINA1, and NFIL3) were confirmed using quantitative real-time PCR. In the end, lncRNA NONRATT009275.2 could facilitate macrophage polarization to M2 type and be involved in inflammatory immune response. In conclusion, this study provided candidate drug targets and potential roles on lncRNAs in the pathogenesis of PAH, and several key regulatory genes were identified, which laid the initial foundation for further mechanism study in PAH.

To adapt to the current situation of large-scale and network development of China's natural gas pipeline industry, we must pay attention to the gas transmission support capability of the entire pipeline network system during its operation period and evaluate its reliability with respect to the fulfillment of transmission tasks. This study focuses on the safe and efficient operation and management of large-scale natural gas pipeline network systems, with the objectives of analyzing the reliability indicators, system reliability, unit reliability, reliability data, and reliability management framework of the pipeline network system. The above incentives have lain a foundation for future sustainable reliability management of large-scale systems of natural gas pipeline networks in China. Herein, the purpose, significance, and content of reliability studies on natural gas pipeline networks are discussed in detail at the system level. We establish a distributed step calculation model of physical pipeline network system reliability based on pipeline process simulation technology, which can simplify and rapidly calculate the reliability of the pipeline network/pipelines/station system. The reliability calculation model of the system was tested in a compressor station. According to the sequence of station yard-typical structure-basic circuit-equipment unit, the network topology structure was divided into reliability block diagrams of different levels from top to bottom, which we used to calculate the reliability of individual equipment from bottom to top. Finally, the reliability of the compressor station under different start-up and shutdown schemes was obtained. Overall, the discussed methods are promising and will provide technical and decision-making support for the safe operation of natural gas pipeline networks. Keywords: Pipeline network systems, Natural gas, Unit reliability, System reliability, Operational efficiency, Reliability indicators

Objectives: The purpose of this study was to provide data on the long-term efficacy and safety of left atrial appendage occlusion (LAAO) in patients with atrial fibrillation (AF) and chronic thrombocytopenia (cTCP).Methods: Between January 2016 and December 2018, a total of 32 AF patients with thrombocytopenia (platelet count 0.9) and device-related thrombus (DRT) (3.13 vs. 2.50%, p > 0.9). Major (12.50 vs. 3.75%, p = 0.065) and minor bleeding (15.63 vs. 1.25%, p = 0.002) was more frequent in cTCP patients but no statistical difference was reached in major bleeding. Moreover, thrombocytopenia was also identified as an independent predictor of any bleeding events (OR: 8.150, 95% CI: 2.579–25.757, p < 0.001), while an inverse relationship between higher absolute platelet count and stroke events was revealed (OR: 1.015; 95% CI: 1.002~1.029, p = 0.022). However, in both groups we saw a significant reduction in observed annualized rates of non-procedural complications compared with the predicted values. In the cTCP and control groups, clinical thromboembolism was reduced by 100 and 74.32%, and major bleeding by 42.47 and 71.67%, respectively.Conclusion: Our preliminary results indicate that LAAO using the Watchman device could be a safe and effective means of preventing stroke in AF patients with or without thrombocytopenia, but bleeding complications should be monitored intensively in cTCP patients.

The U.S. with only 4% of the world’s population, bears a disproportionate share of infections in the COVID-19 pandemic. To understand this puzzle, we investigate how mitigation strategies and compliance can work together (or in opposition) to reduce (or increase) the spread of COVID-19 infection. Building on the Oxford index, we create state-specific stringency indices tailored to U.S. conditions, to measure the degree of strictness of public mitigation measures. A modified time-varying SEIRD model, incorporating this Stringency Index as well as a Compliance Indicator is then estimated with daily data for a sample of 6 U.S. states: New York, New Hampshire, New Mexico, Colorado, Texas, and Arizona. We provide a simple visual policy tool to evaluate the various combinations of mitigation policies and compliance that can reduce the basic reproduction number to less than one, the acknowledged threshold in the epidemiological literature to control the pandemic. Understanding of this relationship by both the public and policy makers is key to controlling the pandemic. This tool has the potential to be used in a real-time, dynamic fashion for flexible policy options. Our methodology can be applied to other countries and has the potential to be extended to other epidemiological models as well. With this first step in attempting to quantify the factors that go into the “black box” of the transmission factor β, we hope that our work will stimulate further research in the dual role of mitigation policies and compliance.

