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Background: Cardiovascular disease (CVD) has been the leading cause of death worldwide for many years. In recent years, exosomes have gained extensive attention in the cardiovascular system due to their excellent biocompatibility. Studies have extensively researched miRNAs in exosomes and found that they play critical roles in various physiological and pathological processes in the cardiovascular system. These processes include promoting or inhibiting inflammatory responses, promoting angiogenesis, participating in cell proliferation and migration, and promoting pathological progression such as fibrosis. Aim of review: This systematic review examines the role of exosomes in various cardiovascular diseases such as atherosclerosis, myocardial infarction, ischemia–reperfusion injury, heart failure and cardiomyopathy. It also presents the latest treatment and prevention methods utilizing exosomes. The study aims to provide new insights and approaches for preventing and treating cardiovascular diseases by exploring the relationship between exosomes and these conditions. Furthermore, the review emphasizes the potential clinical use of exosomes as biomarkers for diagnosing cardiovascular diseases. Key scientific concepts of review: Exosomes are nanoscale vesicles surrounded by lipid bilayers that are secreted by most cells in the body. They are heterogeneous, varying in size and composition, with a diameter typically ranging from 40 to 160 nm. Exosomes serve as a means of information communication between cells, carrying various biologically active substances, including lipids, proteins, and small RNAs such as miRNAs and lncRNAs. As a result, they participate in both physiological and pathological processes within the body.

Abstract Background The purpose of this study was to investigate the relationships between generalized, abdominal, and visceral fat obesity and the prevalence of gout in patients with type 2 diabetes mellitus (T2DM). Methods Data were obtained from the electronic medical databases of the National Metabolic Management Center (MMC) of Yuhuan Second People’s Hospital and Taizhou Central Hospital (Taizhou University Hospital) between September 2017 and June 2023. Four obesity indicators were analyzed: waist circumference (WC), waist-to-hip ratio (WHR), body mass index (BMI), and visceral fat area (VFA). The relationships between these parameters and gout prevalence were analyzed using multivariate logistic regression and restricted cubic spline (RCS) analyses. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of the four parameters for gout. Results This cross-sectional study enrolled 10,535 participants (600 cases and 9,935 controls). Obesity was more common in patients with gout, and the obesity indicators were markedly higher in this group. After adjustment for confounders, obesity, as defined by BMI, WC, WHR, and VFA, was found to be associated with greater gout prevalence, with odds ratios (OR) of 1.775, 1.691, 1.858, and 1.578, respectively (P

Cardiovascular diseases (CVDs) have a complex pathogenesis and pose a major threat to human health. Cardiomyocytes have a low regenerative capacity, and their death is a key factor in the morbidity and mortality of many CVDs. Cardiomyocyte death can be regulated by specific signaling pathways known as programmed cell death (PCD), including apoptosis, necroptosis, autophagy, pyroptosis, and ferroptosis, etc. Abnormalities in PCD can lead to the development of a variety of cardiovascular diseases, and there are also molecular-level interconnections between different PCD pathways under the same cardiovascular disease model. Currently, the link between programmed cell death in cardiomyocytes and cardiovascular disease is not fully understood. This review describes the molecular mechanisms of programmed death and the impact of cardiomyocyte death on cardiovascular disease development. Emphasis is placed on a summary of drugs and potential therapeutic approaches that can be used to treat cardiovascular disease by targeting and blocking programmed cell death in cardiomyocytes.

Abstract As the scale of water conservancy projects continues to expand, the amount and complexity of analytical data have also correspondingly increased. At present, it is difficult to realize project management decision support based on a single data source, and most manual analysis methods not only have high labor costs, but also are prone to the risk of misjudgment, resulting in huge property losses. Based on this problem, this paper proposes visual analysis method for unmanned pumping stations on dynamic platforms based on data fusion technology. First, the method uses the transfer learning method to enable ResNet18 obtain generalization ability. Secondly, the method uses ResNet18 to extract image features, and outputs fixed length sequence data as the input of long short-term memory (LSTM). Finally, the method uses LSTM outputs the classification results. The experimental results demonstrate that the algorithm model can achieve an impressive accuracy of 99.032%, outperforming the combination of traditional feature extraction and machine learning methods. This model effectively recognizes and classifies images of pumping stations, significantly reducing the risk of accidents in these facilities.

