268 research outputs found

    Sulfide-modified nanoscale zero-valent iron as a novel therapeutic remedy for septic myocardial injury

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    Introduction: Myocardial injury is a serious complication in sepsis with high mortality. Zero-valent iron nanoparticles (nanoFe) displayed novel roles in cecal ligation and puncture (CLP)-induced septic mouse model. Nonetheless, its high reactivity makes it difficult for long-term storage. Objectives: To overcome the obstacle and improve therapeutic efficiency, a surface passivation of nanoFe was designed using sodium sulfide. Methods: We prepared iron sulfide nanoclusters and constructed CLP mouse models. Then the effect of sulfide-modified nanoscale zero-valent iron (S-nanoFe) on the survival rate, blood routine parameters, blood biochemical parameters, cardiac function, and pathological indicators of myocardium was observed. RNA-seq was used to further explore the comprehensive protective mechanisms of S-nanoFe. Finally, the stability of S-nanoFe-1d and S-nanoFe-30 d, together with the therapeutic efficacy of sepsis between S-nanoFe and nanoFe was compared. Results: The results revealed that S-nanoFe significantly inhibited the growth of bacteria and exerted a protective role against septic myocardial injury. S-nanoFe treatment activated AMPK signaling and ameliorated several CLP-induced pathological processes including myocardial inflammation, oxidative stress, mitochondrial dysfunction. RNA-seq analysis further clarified the comprehensive myocardial protective mechanisms of S-nanoFe against septic injury. Importantly, S-nanoFe had a good stability and a comparable protective efficacy to nanoFe. Conclusions: The surface vulcanization strategy for nanoFe has a significant protective role against sepsis and septic myocardial injury. This study provides an alternative strategy for overcoming sepsis and septic myocardial injury and opens up possibilities for the development of nanoparticle in infectious diseases

    Prevalence and associated factors of thyroid nodules among 52,003 Chinese 'healthy' individuals in Beijing : a retrospective cross-sectional study

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    Introduction: The prevalence of thyroid nodules has been increasing, and there are few research data on the risk factors of thyroid nodules in the Chinese population. In this study, we aimed to determine the prevalence and risk factors of thyroid nodules by retrospectively investigating the physical examination records of a cohort of “healthy” individuals in Beijing, China. Methods: This was a retrospective cross-sectional study. The database of a Medical Examination Centre (MEC) was searched. Physical examination data, blood test data, and ultrasound examination data, etc., from 2015 to 2017 were accessed. Only those that recorded a thyroid ultrasound were included. Chi-square test and t-test were used to compare clinical features of individuals’ age, gender, body mass index, blood pressure, blood glucose, blood lipids, uric acid, and presence of fatty liver. Risk factors for thyroid nodules were determined using multivariate logistic regression. Results: A total of 52,003 records, which included 19,901 cases with thyroid nodules, were examined. The overall prevalence rate was 38.3% (19,901/52,003): 30.2% (6,726/22,305) and 44.4% (13,175/29,698) in men and women, respectively. Of 52,003 cases, only 35,420 cases had records of all nodule-related metabolic abnormalities and were selected for cross-sectional determination of related risk factors of thyroid nodules. In male, relationships were found between thyroid nodules and increased age (p < 0.001), impaired fasting glucose (p = 0.044), diabetes (p = 0.047), decreased HDL-C (p = 0.018) and prostatic hyperplasia (p < 0.001). And in female, relationships were found between thyroid nodules and increased age (p < 0.001) and decreased HDL-C (p < 0.001). Conclusion: Thyroid nodules are common in China. This study found that thyroid nodules are associated with several metabolic indicators or metabolic diseases, although the mechanism is unclear. Further research is needed

    Factors contributing to development and resolution of dysglycemia in patients with pheochromocytomas and catecholamine-secreting paragangliomas

