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Background: Postoperative cognitive dysfunction (POCD) is a common postoperative complication in elderly patients. The purpose of this study was to investigate the mechanism through which metformin improves postoperative cognitive function. Methods: In the in vivo experiment, 18-month-old Sprague–Dawley (SD) rats were randomly divided into four groups (n = 12 in each group): the control, metformin, operation, and operation plus metformin groups. The animals were pretreated with metformin by gavage once daily for two weeks. The Morris water maze (MWM) was used to measure cognitive ability. In the in vitro experiment, BV2 cells were divided into five groups: the control, metformin, lipopolysaccharide (LPS), LPS plus metformin, and LPS plus metformin plus compound C groups. We stimulated microglia with LPS (500 ng/mL). Immunofluorescence and Western blotting were used to assess ROS (reactive oxygen species) levels, autophagy-associated protein levels and adenosine monophosphate-activated protein kinase (AMPK)/regulator factor 2-related enzyme 1 (SIRT1) signaling pathway activity in the rat cortex and microglial cells. Results: In the MWM test, the metformin-pretreated rats spent a higher proportion of time in the target quadrant. Immunofluorescence showed that the fluorescence intensity of LC3 in the cortex was increased in rats pretreated with metformin. Western blotting indicated that metformin upregulated the expression of autophagy-related and AMPK/SIRT1 signaling pathway-related proteins in the cortex after surgery. By activating the AMPK/SIRT1 signaling pathway in vitro, metformin reduced microglial activation and oxidative stress and promoted autophagy. Conclusions: Through the AMPK/SIRT1 pathway, metformin can boost autophagy and reduce oxidative stress in cortical microglia in older rats, in turn improving postoperative cognitive function.

Nanofluids based direct absorption solar collectors (DASCs) are considered as the important alternative for further improve the utilization of solar energy. However the low-quality energy and aggregation of nanoparticles obstructs their large-scale application. In this work, a new method of using magnetic nanofluids in DASCs is proposed. By this method, not only high-quality energy is got as well as the problems of blockage and corrosion in heat exchanger are well avoided. The result shows that the maximum temperature can reach 98 °C under 3 solar irradiations and the photothermal conversion efficiency can be further increased by 12.8% when the concentration is 500 ppm after adding an external rotating magnetic field. The highest viscosity of working fluid reduced by 21% when the concentration is 500 ppm at 95 °C after separating the Fe3O4@C nanoparticles from the nanofluids via magnetic separation technology. Meanwhile, the obtained pure base liquids with high temperature flow to heat exchanger, which also reduces the flow resistance in pipeline and avoids the problems such as blockage and corrosion in heat exchanger. This research promotes a new way for the efficient utilization of solar energy.

Abstract Background Sarcopenia is a common skeletal disease related to myogenic disorders and muscle atrophy. Current clinical management has limited effectiveness. We sought to investigate the role of miR-1290 in myoblast differentiation and muscle atrophy. Methods By transfecting miR-1290 into C2C12 cells, we investigated whether miR-1290 regulates myogenesis and myotube atrophy via AKT/P70 signaling pathway. MHC staining was performed to assess myoblast differentiation. Differentiation-related MHC, Myod, and Myog protein levels, and atrophy-related MuRF1 and atrogin-1 were explored by western blot. An LPS-induced muscle atrophy rat model was developed. RT-PCR was conducted to analyze miR-1290 serum levels in muscle atrophy patients and normal controls (NCs). Results The miR-1290 transfection increased MHC-positive cells and MHC, Myod, and Myog protein levels in the miR-1290 transfection group, demonstrating that miR-1290 promoted C2C12 myoblast differentiation. Myotube diameter in the miR-1290 transfection group was higher than in the TNF-α-induced model group. Western blot analysis showed decreased MuRF1 and atrogin-1 levels in the miR-1290 transfection group compared with the model group, demonstrating that miR-1290 protected against myoblast cellular atrophy. Luciferase assay and western blot analysis showed that miR-1290 regulation was likely caused by AKT/p70/FOXO3 phosphorylation activation. In the LPS-induced muscle atrophy rat model, miR-1290 mimics ameliorated gastrocnemius muscle loss and increased muscle fiber cross-sectional area. Clinically, miR-1290 serum level was significantly decreased in muscle atrophy patients. Conclusions We found that miR-1290 enhances myoblast differentiation and inhibits myotube atrophy through Akt/p70/FoxO3 signaling in vitro and in vivo. In addition, miR-1290 may be a potential therapeutic target for sarcopenia treatment.

