Efficiency-Oriented Model Predictive Control: A Novel MPC Strategy to Optimize the Global Process Performance.

Existing control strategies, such as Real-time Optimization (RTO), Dynamic Real-time Optimization (DRTO), and Economic Model Predictive Control (EMPC) cannot enable optimal operation and control behavior in an optimal fashion. This work proposes a novel control strategy, named the efficiency-oriented model predictive control (MPC), which can fully realize the potential of the optimization margin to improve the global process performance of the whole system. The ideas of optimization margin and optimization efficiency are first proposed to measure the superiority of the control strategy. Our new efficiency-oriented MPC innovatively uses a nested optimization structure to optimize the optimization margin directly online. To realize the computation, a Periodic Approximation technique is proposed, and an Efficiency-Oriented MPC Type I is constructed based on the Periodic Approximation. In order to alleviate the strict constraint of Efficiency-Oriented MPC Type I, the zone-control-based optimization concept is used to construct an Efficiency-Oriented MPC Type II. These two well-designed efficiency-oriented controllers were compared with other control strategies over a Continuous Stirred Tank Reactor (CSTR) application. The simulation results show that the proposed control strategy can generate superior closed-loop process performance, for example, and the Efficiency-Oriented MPC Type I can obtain 7.11% higher profits than those of other control strategies; the effectiveness of the efficiency-oriented MPC was, thereby, demonstrated.

Effects of Long-Term Exposure to Ambient Formaldehyde on Hypertension and Angina Pectoris Symptoms: Evidence From the WHO SAGE Cohort Study.

BACKGROUND: We aimed to investigate the associations of long-term exposure to ambient formaldehyde with hypertension and angina pectoris symptoms in Chinese adults. METHODS AND RESULTS: Participants' information was obtained from the WHO SAGE (World Health Organization Study on Global Aging and Adult Health) study. The Cox proportional hazards regression model was applied to estimate the associations of formaldehyde with hypertension and angina pectoris symptoms. Mediating effect analysis was used to investigate the mediating effect of hypertension between formaldehyde exposure and angina pectoris symptoms. Long-term exposure to formaldehyde was positively associated with the risk of angina pectoris symptoms (hazard ratio [HR], 1.66 [95% CI, 1.29-2.13], per interquartile range [IQR], 3.33, 1015 molecules/cm2) and hypertension (HR, 1.17 [95% CI, 1.02-1.34], per IQR, 3.34, 1015 molecules/cm2). The associations between formaldehyde and angina pectoris symptoms were greater in participants aged ≥65 years (HR, 1.90 [95% CI, 1.29-2.80]) and in rural areas (HR, 2.71 [95% CI, 1.54-4.77]), whereas the associations of formaldehyde with hypertension were stronger in men (HR, 1.27 [95% CI, 1.02-1.58]), rural areas (HR, 1.22 [95% CI, 0.94-1.59]), and in ever smokers (HR, 1.33 [95% CI, 1.02-1.72]). The mediation effect analysis indicated that 18.44% (95% CI, 2.17-37.65) of the association between formaldehyde exposure and angina pectoris symptoms was mediated by hypertension. CONCLUSIONS: Long-term exposure to ambient formaldehyde was positively associated with hypertension and angina pectoris symptoms. The effects of formaldehyde may be modified by age, sex, urbanicity, and smoking status. Hypertension might play a mediating effect in formaldehyde-induced angina pectoris symptoms.

The association between long-term exposure to ambient formaldehyde and respiratory mortality risk: A national study in China.