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    AbstractBackground Pheochromocytomas and paragangliomas (PPGLs) are a group of rare neuroendocrine tumors. Dysglycemia has been observed in patients with PPGLs in some small case series. However, there is limited information available on the factors associated with development and resolution of dysglycemia in these patients.Patients and methods The clinical data of consecutive patients admitted to our hospital with PPGLs between January 2018 and June 2020 were retrospectively analyzed. Clinical characteristics were compared between patients with and without dysglycemia. Logistic regression analysis was used to identify risk factors and receiver-operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the variables.Results Among 163 patients, 58.9% had preoperative dysglycemia. Patients with dysglycemia were significantly older at diagnosis (p = 0.01) and were significantly more likely to have hypertension (p = 0.007). White blood cell counts (p = 0.016), 24-hour urinary epinephrine (24hU-E) levels (p < 0.001) and 24-hour urinary norepinethrine levels (p = 0.008) were significantly higher in patients with dysglycemia. Regression analysis showed that age (odds ratio [OR] 1.028, 95% confidence interval [CI] 1.001–1.055; p = 0.041), hypertension (OR 2.164, 95% CI 1.014–4.619; p = 0.046) and the 24hU-E concentration (OR 1.010, 95% CI, 1.001–1.019; p = 0.025) were positively associated with preoperative dysglycemia. Taking age, hypertension, and 24hU-E into account in the same model, the area under the ROC curve for prediction of preoperative dysglycemia was 0.703. The proportion of patients with dysglycemia decreased significantly after surgery (p < 0.001) and patients with preoperative dyssglycemia that resolved after surgery tended to have a larger preoperative tumor diameter (p = 0.018).Conclusion Age, hypertension, and the 24hU-E concentration are risk factors for preoperative dysglycemia. Removal of PPGLs can improve dysglycemia in most patients, and postoperative remission of dysglycemia is associated with the preoperative tumor diameter. These results are important for risk assessment and for selecting optimal therapies in patients with dysglycemia in PPGLs.KEY MESSAGESThere have been insufficient data to identify factors associated with development and resolution of dysglycemia in patients with PPGLs.Our results show that approximately half of the patients with PPGLs develop dysglycemia; age, hypertension, and the 24hU-E concentration are risk factors for preoperative dysglycemia.Removal of the PPGLs improves dysglycemia in a majority of patients, and a large preoperative tumor diameter is associated with remission of dysglycemia after surgery

    Protection of melatonin treatment and combination with traditional antibiotics against septic myocardial injury

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    Abstract Background Heart failure is a common complication of sepsis with a high mortality rate. It has been reported that melatonin can attenuate septic injury due to various properties. On the basis of previous reports, this study will further explore the effects and mechanisms of melatonin pretreatment, posttreatment, and combination with antibiotics in the treatment of sepsis and septic myocardial injury. Methods and results Our results showed that melatonin pretreatment showed an obvious protective effect on sepsis and septic myocardial injury, which was related to the attenuation of inflammation and oxidative stress, the improvement of mitochondrial function, the regulation of endoplasmic reticulum stress (ERS), and the activation of the AMPK signaling pathway. In particular, AMPK serves as a key effector for melatonin-initiated myocardial benefits. In addition, melatonin posttreatment also had a certain degree of protection, while its effect was not as remarkable as that of pretreatment. The combination of melatonin and classical antibiotics had a slight but limited effect. RNA-seq detection clarified the cardioprotective mechanism of melatonin. Conclusion Altogether, this study provides a theoretical basis for the application strategy and combination of melatonin in septic myocardial injury

    Analysis and optimization of a novel high cooling flux stacked T-shaped thermoelectric cooler

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    To meet the cooling demands of high heat flow density hotspots in scenarios such as electronic chips, a novel three-dimensional stacked T-shaped thermoelectric cooler (STTEC) is designed in this study. Under steady-state conditions, a finite element method with coupled thermal–electrical–mechanical physical fields is utilized, and the temperature dependence of thermoelectric (TE) materials is considered. First, the cooling flux, coefficient of performance (COP), and minimum cooling temperature of STTEC under different input-current and thermal boundary conditions are investigated and compared to the traditional π-shaped thermoelectric cooler (π-TEC). Second, the effects of geometrical parameter variations under optimal currents on the cooling performance and reliability of STTEC are studied. Finally, the structural parameters are optimized. The results show that the STTEC altered the path of TE conversion and transfer, which significantly improved the optimal current. The STTEC has a remarkable advantage in cooling performance under low temperature differences or high cooling loads. Compared to the π-TEC, STTEC enhances cooling flux by 101.6%, rises COP by 358.5%, and lowers the cold-end temperature by 46.6 K. At optimal current conditions, by optimizing the thickness of the T-shaped copper slice and the height difference between the TE leg and the T-shaped copper slice, the thermal stress decreased by 18.4%. The STTEC’s novel design could inspire the manufacturing and commercialization of high-performance thermoelectric coolers

    ArticleGust Alleviation by Active–Passive Combined Control of the Flight Platform and Antenna Array for a Flying Wing SensorCraft

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    SensorCraft is an intelligence, surveillance, and reconnaissance (ISR) system that integrates unmanned flight platforms and airborne antenna arrays. Under gust loads, the high–aspect–ratio, light–wing structure of SensorCraft has considerable bending and torsion deformation, affecting the flight performance of unmanned flight platforms and leading to the loss of antenna arrays’ electromagnetic performance. Taking SensorCraft as the background, a wing conformal antenna array was designed, an aircraft model with a passive wingtip device was established, a control law was developed by the LQG/LTR method, and a gust alleviation active–passive combined control method of a “LQG/LTR active controller + passive wingtip device” was proposed. By constructing an unsteady aerodynamic reduced–order model (ROM) based on the Volterra series and a conformal array pattern fast method based on the modal form, the effectiveness of the gust alleviation active–passive combined control method on the aircraft platform and antenna array was analyzed. The results show that structural deformation of the wing conformal antenna leads to changes in the main lobe gain, beam direction, and sidelobe level. The active–passive gust alleviation method has obvious advantages. Compared with the LQG/LTR active gust alleviation method, the peak value of wingtip displacement is reduced by 15.6%, and the peak value of the gain loss is reduced by 0.72 dB, which is conducive to better performance of the airborne conformal antenna array