With the increasing diversity and complexity of material requirements for underground water conservancy and water pipeline networks, the efficient and convenient design of special materials to meet individual needs through machine learning has become a hot topic of concern.Traditional supervised learning methods are all based on a large dataset to train models, but obtaining large datasets for special materials required in deeply buried underground water pipeline networks and high-end military equipment, such as rare and high-entropy alloys, etc.requires extremely high cost and a long period.To solve this problem, we propose a small sample expansion model-RX-SMOGN, using XGBoost and RFECV algorithms for feature screening.We enrich the dataset with the SMOGN algorithm.In this paper, the phase structure of high-entropy alloys is used as the research object, and traditional machine learning models are trained to predict them to verify the effectiveness of the RX-SMOGN model.From the results of 5-fold cross-verification and 4 evaluation indicators, it can be seen that the RX-SMOGN model fully improves the performance of the machine learning model, provides a more convenient method for alloy material design, and fully improves the efficiency of alloy material design.

With the increasing number of electric vehicles taking to the roads, the impact of tailpipe emissions on air quality will decrease, while resuspended road dust and brake/tire wear will become more significant. This study quantified PM10 emissions from tire wear under a range of real highway conditions with measurements across different seasons and roadway surface types in Phoenix, Arizona. Tire wear was quantified in the sampled PM10 using benzothiazoles (vulcanization accelerators) as tire markers. The measured emission factors had a range of 0.005–0.22 mg km−1 veh−1 and are consistent with an earlier experimental study conducted in Phoenix. However, these results are lower than values typically found in the literature and values calculated from emissions models, such as MOVES (MOtor Vehicle Emission Simulator). We found no significant difference in tire wear PM10 emission factors for different surface types (asphalt vs. diamond grind concrete) but saw a significant decrease in the winter compared to the summer.

Occupational exposure to airborne nanoparticles in semiconductor-manufacturing facilities is of growing concern. Currently, comprehensive information regarding atmospheric concentrations, potential origins, and the physical and chemical properties of nanoparticles in these industrial settings is lacking. This study investigated the occurrence of airborne nanoparticles within a semiconductor-research and -manufacturing facility, during both routine operation and maintenance activities. A Scanning Mobility Particle Sizer was used to monitor size-resolved airborne-nanoparticle number concentrations spanning the range of 6 to 220 nm. Breathing zone filter samples were also collected during maintenance processes and underwent subsequent analyses via Transmission Electron Microscopy and Inductively Coupled Plasma Mass Spectrometry, to discover the size, morphology, and chemical composition of the observed nanoparticles. The findings reveal low levels of airborne nanoparticles during routine operations, but maintenance tasks resulted in substantial concentration surges particularly for plasma-enhanced chemical vapor deposition tools with concentrations up to 11,800 particles/cm3. More than 80% of observed particles were smaller than 30 nm. These smallest particles were predominately composed of metals such as iron, nickel, and copper. Moreover, larger particles above 100 nm were also identified, comprising process-related materials such as silicon and indium. Comparative assessment against established mass-based exposure limits did not yield any exceedances. Current exposure limits do not typically consider size though, and the preponderance of small nanoparticles (

The emergence of facial masks as a critical health intervention to prevent the spread of airborne disease and protect from occupational nanomaterial exposure highlights the need for fundamental insights into the interaction of nanoparticles (f) in the range of −10 V to −800 V were measured by an electrostatic voltmeter and used for testing. Results show that the mask filtration efficiency is highly dependent on the mask surface potential as well as the charge on the challenge aerosol, ranging from 60% to 98%. Eliminating the surface charge results in a maximum 43% decrease in filtration efficiency, emphasizing the importance of electrostatic charge interactions during the particle capture process. Moreover, increased humidity can decrease the surface charge on filters, thereby decreasing the mask filtration efficiency. The knowledge gained from this study provides insight into the critical role of electrostatic attraction in nanoparticle capture mechanisms and benefits future occupational and environmental health studies.

The stay cable is one of the most critical structural components of a cable dome structure. However, during its service life, it may lose its stiffness due to environmental factors and metal fatigue, thus making the structure a safety hazard. As the most important mechanical physical parameter of the cable, it is necessary to create a health-monitoring method to ensure the safety of the structure. In this study, a smart cable with a fiber optic Bragg grating (FBG) sensor is proposed. The sensor is embedded in the Z-shaped cable of the stay cable to ensure the simultaneous deformation of the sensor and cable. The monitoring of the cable force can be achieved after obtaining the relationship coefficient between the sensor and the cable force. In the rest of the paper, the sensing principle and fabrication procedure are described. A series of tests are conducted to verify the sensing performance of the smart cable. Finally, the dynamic monitoring and long-term monitoring of the cable force in the cable-supported grid system of Dalian Suoyuwan Football Stadium are carried out by using the smart cable, and the stability and safety of the structure are evaluated by the monitoring results.