INTRODUCTION: While ambient formaldehyde (HCHO) concentrations are increasing worldwide, there was limited research on its health effects. OBJECTIVES: To assess the association of long-term exposure to ambient HCHO with the risk of respiratory (RESP) mortality and the associated mortality burden in China. METHODS: Annual and seasonal RESP death and tropospheric HCHO vertical columns data were collected in 466 counties/districts across China during 2013-2016. A difference-in-differences approach combined with a generalized linear mixed-effects regression model was employed to assess the exposure-response association between long-term ambient HCHO exposure and RESP mortality risk. Additionally, we computed the attributable fraction (AF) to gauge the proportion of RESP mortality attributable to HCHO exposure. RESULTS: This analysis encompassed 560,929 RESP deaths. The annual mean ambient HCHO concentration across selected counties/districts was 8.02×1015 ± 2.22×1015 molec.cm-2 during 2013-2016. Each 1.00×1015 molec.cm-2 increase in ambient HCHO was associated with a 1.61 % increase [excess risk (ER), 95 % confidence interval (CI): 1.20 %, 2.03 %] in the RESP mortality risk. The AF of RESP mortality attributable to HCHO was 12.16 % (95 %CI:9.33 %, 14.88 %), resulting in an annual average of 125,422 (95 %CI:96,404, 153,410) attributable deaths in China. Stratified analyses suggested stronger associations in individuals aged ≥65 years old (ER=1.87 %, 95 %CI:1.43 %, 2.32 %), in cold seasons (ER=1.00 %, 95 %CI:0.56 %, 1.44 %), in urban areas (ER=1.65 %, 95 %CI:1.15 %, 2.16 %), and in chronic obstructive pulmonary disease patients (ER=1.95 %, 95 %CI:1.42 %, 2.48 %). CONCLUSIONS: This study suggested that long-term HCHO exposure may significantly increase the risk of RESP mortality, leading to a substantial mortality burden. Targeted measures should be implemented to control ambient HCHO pollution promptly.

Weighted ultrasound entropy imaging for HIFU ablation monitoring with improved sensitivity and contrast.

BACKGROUND: Although B-mode imaging has been widely used in ultrasound-guided high-intensity focused ultrasound (HIFU) treatment, challenges remain in improving its quality and sensitivity for monitoring the thermal dose. Recently, quantitative ultrasound (QUS) imaging has been recognized with the potential to better sense the changes in the microstructure of ablated tissues. PURPOSE: This study proposed to use a QUS method called weighted ultrasound entropy (WUE) imaging to monitor the HIFU ablation. METHODS: Based on ex-vivo and in-vivo experiments, WUE images reflecting tissue changes during HIFU treatment under different acoustic power levels (174-308 W) were reconstructed with a newly established imaging framework. The performance of the proposed WUE imaging in the monitoring of HIFU treatment was compared with the corresponding B-mode images in terms of their contrast-to-noise ratios (CNRs) between the focal region and the background. RESULTS: It was found that HIFU irradiation with higher power generated larger WUE values in the focal region, and the bright spots grew in size as the acoustic sonication proceeded. Compared with the in-situ B-mode images, the WUE images had higher image quality in indicating lesion formation, with a 39.2%-53.4% improvement in the CNR at different stages. Meanwhile, a correlation (R = 0.84) between the damage area estimated in WUE images and that measured from the dissected ex-vivo tissue samples was found. CONCLUSIONS: WUE imaging is more sensitive and accurate than B-mode imaging in monitoring HIFU therapy. These findings suggest that WUE imaging could be a promising technique for assisting ultrasound-guided HIFU ablation.

Suppressing the HIFU interference in ultrasound guiding images with a diffusion-based deep learning model.

BACKGROUND AND OBJECTIVES: In ultrasound guided high-intensity focused ultrasound (HIFU) surgery, it is necessary to transmit sound waves at different frequencies simultaneously using two transducers: one for the HIFU therapy and another for the ultrasound imaging guidance. In this specific setting, real-time monitoring of non-invasive surgery is challenging due to severe contamination of the ultrasound guiding images by strong acoustic interference from the HIFU sonication. METHODS: This paper proposed the use of a deep learning (DL) solution, specifically a diffusion implicit model, to suppress the HIFU interference. We considered the images contaminated with HIFU interference as low-resolution images, and those free from interference as high-resolution. While suppressing HIFU interference using the diffusion implicit (HIFU-Diff) model, the task was transformed into generating a high-resolution image through a series of forward diffusion steps and reverse sampling. A series of ex-vivo and in-vivo experiments, conducted under various parameters, were designed to validate the performance of the proposed network. RESULTS: Quantitative evaluation and statistical analysis demonstrated that the HIFU-Diff network achieved superior performance in reconstructing interference-free images under a variety of ex-vivo and in-vivo conditions, compared to the most commonly used notch filtering and the recent 1D FUS-Net deep learning network. The HIFU-Diff maintains high performance with 'unseen' datasets from separate experiments, and its superiority is more pronounced under strong HIFU interferences and in complex in-vivo situations. Furthermore, the reconstructed interference-free images can also be used for quantitative attenuation imaging, indicating that the network preserves acoustic characteristics of the ultrasound images. CONCLUSIONS: With the proposed technique, HIFU therapy and the ultrasound imaging can be conducted simultaneously, allowing for real-time monitoring of the treatment process. This capability could significantly enhance the safety and efficacy of the non-invasive treatment across various clinical applications. To the best of our knowledge, this is the first diffusion-based model developed for HIFU interference suppression.