BackgroundA number of studies have reported the association between the use of angiotensin-converting enzyme inhibitor (ACEI) and angiotensin-II receptor blocker (ARB) medications and the occurrence or severity of coronavirus disease 2019 (COVID-19). Published results are inconclusive, possibly due to differences in participant comorbidities and sociodemographic backgrounds. Since ACEI and ARB are frequently used anti-hypertension medications, we aim to determine whether the use of ACEI and ARB is associated with the occurrence and severity of COVID-19 in a large study of US Veterans with hypertension.MethodsData were collected from the Department of Veterans Affairs (VA) National Corporate Data Warehouse (VA-COVID-19 Shared Data Resource) between February 28, 2020 and August 18, 2020. Using data from 228,722 Veterans with a history of hypertension who received COVID-19 testing at the VA, we investigated whether the use of ACEI or ARB over the two years prior to the index date was associated with increased odds of (1) a positive COVID-19 test, and (2) a severe outcome (hospitalization, mortality, and use of intensive care unit (ICU) and/or mechanical ventilation) among COVID-19-positive patients. We used logistic regression with and without propensity score weighting (PSW) to estimate the odds ratio (OR) and 95% confidence interval (95% CI) for the association between ACEI/ARB use and a positive COVID-19 test result. The association between medication use and COVID-19 outcome severity was examined using multinomial logistic regression comparing participants who were not hospitalized to participants who were hospitalized, were admitted to the ICU, used a mechanical ventilator, or died. All models were adjusted for relevant covariates, including demographics (age, sex, race, ethnicity), selected comorbidities, and the Charlson Comorbidity Index (CCI).ResultsThe use of ACEI significantly decreased the odds of a positive COVID-19 test among Veterans with hypertension (OR = 0.917, (0.887, 0.948) and OR = 0.926, (0.894, 0.958) with PSW). The use of ACEI, but not of ARB, was also associated with significantly increased odds of using mechanical ventilators (OR = 1.265, (1.010, 1.584) and OR = 1.210, (1.053, 1.39) with PSW) among all COVID-19 inpatients compared to outpatients.ConclusionsIn this study of Veterans with hypertension, ACEI was significantly associated with decreased odds of testing positive for COVID-19. With the exception of the association of ACEI with a small non-clinically-important increase in the odds of using mechanical ventilators, neither ACEI nor ARB was found to be associated with clinical severity or mortality among COVID-19-positive Veterans. The results of this study need further corroboration and validation in other cohort samples outside the VA.

Focusing on the safe operation of rail transit during earthquakes, the finite element method is used to construct a wheel-track-subgrade dynamics model in this study. Through spring-damper units, the relationship between the rail and the track slab and the connection between the track slab and the subgrade are established. A method for establishing a viscoelastic artificial boundary is proposed. Four seismic waves—the Tianjin wave, the El Centro wave, the Taft wave, and the Qian’an wave—are selected as the seismic input waveforms, and only the impact of the lateral ground motion on the wheel-track-subgrade system is considered. In this paper, the ground motion problem is transformed into a wave source problem, the seismic input is transformed into an equivalent load acting on the artificial boundary, and the wave input of the viscoelastic artificial boundary is realized. The normalization method is used to process the seismic waves, and a method that converts the input of the seismic waves into equivalent loads is proposed. The changing laws of different dynamic response indexes under the influence of the four waveforms are studied. Under the action of the Tianjin wave, the wheel-rail dynamic response is very violent near the acceleration peak, whereas, after the peak, all dynamic response indexes are within a safe range. Under the effect of the El Centro wave, the collision between the wheel and the track is relatively violent, and the train is already in a dangerous state. Under the action of the Taft wave, due to the sudden action of the peak ground motion acceleration, the displacement between the wheel and the track increases instantaneously, causing the train to derail. Under the action of the Qian’an wave, the force between the wheel and rail changes approximately linearly with respect to the frequency of the ground motion, and all dynamic response indexes are within a safe range. The vibration intensity of the four seismic waves is amplified by an intensity expansion factor. Except for the Tianjin wave, the amplified seismic wave has a greater impact on safe train operations. This paper can provide a reference for research on the running safety of trains under similar dynamic disturbance conditions.