In this paper, the dewatering and screening process of vibrating-dewatering screen is studied by the combination of simulation test and physical experiment. The simulation model of dewatering screening process is established by the method of computational fluid dynamics (CFD) and discrete element method (DEM). Subsequently, the screening efficiency and processing efficiency are defined as evaluating indexes for performance of dewatering and screening. Four parameters of amplitude, vibration frequency, vibrating direction angle and slope angle of screen surface are selected for single factor simulation test, and the variation of dewatering and screening performance under different parameters is obtained. Finally, the reliability of the simulation results is verified by the physical experiment platform, and significant factors affecting the evaluation index of the vibrating-dewatering screen parameters and the optimal parameter combination are obtained by orthogonal test.

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Small private online course-based flipped teaching is an innovative hybrid instructional approach that merges online and offline activities, fostering autonomous learning among students prior to class and facilitating the assimilation of knowledge within the confines of the classroom. To determine the effectiveness of the small private online course‐based flipped teaching model on undergraduate nursing students' self-directed learning abilities, we performed a practice version of the small private online course‐based flipped teaching program as a quasi-experiment, specifically designed for the Surgical Nursing course and assessed its effectiveness in students' self-directed learning abilities. To this end, a total of 264 students from the 2019 nursing major cohort were selected. Three classes comprising 131 students were randomly selected as the experimental group, and three other classes, comprising 133 students, were set as the control group. The results revealed no significant differences in the scores of self-directed learning abilities between the experimental and control groups before the experiment. After the experiment, the average scores on each dimension of the self-directed learning abilities improved significantly, and the final score of the experimental group was higher than that of the control group. The small private online course‐based flipped teaching model can promote undergraduate nursing students’ self-directed learning abilities, owing to its practicality and feasibility for the selected course.

As the second part of the investigation on vibration characteristics of ± 800 kV converter transformers, this part focuses on load conditions. A full-load thermal stability test is carried out on 12 YY and YD converter transformers in a converter station. Vibration, voltage, current, and other important information are collected by online monitoring devices. Vibration amplitude, vibration spectrum, dominant frequency, vibration power ratio of frequency band, vibration power entropy, and odd–even harmonic ratio under different load conditions are calculated and studied to evaluate the vibration state. Theoretical analyses of winding electromagnetic force and vibration acceleration are carried out and results show that the harmonic currents make the dominant frequency shift to 300 Hz or 400 Hz. Most vibration characteristics are sensitive to load rate changes. The odd–even harmonic ratio is sensitive to filter disconnection and reconnection. The results of this study reveal the vibration regularity and corresponding mechanism and can contribute to further study on vibration assessment of ± 800 kV converter transformers under load conditions.

For lack of study on the vibration characteristics of converter transformers, this paper studied the vibration signal analysis method and investigated the vibration characteristics of converter transformers during the commissioning of a ±800 kV converter station. The study comprised two parts including no-load and load conditions. In Part I, converter transformers are under no-load conditions. Vibration data are collected under conditions of no-load closing, no-load voltage rise with converters, and no-load voltage rise with DC lines. The vibration characteristics and mechanisms are studied based on the collected data. Several different vibration characteristics are introduced. Vibration power entropy, vibration total harmonic distortion, and recurrence rate are calculated under no-load conditions. Results show that vibration amplitude, vibration total harmonic distortion, and recurrence rate are sensitive to no-load closing. Vibration power entropy is sensitive to no-load voltage rise with DC lines. All these vibration characteristics are sensitive to converter unlocking. These characteristics show different and significant changes under different no-load conditions. Thus, it is vital to choose appropriate vibration characteristics when assessing the vibration state of converter transformers under different operating conditions. The study examines the vibration characteristics’ physical meanings and their changing mechanisms. It also summarizes distribution intervals and typical values of vibration characteristics. This research reveals the vibration regularity and corresponding mechanism. It provides a reference for vibration state assessment and early fault detection of ±800 kV converter transformers under no-load conditions.