Association of Residential Greenness Exposure with Depression Incidence in Adults 50 Years of Age and Older: Findings from the Cohort Study on Global AGEing and Adult Health (SAGE) in China.

BACKGROUND: Depression is a social and public health problem of great concern globally. Identifying and managing the factors influencing depression are crucial for preventing and decreasing the burden of depression. OBJECTIVES: Our objectives are to explore the association between residential greenness and the incidence of depression in an older Chinese population and to calculate the disease burden of depression prevented by greenness exposure. METHODS: This study was the Chinese part of the World Health Organization Study on Global AGEing and Adult Health (WHO SAGE). We collected the data of 8,481 residents ≥50 years of age in China for the period 2007-2018. Average follow-up duration was 7.00 (±2.51) years. Each participant was matched to the yearly maximum normalized difference vegetation index (NDVI) at their residential address. Incidence of depression was assessed using the Composite International Diagnostic Interview (CIDI), self-reports of depression, and/or taking depression medication. Association between greenness and depression was examined using the time-dependent Cox regression model with stratified analysis by sex, age, urbanicity, annual family income, region, smoking, drinking, and household cooking fuels. Furthermore, the prevented fraction (PF) and attributable number (AN) of depression prevented by exposure to greenness were estimated. RESULTS: Residential greenness was negatively associated with depression. Each interquartile range (IQR) increase in NDVI 500-m buffer was associated with a 40% decrease [hazard ratio (HR)=0.60; 95% confidence interval (CI): 0.37, 0.97] in the risk of depression incidence among the total participants. Subgroup analyses showed negative associations in urban residents (HR=0.32; 95% CI: 0.12, 0.86) vs. rural residents, in high-income residents (HR=0.28; 95% CI: 0.11, 0.71) vs. low-income residents, and in southern China (HR=0.50; 95% CI: 0.26, 0.95) vs. northern China. Over 8.0% (PF=8.69%; 95% CI: 1.38%, 15.40%) and 1,955,199 (95% CI: 310,492; 3,464,909) new cases of depression may be avoided by increasing greenness exposures annually across China. DISCUSSION: The findings suggest protective effects of residential greenness exposure on depression incidence in the older population, particularly among urban residents, high-income residents, and participants living in southern China. The construction of residential greenness should be included in community planning. https://doi.org/10.1289/EHP13947.

Exercise training decreases lactylation and prevents myocardial ischemia-reperfusion injury by inhibiting YTHDF2.

Exercise improves cardiac function and metabolism. Although long-term exercise leads to circulating and micro-environmental metabolic changes, the effect of exercise on protein post-translational lactylation modifications as well as its functional relevance is unclear. Here, we report that lactate can regulate cardiomyocyte changes by improving protein lactylation levels and elevating intracellular N6-methyladenosine RNA-binding protein YTHDF2. The intrinsic disorder region of YTHDF2 but not the RNA m6A-binding activity is indispensable for its regulatory function in influencing cardiomyocyte cell size changes and oxygen glucose deprivation/re-oxygenation (OGD/R)-stimulated apoptosis via upregulating Ras GTPase-activating protein-binding protein 1 (G3BP1). Downregulation of YTHDF2 is required for exercise-induced physiological cardiac hypertrophy. Moreover, myocardial YTHDF2 inhibition alleviated ischemia/reperfusion-induced acute injury and pathological remodeling. Our results here link lactate and lactylation modifications with RNA m6A reader YTHDF2 and highlight the physiological importance of this innovative post-transcriptional intrinsic regulation mechanism of cardiomyocyte responses to exercise. Decreasing lactylation or inhibiting YTHDF2/G3BP1 might represent a promising therapeutic strategy for cardiac diseases.

Frequency-Domain Robust PCA for Real-Time Monitoring of HIFU Treatment.