Considering the unique conditions of deep and large subway foundation pit excavation affected by heavy rainfall in soil-rock composite strata, this paper employs finite element numerical simulation methods to study foundation pit instability under the influence of heavy rainfall. According to the hydraulic coupling conditions caused by rainfall, a fluid-solid coupling numerical model for a deep and large subway foundation pit in soil-rock composite strata is established in this paper. By selecting the Anshan road station of Qingdao subway line 4 as the engineering background, various parameters related to foundation pit excavation affected by heavy rainfall at different excavation depths were analyzed. The study found that after the foundation pit was excavated, the surrounding pore water pressure decreased and the pore water pressure near the ground surface increased rapidly due to rainfall. As the horizontal distance from the foundation pit increased, the pore water pressure at the same depth also increased. The excavation of the foundation pit caused uplift of the bottom of the pit. After rainfall, the uplift value decreased compared with that before rainfall. With increasing excavation depth, the decreased value of the bottom uplift decreased and then increased. The rainfall caused the horizontal displacement of the pit walls on both sides of the pit to increase. When the excavation depth was 10 m, the horizontal displacements on both sides of the pit were equivalent. When the excavation depth was 20 m, the horizontal displacement was concentrated in the first 10 m; when the excavation depths were 30 m and 40 m, the horizontal displacement was concentrated in the first 13 m. This finding shows that when the foundation pit was affected by rainfall, the sidewall collapsed at a distance of 13 meters from the ground. As the excavation depth increased, the depth of excavation instability was closer to the bottom of the pit. The research in this paper can provide a reference for the construction of deep and large foundation pits in similar composite ground conditions that are affected by rainfall.

The coupling of the joint network and groundwater in rock under bias conditions has a significant impact on the deformation and failure of the surrounding rock due to tunnel excavation. This paper studies the deformation and failure of surrounding rock after tunnel excavation under different joint network and groundwater conditions. A finite element-based composite joint network modeling method is proposed in this paper, and the typical parameters of the surrounding rock, such as the plastic zone size, vertical displacement, and lateral displacement, are analyzed and compared through numerical calculations. According to the different stratum and hydraulic conditions considered, four numerical models under four different working conditions are established and studied. The deformation and failure laws of the surrounding rock during tunnel excavation are obtained. The results show that with a single joint network, when there is no influence of groundwater, the surrounding rock mainly undergoes shear failure at the arch crown after tunnel excavation. When the influence of groundwater is considered, there are differences in the mode of damage between the left and right sides of the tunnel. The stratum approximately 1 m from the invert breaks, and the right sidewall fails approximately 1 m from the measuring point. In rock with a composite joint network, when groundwater is not considered, two kinds of failures occur in the surrounding rock near the tunnel; however, the surrounding rock far from the tunnel is dominated by shear failure. The stratum approximately 3.5 m from the arch crown fractures and the surrounding rock within approximately 5.5 m from the measurement point on the right sidewall undergoes separation failure. Under the dual effects of joints and groundwater, soft rock deforms considerably. The total hydraulic gradient decreases from left to right before and after tunnel excavation. The total hydraulic gradient of the composite joint network strata is generally smaller than that of the single joint network. In the composite joint network strata, the total hydraulic gradient near the tunnel changes dramatically. This research can provide a reference for tunnel engineering under similar conditions.