    Smart grid power load type forecasting: research on optimization methods of deep learning models

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    Introduction: In the field of power systems, power load type prediction is a crucial task. Different types of loads, such as domestic, industrial, commercial, etc., have different energy consumption patterns. Therefore, accurate prediction of load types can help the power system better plan power supply strategies to improve energy utilization and stability. However, this task faces multiple challenges, including the complex topology of the power system, the diversity of time series data, and the correlation between data. With the rapid development of deep learning methods, researchers are beginning to leverage these powerful techniques to address this challenge. This study aims to explore how to optimize deep learning models to improve the accuracy of load type prediction and provide support for efficient energy management and optimization of smart grids.Methods: In this study, we propose a deep learning method that combines graph convolutional networks (GCN) and sequence-to-sequence (Seq2Seq) models and introduces an attention mechanism. The methodology involves multiple steps: first, we use the GCN encoder to process the topological structure information of the power system and encode node features into a graph data representation. Next, the Seq2Seq decoder takes the historical time series data as the input sequence and generates a prediction sequence of the load type. We then introduced an attention mechanism, which allows the model to dynamically adjust its attention to input data and better capture the relationship between time series data and graph data.Results: We conducted extensive experimental validation on four different datasets, including the National Grid Electricity Load Dataset, the Canadian Electricity Load Dataset, the United States Electricity Load Dataset, and the International Electricity Load Dataset. Experimental results show that our method achieves significant improvements in load type prediction tasks. It exhibits higher accuracy and robustness compared to traditional methods and single deep learning models. Our approach demonstrates advantages in improving load type prediction accuracy, providing strong support for the future development of the power system.Discussion: The results of our study highlight the potential of deep learning techniques, specifically the combination of GCN and Seq2Seq models with attention mechanisms, in addressing the challenges of load type prediction in power systems. By improving prediction accuracy and robustness, our approach can contribute to more efficient energy management and the optimization of smart grids

    Performance analysis and optimization of the rough-contact Bi2Te3-based thermoelectric cooler via metallized layers

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    We use the numerical simulation to investigate an optimized metallized barrier layer for enhancing the performance of the Bi2Te3-based thermoelectric cooler (TEC). The maximal cooling capacity of 15.627 W and 5.646 W has been obtained under perfect and rough contact TEC. For the actual rough contact TEC, the Ag layer has the best cooling performance among conventional materials such as Ag, Ni, Ti, Co and Pd. The maximal cooling capacity (Qc max) for the rough contact TEC with Ag layer is 84.5% of the perfect contact TEC, which is 2.33 times of the rough contact TEC without any barrier layers. Subsequently, the roughness slope of the TEC and the pressure applied on the TEC have been researched. The Qc max can achieve 90% of the perfect contact TEC when the slope of the roughness is higher than 0.8. Under pressure over 3000 kPa, the Qc max can reach 91% of the perfect contact TEC. For practical production of Bi2Te3-based TEC can introduce Ag as the metallized layer to reduce the contact resistance and improve the cooling performance

    Quantitative prediction and evaluation of geothermal resource areas in the southwest section of the Mid-Spine Belt of Beautiful China

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    The geothermal resources in the southwest section of the Mid-Spine Belt of Beautiful China are abundant, but the quantitative prediction and evaluation of geothermal resources are very difficult. Based on geographic information system (GIS) and remote sensing (RS) platforms, six impact factors, namely land surface temperature, fault density, Gutenberg–Liszt B value, formation combination entropy, distance to river and aeromagnetic anomaly were selected. Through the establishment of the certainty factor model (CF), weights of the information entropy certainty factor model (ICF) and weights of the evidence certainty factor model (ECF), the geothermal potential in the study area were predicted quantitatively. Based on the ECF results, the six main geothermal resource areas were delineated. The results show that (1) ECF had high prediction accuracy (success index is 0.00405%, area ratio is 0.867); (2) The geothermal resource areas obtained were Ganzi–Ya’an–Liangshan, Panzhihua–Liangshan, Dali–Chuxiong, Nujiang–Baoshan, Diqing–Dali, and Lijiang–Diqing. The results provide a basis for the effective development and utilization of geothermal resources in the southwest section of the mid-ridge belt
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