High intensity focused ultrasound (HIFU) is a thriving non-invasive technique for thermal ablation of tumors, but significant challenges remain in its real-time monitoring with medical imaging. Ultrasound imaging is one of the main imaging modalities for monitoring HIFU surgery in organs other than the brain, mainly due to its good temporal resolution. However, strong acoustic interference from HIFU irradiation severely obscures the B-mode images and compromises the monitoring. To address this problem, we proposed a frequency-domain robust principal component analysis (FRPCA) method to separate the HIFU interference from the contaminated B-mode images. Ex-vivo and in-vivo experiments were conducted to validate the proposed method based on a clinical HIFU therapy system combined with an ultrasound imaging platform. The performance of the FRPCA method was compared with the conventional notch filtering method. Results demonstrated that the FRPCA method can effectively remove HIFU interference from the B-mode images, which allowed HIFU-induced grayscale changes at the focal region to be recovered. Compared to notch-filtered images, the FRPCA-processed images showed an 8.9% improvement in terms of the structural similarity (SSIM) index to the uncontaminated B-mode images. These findings demonstrate that the FRPCA method presents an effective signal processing framework to remove the strong HIFU acoustic interference, obtains better dynamic visualization in monitoring the HIFU irradiation process, and offers great potential to improve the efficacy and safety of HIFU treatment and other focused ultrasound related applications.

Analysis of the chain mediation effect between intergenerational support and mental health of older adults in urban China: a structural equation model.

BACKGROUND: This study aimed investigate the impact of intergenerational support on the mental health of older adults in urban China. It also sought to evaluate the chain mediation effect of attitudes toward younger people and willingness to interact with younger people within a non-familial context between intergenerational support and mental health. METHODS: Data were derived from a community survey that adopted quota sampling in mainland China in 2022 (N = 780). Structural equation modeling was used to analyze the data, and the bootstrap technique was used to test the mediation effect. RESULTS: A significant positive association was found between intergenerational support and the mental health of older adults in urban China (B = 0.852, 95% confidence interval CI [0.157,1.617]). Intergenerational support had a specific indirect effect on mental health through older adults' attitudes toward younger people within a non-familial context (B = 0.665, 95% CI [0.443,1.046]). There was a chain mediation effect (B = 0.126, 95% CI [0.069,0.224]) in relation to attitudes toward younger people and the willingness to interact with younger people between intergenerational support and mental health. Mediation accounted for 44.44% of the total effects in the model. CONCLUSION: These findings help identify modifiable factors that can improve the mental health of older adults. In line with the proposed serial multiple mediation model, this study provides theoretical and practical insights concerning the synergistic effect of intergenerational support at the family level and intergenerational interaction at the community level. Policy and social service implications are also discussed.

AbDPP: Target-oriented antibody design with pretraining and prior biological structure knowledge.

Antibodies represent a crucial class of complex protein therapeutics and are essential in the treatment of a wide range of human diseases. Traditional antibody discovery methods, such as hybridoma and phage display technologies, suffer from limitations including inefficiency and a restricted exploration of the immense space of potential antibodies. To overcome these limitations, we propose a novel method for generating antibody sequences using deep learning algorithms called AbDPP (target-oriented antibody design with pretraining and prior biological knowledge). AbDPP integrates a pretrained model for antibodies with biological region information, enabling the effective use of vast antibody sequence data and intricate biological system understanding to generate sequences. To target specific antigens, AbDPP incorporates an antibody property evaluation model, which is further optimized based on evaluation results to generate more focused sequences. The efficacy of AbDPP was assessed through multiple experiments, evaluating its ability to generate amino acids, improve neutralization and binding, maintain sequence consistency, and improve sequence diversity. Results demonstrated that AbDPP outperformed other methods in terms of the performance and quality of generated sequences, showcasing its potential to enhance antibody design and screening efficiency. In summary, this study contributes to the field by offering an innovative deep learning-based method for antibody generation, addressing some limitations of traditional approaches, and underscoring the importance of integrating a specific antibody pretrained model and the biological properties of antibodies in generating novel sequences. The code and documentation underlying this article are freely available at https://github.com/zlfyj/AbDPP.

Effects of long-term exposure to ambient fine particulate matter and its specific components on blood pressure and hypertension incidence.