The primary objective of this study was to systematically evaluate the clinical efficacy of intravitreal ranibizumab injection in the treatment for retinopathy of prematurity (ROP) in infants. The MEDLINE (PubMed), Embase, China Biology Medicine disc, Cochrane Library, Web of Science, WanFang Data, CNKI, and CQVIP databases were searched to collect randomized controlled trials (RCTs) comparing the efficacy of ranibizumab with laser treatment in ROP. The retrieval time was from 2007, on which ranibizumab was approved until 12 January 2022. Data were extracted based on predetermined inclusion and exclusion criteria. Two investigators employed QUADAS-2 to independently assess the quality of all eligible original studies. Following quality evaluation, we also performed a meta-analysis using STATA v 15.1 and RevMan v 5.4 and funnel plots were used to detect publication bias. A total of five RCTs were included in the meta-analysis. In this study, the regression rate of retinal neovascularization was used as the index of therapeutic effectiveness. According to the results, the retinal neovascularization regression rate of the intravitreal ranibizumab injection group was statistically higher than that of the laser therapy group [risk ratio (RR) = 1.26, 95% confidence interval (CI): 1.18–1.35]; however, the incidence of adverse events, including recurrence and complications, was not different between them (RR = 0.73, 95%CI: 0.19–2.80). Therefore, intravitreal ranibizumab injection may be more clinically effective than laser therapy in the treatment for ROP. The safety and efficacy of ranibizumab in the long-term treatment for ROP needs further investigation.Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, CRD42022296387

This paper presents a dual-antenna energy harvesting (EH) system using full-energy extraction (FEE) to reduce the buffer capacitor requirement and improve the input power response speed. Instead of using large buffer capacitors (in the order of $\mu \text{F}$ ) at the rectifier output, the proposed FEE technique can efficiently harvest the incoming 915MHz radio-frequency (RF) energy using only a 1-nF capacitor per channel, while reducing the operating frequency of the subsequent DC-DC converter for power loss minimization. The two most popular RF rectifiers are investigated to relax the energy extraction degradation due to impedance mismatch. The boost DC-DC converter combines the harvested energy from the dual-inputs through the FEE controller. Fabricated in standard 0.18- $\mu \text{m}$ CMOS and wire-bonded on an FR4 PCB, the dual-antenna EH system with the DC-DC converter operating typically at 6.25kHz demonstrates a measured peak system efficiency of 75.2% at an output power of $22.6~\mu \text{W}$ using only two 1-nF buffer capacitors at 1- $\mu \text{F}$ load. With a significant buffer size reduction than prior art at similar efficiency/power level, a short startup time of ~ 45ms is achieved when the output rises from 0 to 1 V at $15.5~\mu \text{W}$ total rectifier output power, corresponding to 90% startup time reduction over the conventional approach.

It is well recognized that strain and deflection data are important indexes to judge the safety of truss structures. Specifically, the shape sensing technology can estimate the deformation of a structure by exploiting the discrete strain data without considering the material property conditions. To fill the gap in which most of the methods in SHM (structural health monitoring) cannot be directly used to predict the displacement field, this paper proposed a novel inverse finite element method (iFEM) algorithm based on the equivalent stiffness theory. A deflection sensor is fabricated to focus on predicting the distributed deflection variation of the truss structure. The performance of the deflection sensor was evaluated by a calibration test and a stability test. Finally, it was applied to distributed deflection monitoring in the testing of truss structures. Results of all tests verify that the deflection sensor based on the i-FEM algorithm can predict the distributed deflection variation of the truss structure accurately, in real time, and dynamically.

Localization and tracking of personnel and equipment are technical issues that urgently need to be solved for Indoor positioning. To improve the accuracy and environmental adaptability of personnel and equipment localization algorithms in the construction and operation of smart water platform, this paper proposes a fingerprint localization algorithm (KF-KNN) based on FM signals. Firstly, use FM data collection device to obtain RSSI fingerprint information within the coverage area, and train them to build a fingerprint database; secondly, KNN technology is used to complete the rough localization calculation based on the RSSI data received by the module to be located, the RSSI fingerprint database and environmental noise parameters; finally, the Kalman filter model is used to predict and optimize the rough position information, so as to have better environmental adaptability and effectively improve the accuracy of localization. The analysis results show that: compared with the original KNN and WKNN fingerprint localization models, the KF-KNN algorithm has better performance in localization, and its average localization error can be as low as 1.9 meters.