Hyperhomocysteinemia is a well-recognized independent risk factor for cardiovascular disease. To date, the mechanism of pathological plasma homocysteine (Hcy) level elevation remains to be elucidated. We aimed to investigate the levels of progranulin (PGRN), Eph-receptor tyrosine kinase-type A2 (EphA2), vascular cell adhesion molecule-1 (VCAM-1), and Hcy in patients with arteriosclerosis and investigate their functions in Hcy-injured human umbilical vein endothelial cells (HUVECs). EphA2 knockdown was induced in HUVECs by shRNA lentivirus infection with EphA2-RNAi, and bulk RNA-seq assay was performed. Then we investigated the mechanism underlying the effect of recombinant human PGRN (rhPGRN) combined with shRNA interference of EphA2 on cell proliferation, migration, and angiogenesis in Hcy-injured HUVECs. Results showed that serum EphA2, VCAM-1, and Hcy levels in acute coronary syndrome patients were significantly higher than those in chronic coronary syndrome patients (p = 0.000; p = 0.000; p = 0.033, respectively). In vitro, we demonstrated that knockdown of EphA2 significantly impaired cell adhesion and inhibited HUVECs migration and angiogenesis (p < 0.001), which was associated with reduction in VCAM1 and VE-cadherin (p < 0.05). Hcy modulated the expression of PGRN and EphA2 in a time-and dose-dependent manner. However, rhPGRN ameliorated the Hcy-induced reduction in cell viability and migration (p < 0.05). Mechanistically, we found that PGRN/EphA2 and its downstream AKT/NF-κB signaling might be the primary signal transduction pathways underlying Hcy-induced injury. The present study illustrated that PGRN plays a previously unrecognized role in Hcy-induced endothelial injury, which is achieved through its interaction with EphA2 signaling, implying a promising therapeutic target for cardiovascular disease.

A solid oxide fuel cell (SOFC) system is a kind of green chemical-energy–electric-energy conversion equipment with broad application prospects. In order to ensure the long-term stable operation of the SOFC power-generation system, prediction and evaluation of the system’s operating state are required. The mechanism of the SOFC system has not been fully revealed, and data-driven single-step prediction is of little value for practical applications. The state-prediction problem can be regarded as a time series prediction problem. Therefore, an innovative deep learning model for SOFC system state prediction is proposed in this study. The model uses a two-layer LSTM network structure that supports multiple sequence feature inputs and flexible multi-step prediction outputs, which allows multi-step prediction of system states using SOFC system experimental data. Comparing the proposed model with the traditional ARIMA model and LSTM recursive prediction model, it is shown that the multi-step LSTM prediction model performs better than the ARIMA and LSTM recursive prediction models in terms of two evaluation criteria: root mean square error and mean absolute error. Thus, the proposed multi-step LSTM prediction model can effectively and accurately predict and evaluate the SOFC system’s state.

In this study, a data-driven fault diagnosis method was developed for solid oxide fuel cell (SOFC) systems. First, the complete experimental data was obtained following the design of the SOFC system experiments. Then, principal component analysis (PCA) was performed to reduce the dimensionality of the obtained experimental data. Finally, the fault diagnosis algorithms were designed by support vector machine (SVM) and BP neural network to identify and prevent the reformer carbon deposition and heat exchanger rupture faults, respectively. The research results show that both SVM and BP fault diagnosis algorithms can achieve online fault identification. The PCA + SVM algorithm was compared with the SVM algorithm, BP algorithm, and PCA + BP algorithm, and the results show that the PCA + SVM algorithm is superior in terms of running time and accuracy, the diagnosis accuracy reached more than 99%, and the running time was within 20 s. The corresponding system optimization scheme is also proposed.