BACKGROUND: Epidemiological evidence on the association of PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 µm) and its specific components with hypertension and blood pressure is limited. METHODS: We applied information of participants from the World Health Organization's (WHO) Study on Global Ageing and Adult Health (SAGE) to estimate the associations of long-term PM2.5 mass and its chemical components exposure with blood pressure (BP) and hypertension incidence in Chinese adults ≥ 50 years during 2007-2018. Generalized linear mixed model and Cox proportional hazard model were applied to investigate the effects of PM2.5 mass and its chemical components on the incidence of hypertension and BP, respectively. RESULTS: Each interquartile range (IQR = 16.80 µg/m3) increase in the one-year average of PM2.5 mass concentration was associated with a 17 % increase in the risk of hypertension (HR = 1.17, 95 % CI: 1.10, 1.24), and the population attributable fraction (PAF) was 23.44 % (95 % CI: 14.69 %, 31.55 %). Each IQR µg/m3 increase in PM2.5 exposure was also related to increases of systolic blood pressure (SBP) by 2.54 mmHg (95 % CI:1.99, 3.10), and of diastolic blood pressure (DBP) by 1.36 mmHg (95 % CI: 1.04, 1.68). Additionally, the chemical components of SO42-, NO3-, NH4+, OM, and BC were also positively associated with an increased risk of hypertension incidence and elevated blood pressure. CONCLUSIONS: These results indicate that long-term exposure to PM2.5 mass and its specific components may be major drivers of escalation in hypertension diseases.

Suppressing HIFU interference in ultrasound images using 1D U-Net-based neural networks.

Objective.One big challenge with high-intensity focused ultrasound (HIFU) is that the intense acoustic interference generated by HIFU irradiation overwhelms the B-mode monitoring images, compromising monitoring effectiveness. This study aims to overcome this problem using a one-dimensional (1D) deep convolutional neural network.Approach. U-Net-based networks have been proven to be effective in image reconstruction and denoising, and the two-dimensional (2D) U-Net has already been investigated for suppressing HIFU interference in ultrasound monitoring images. In this study, we propose that the one-dimensional (1D) convolution in U-Net-based networks is more suitable for removing HIFU artifacts and can better recover the contaminated B-mode images compared to 2D convolution.Ex vivoandinvivoHIFU experiments were performed on a clinically equivalent ultrasound-guided HIFU platform to collect image data, and the 1D convolution in U-Net, Attention U-Net, U-Net++, and FUS-Net was applied to verify our proposal.Main results.All 1D U-Net-based networks were more effective in suppressing HIFU interference than their 2D counterparts, with over 30% improvement in terms of structural similarity (SSIM) to the uncontaminated B-mode images. Additionally, 1D U-Nets trained usingex vivodatasets demonstrated better generalization performance ininvivoexperiments.Significance.These findings indicate that the utilization of 1D convolution in U-Net-based networks offers great potential in addressing the challenges of monitoring in ultrasound-guided HIFU systems.

Corrigendum to "Engineering exosomes and their application in cardiovascular field: Bibliometric analysis from 2002 to 2022" [Heliyon 9(8) (August 2023) e18809].

[This corrects the article DOI: 10.1016/j.heliyon.2023.e18809.].

Joint Effects of Long-Term Exposure to Ambient Fine Particulate Matter and Ozone on Asthmatic Symptoms: Prospective Cohort Study.

BACKGROUND: The associations of long-term exposure to air pollutants in the presence of asthmatic symptoms remain inconclusive and the joint effects of air pollutants as a mixture are unclear. OBJECTIVE: We aimed to investigate the individual and joint associations of long-term exposure to ambient fine particulate matter (PM2.5) and daily 8-hour maximum ozone concentrations (MDA8 O3) in the presence of asthmatic symptoms in Chinese adults. METHODS: Data were derived from the World Health Organization Study on Global Ageing and Adult Health (WHO SAGE) cohort study among adults aged 50 years or older, which was implemented in 1 municipality and 7 provinces across China during 2007-2018. Annual average MDA8 O3 and PM2.5 at individual residential addresses were estimated by an iterative random forest model and a satellite-based spatiotemporal model, respectively. Participants who were diagnosed with asthma by a doctor or taking asthma-related therapies or experiencing related conditions within the past 12 months were recorded as having asthmatic symptoms. The individual associations of PM2.5 and MDA8 O3 with asthmatic symptoms were estimated by a Cox proportional hazards regression model, and the joint association was estimated by a quantile g-computation model. A series of subgroup analyses was applied to examine the potential modifications of some characteristics. We also calculated the population-attributable fraction (PAF) of asthmatic symptoms attributed to PM2.5 and MDA8 O3. RESULTS: A total of 8490 adults older than 50 years were included, and the average follow-up duration was 6.9 years. During the follow-up periods, 586 (6.9%) participants reported asthmatic symptoms. Individual effect analyses showed that the risk of asthmatic symptoms was positively associated with MDA8 O3 (hazard ratio [HR] 1.12, 95% CI 1.01-1.24, for per quantile) and PM2.5 (HR 1.18, 95% CI 1.05-1.31, for per quantile). Joint effect analyses showed that per equal quantile increment of MDA8 O3 and PM2.5 was associated with an 18% (HR 1.18, 95% CI 1.05-1.33) increase in the risk of asthmatic symptoms, and PM2.5 contributed more (68%) in the joint effects. The individual PAFs of asthmatic symptoms attributable to PM2.5 and MDA8 O3 were 2.86% (95% CI 0.17%-5.50%) and 4.83% (95% CI 1.42%-7.25%), respectively, while the joint PAF of asthmatic symptoms attributable to exposure mixture was 4.32% (95% CI 1.10%-7.46%). The joint associations were greater in participants with obesity, in urban areas, with lower family income, and who used unclean household cooking fuel. CONCLUSIONS: Long-term exposure to PM2.5 and MDA8 O3 may individually and jointly increase the risk of asthmatic symptoms, and the joint effects were smaller than the sum of individual effects. These findings informed the importance of joint associations of long-term exposure to air pollutants with asthma.