In view of the existing double-layer linear vibrating screen, the screen surface inclination angle is basically parallel design. This paper puts forward a double-layer non-parallel and relatively independent vibrating screen. Experimental prototype of double-layer relatively independent vibrating screen under variable parameters was manufactured to verify the feasibility of simulations. This paper applied the single-factor tests to establish relationships between screening parameters of double-layer non-parallel and relatively independent vibrating screen and screening performance. What's more, the combination of screening parameters was optimized using the response surface method (RSM). The optimal combination after the round is as follow: vibration frequency 35 Hz, the angle of eccentric block 14.2#176;, the inclination of the upper screen 14.6#176; and the inclination of the lower screen 8.8°. [ABSTRACT FROM AUTHOR]

Abstract The winding current harmonic content of converter transformer in operation is rich. However, affected by harmonics, the radial vibration characteristics are quite different from the power transformer, structural loosening and deformation caused by winding vibration are more prominent. Therefore, in view of the influence of current harmonics on axial and radial vibrations, this study builds a 3D winding electromagnetic vibration finite element simulation model, analyses the components and contents of current harmonics, compares the leakage flux and vibration distribution under the fundamental, harmonic and superimposed currents. The correlation relationship between the harmonic components and axial and radial vibration characteristics is obtained, and the vibration measurement test of ±800 kV converter transformer in operation is carried out to verify the correctness of the analysis method. The results show that the winding radial vibration amplitude will exceed the axial vibration affected by harmonics. More importantly, under the superposition of the fundamental, 5th and 7th harmonic current, the vibration contribution of the converter transformer tank is mainly radial vibration, and the vibration dominant frequency is shifted from 100 Hz under the fundamental current to 400 Hz.

Background: Hypotheses on the development of postoperative delirium (PD) include "neuroinflammatory," "neuronal aging," "oxidative stress," "neurotransmitter deficiency," and "neuroendocrine." Here, we employed metabolomics to determine the serum metabolites in the baseline associated with an increased risk of PD.Methods: Two hundred and nine elderly hip-fracture patients who had undergone hemiarthroplasty and had completed our assessments were selected. Fasting venous blood was collected at 7:00 on the morning of surgery and a serum sample bank was created for analysis. On the first 3 postoperative days, the patients were assessed twice daily using the Confusion Assessment Method - Chinese Revision. Ultimately, 43 patients were diagnosed with PD, who comprised the PD group. Meanwhile, 43 matched non-PD patients were selected based on age, sex, and body mass index. Serum samples from the two groups were analyzed by gas chromatography-time-of-flight mass spectrometry and Acquity ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry.Results: The demographic characteristics of the groups were matched. Four metabolites associated with an increased risk of PD were identified, including S-methylcysteine, linolenic acid, eicosapentaenoic acid, and linoleic acid.Conclusions: Multiple metabolic pathways in the PD group altered before surgery, including deficiency of ω3 and ω6 fatty acids, energy metabolism and oxidative stress with interactions between hypoxia and mitochondrial dysfunction, in addition to glutamate-glutamine cycle dysfunction. These metabolic abnormalities could possibly increase the fragility of the brain and then contribute to PD. [ABSTRACT FROM AUTHOR]

Recognizing traffic signs is key to achieving safe automatic driving. With the decreasing cost of LiDAR, the accurate extraction of traffic signs using point cloud data has received wide attention. In this study, we propose combining point cloud and image traffic sign extraction: firstly, we use the improved YoloV3 model to detect traffic signs in panoramic images. The specific improvement is that the convolution block attention module is added to the algorithm framework, the traditional K-means clustering algorithm is improved, and Focal Loss is introduced as the loss function. It shows higher accuracy on the TT100K dataset, with a 1.4% improvement in accuracy compared to the previous YoloV3. Then, the point cloud of the area where the traffic sign is located is extracted by combining the image detection results. On this basis, the outline of the traffic sign is accurately extracted using the reflection intensity, spatial geometry and other information. Compared with the traditional method, the proposed method can effectively reduce the missed detection rate, narrow the range of point cloud, and improve the detection accuracy by 10.2%.

The operating condition and structural state of the converter transformer are closely related to vibration. Abundant harmonics aggravate the vibration of windings and iron cores, resulting in frequent mechanical structural failures, which seriously affect the stable operation of the power system. Traditional research mainly focuses on the vibration of AC transformers without harmonics and there is no in-depth discussion of the vibration mechanism and the numerical calculation model of windings and iron cores under harmonics. In addition, the influence of harmonics, winding connection method and other factors on the vibration characteristics are not clear. Therefore, this paper analyzes the voltage and current harmonic components and contents, establishes a harmonic-vibration numerical model and compares the vibration time-frequency characteristics with or without harmonics and different valve side winding connections through vibration measurement experiments. Finally, a combined simulation analysis reveals the contribution of the windings and core to the tank. The results show that the tank vibration amplitude and dominant frequency will increase under harmonica and the valve side current will affect the dominant frequency. Among these results, when there are harmonics, the amplitude increases by three times, the vibration dominant frequency changes from 100 Hz to 400 Hz and the frequency spectrum widens to 2000 Hz. In particular, the contribution of the winding vibration under the harmonic current will exceed the iron core. The research results reveal the influence of converter transformer harmonics on vibration, which can provide a theoretical basis for numerical calculation of vibration and monitoring of operating conditions and guide the design of structural vibration reduction to reduce mechanical failures caused by vibration.