Long-Term Exposure to Ambient PM2.5 and Age-Related Cataracts among Chinese Middle-Aged and Older Adults: Evidence from Two National Cohort Studies.

Cataract is one key cause of visual disability and blindness. Ambient particulate matter is more likely to increase cataract risk due to eye continuous exposure to the environment. However, less is known about whether long-term exposure to particulate matter 2.5 (PM2.5) is related to age-related cataracts. We conducted a population-based study among 22,298 adults from two multicenter cohort studies [China Family Panel Studies (CFPS) and Chinese Longitudinal Healthy Longevity Survey (CLHLS)]. The associations between PM2.5 and age-related cataracts were analyzed by Cox proportional hazard regression models, which were also stratified according to demographic characteristics. The restricted cubic spline (RCS) model was used to explore the dose-response relationships between PM2.5 and age-related cataracts. The population attributable fraction (PAF) was calculated to assess the burden of age-related cataracts that can be attributed to PM2.5. In the final analysis, 1897 participants reported age-related cataracts during follow-up. Long-term exposure to PM2.5 was associated with age-related cataracts, with HRs of 1.165 (1.130, 1.201), 1.138 (1.103, 1.173), and 1.091 (1.057, 1.126) for per 10 µg/m3 increase at one-, two-, and three-year before the end of follow-up, respectively. Furthermore, associations between PM2.5 and age-related cataracts were also demonstrated in RCS models. The PAF of age-related cataracts to PM2.5 in the total participants was 24.63%. Our research found that long-term exposure to PM2.5 may increase the risk of age-related cataracts, and age-related cataracts should be considered as an important public health issue due to air pollution.

Engineering exosomes and their application in cardiovascular field: Bibliometric analysis from 2002 to 2022.

Cardiovascular disease (CVD) is the leading cause of death around the world, warranting an increasing number of studies for its treatment. Among all of its therapeutical strategies, engineered exosomes are attracting growing attention due to their excellent biocompatibility, non-immunogenicity, and favorable plasticity. Despite its increasing popularity, there is yet to be a bibliometric analysis regarding the application of exosomes in CVD treatment. Therefore, the present study assessed the current trends in engineered exosomes in treating CVD by conducting a bibliometric analysis. All associated literatures published between years 2002-2022 were collected, through the Web of Science Core Collection. Our results showed that related studies robustly increased in 2020, followed by a gradual increase from 2020 to 2022, indicating that this field attracted growing attention. Additionally, we described critical network of countries, institutions, authors, top-cited references, and keywords. The present bibliometric study provides systematic observations on engineering exosomes in treating CVD, reveals potential challenges and future direction for additional studies, and may inspire more researchers to commit to investigating treatments for CVD.

Extracellular Vesicles and Vascular Inflammation.

Vascular inflammation is the most common pathological feature in the pathogenesis of human disease. It is a complex immune process involved with many different types of cells including platelet, monocytes, macrophages, endothelial cells, and others. It is widely accepted that both innate and adaptive immune responses are important for the initiation and progression of vascular inflammation. The cell-cell interaction constitutes an important aspect of those immune responses in the vascular inflammation. Extracellular vesicles (EVs) are nanometer-sized double-layer lipid membrane vesicles released from most types of cells. They have been proved to play critical roles in intercellular communication in the occurrence and development of multisystem diseases. With the advancement of basal medical science, the biological roles of EVs in vascular inflammation have been clearer today. In this chapter, we will summarize the advance progress of extracellular vesicles in regulating vascular inflammation and its potential application in the clinical.

Dynamic Event-Driven Finite-Horizon Optimal Consensus Control for Constrained Multiagent Systems.

This article investigates the event-driven finite-horizon optimal consensus control problem for multiagent systems with symmetric or asymmetric input constraints. Initially, in order to overcome the difficulty that the Hamilton-Jacobi-Bellman equation is time-varying in finite-horizon optimal control, a single critic neural network (NN) with time-varying activation function is applied to obtain the approximate optimal control. Meanwhile, for minimizing the terminal error to satisfy the terminal constraint of the value function, an augmented error vector containing the Bellman residual and the terminal error is constructed to update the weight of the NN. Furthermore, an improved learning law is proposed, which relaxes the tricky persistence excitation condition and eliminates the requirement of initial stability control. Moreover, a specific algorithm is designed to update the historical dataset, which can effectively accelerate the convergence rate of network weight. In addition, to improve the utilization rate of the communication resource, an effective dynamic event-triggering mechanism (DETM) composed of dynamic threshold parameters (DTPs) and auxiliary dynamic variables (ADVs) is designed, which is more flexible compared with the ADV-based DETM or DTP-based DETM. Finally, to support the effectiveness of the proposed method and the superiority of the designed DETM, a simulation example is provided.

RNA m6A-Regulated circ-ZNF609 Suppression Ameliorates Doxorubicin-Induced Cardiotoxicity by Upregulating FTO.

Cardiac death is a major burden for cancer survivors, yet there is currently no effective treatment for doxorubicin (DOX)-induced cardiotoxicity. Here, we report that circ-ZNF609 knockdown knockdown had cardioprotective effects against DOX-induced cardiomyocyte toxicity. Mechanistically, circ-ZNF609 knockdown alleviated DOX-induced cardiotoxicity through attenuating cardiomyocyte apoptosis, reducing reactive oxygen species production, ameliorating mitochondrial nonheme iron overload. circ-ZNF609 inhibition blocked the elevation of RNA N6-methyladenosine (RNA m6A) methylation level in DOX-treated mice hearts, whereas m6A demethylase fat mass and obesity associated (FTO) acted as the downstream factor of circ-ZNF609. Moreover, the stability of circ-ZNF609 was regulated by RNA m6A methylation alteration, and suppression of RNA m6A methylation by methyltransferase like 14 (METTL14) modulated the function of circ-ZNF609. These data suggest that circ-ZNF609 inhibition represents a potential therapy for DOX-induced cardiotoxicity.

Diagnosis of acute myocardial infarction using a combination of circulating circular RNA cZNF292 and clinical information based on machine learning.

Circulating circular RNAs (circRNAs) are emerging as novel biomarkers for cardiovascular diseases (CVDs). Machine learning can provide optimal predictions on the diagnosis of diseases. Here we performed a proof-of-concept study to determine if combining circRNAs with an artificial intelligence approach works in diagnosing CVD. We used acute myocardial infarction (AMI) as a model setup to prove the claim. We determined the expression level of five hypoxia-induced circRNAs, including cZNF292, cAFF1, cDENND4C, cTHSD1, and cSRSF4, in the whole blood of coronary angiography positive AMI and negative non-AMI patients. Based on feature selection by using lasso with 10-fold cross validation, prediction model by logistic regression, and ROC curve analysis, we found that cZNF292 combined with clinical information (CM), including age, gender, body mass index, heart rate, and diastolic blood pressure, can predict AMI effectively. In a validation cohort, CM + cZNF292 can separate AMI and non-AMI patients, unstable angina and AMI patients, acute coronary syndromes (ACS), and non-ACS patients. RNA stability study demonstrated that cZNF292 was stable. Knockdown of cZNF292 in endothelial cells or cardiomyocytes showed anti-apoptosis effects in oxygen glucose deprivation/reoxygenation. Thus, we identify circulating cZNF292 as a potential biomarker for AMI and construct a prediction model "CM + cZNF292."