Aim: Pathophysiological disorders after surgery might be related to postoperative delirium (POD). This study was designed to elucidate the pathogenesis of POD in elderly oral cancer patients by determining the perioperative kinetics of inflammatory cytokines, cortisol, and amyloid β1-40 (Aβ1-40). Methods: A total of 257 elderly oral cancer patients who underwent tumor resection surgery were selected. Venous blood was collected prior to surgery (T0), at the end of surgery (T1), and at 12 hours after surgery (T2). During the first three postoperative days, patients were examined using the confusion assessment method twice a day (8 am and 8 pm). Mini-Mental State Examination scores were recorded at T0 and on postoperative days 1, 3, and 7. Ultimately, 56 patients suffering from POD made up the POD group, and 56 patients randomly selected from a cohort of patients without POD were allocated to the no POD (NPOD) group. Subsequently, interleukin-6, C-reactive protein, procalcitonin, cortisol, and Aβ1-40 in plasma from the two groups were measured. Results: The two groups displayed comparable basic characteristics. There were no differences in all tested biomarkers between the two groups at T0. However, after surgery, the biomarker levels displayed distinct patterns between the two groups. The peak levels of all biomarkers were higher in the POD group than in the NPOD group. Conversely, the Mini-Mental State Examination scores after surgery were lower in the POD group than in the no POD group. Conclusion: The boost of inflammatory cytokines, cortisol, and Aβ1-40 after surgery might be involved in POD onset among elderly oral cancer patients. POD was accompanied by progressive cognitive deficiency. [ABSTRACT FROM AUTHOR]

BackgroundRosacea, a chronic inflammatory skin disorder etiologically associated with immune cells and the antibacterial peptide cathelicidin LL-37, can be effectively treated by oral carvedilol administration.ObjectiveTo investigate the molecular mechanisms underlying carvedilol efficacy in rosacea treatment.MethodsSkin samples of patients with rosacea were subjected to histopathological (hematoxylin and eosin) and immunohistochemical (CD68, Toll-like receptor 2 (TLR2), kallikrein 5, cathelicidin, TNF-α, and IL-1β) evaluation. An in vivo murine rosacea-like inflammation model was established by LL-37 intradermal injection with or without carvedilol gavage-based pretreatment. Erythema proportion (Image J) and skin redness (L*a*b colorimetry) were quantified. Murine skin samples underwent pathological examination for inflammatory status and immunofluorescence staining. Murine skin and lipopolysaccharide-stimulated RAW 264.7 cells with or without carvedilol pretreatment were evaluated by quantitative reverse transcription-polymerase chain reaction and western blotting. Clinical facial images of patients were obtained using the VISIA skin analysis system before, 4, and 6 months following oral carvedilol administration.ResultsRosacea skin lesions exhibited more pronounced inflammatory cell infiltration than peripheral areas, with profound macrophage infiltration and inflammatory cytokines (TLR2, kallikrein 5, cathelicidin, TNF-α, and IL-1β). In vivo, carvedilol alleviated inflammation in LL-37 mice, down-regulating TLR2, KLK5, and cathelicidin expression. In vitro, carvedilol decreased TLR2 expression in RAW 264.7 cells, further reducing KLK5 secretion and LL-37 expression and ultimately inhibiting rosacea-like inflammatory reactions. Clinical manifestations and facial redness obviously improved during 6-month follow-up with systemic carvedilol administration.ConclusionCarvedilol is effective against rosacea, with inhibition of macrophage TLR2 expression as a novel anti-inflammatory mechanism.

The converter transformer is a special power transformer that connects the converter bridge to the AC system in the HVDC transmission system. Due to the special structure of the converter transformer, it is necessary to test its operation state during its manufacture and processing to ensure the safety of its future connection to the grid. Numerous studies have shown that vibration signals in transformers can reflect their operating state. Therefore, in order to achieve an effective identification of the operation state of the converter transformer, this paper proposes a method for identifying the operation state of the converter transformer based on vibration detection technology and a deep belief network optimization algorithm. This paper firstly describes the background, principle and application of vibration detection technology, using vibration measurement systems with piezoelectric acceleration sensors, piezoelectric actuators and data acquisition instruments to collect vibration signals at different measurement points on the converter transformer in states of no-load and on-load. By analyzing the time-frequency characteristics of the vibration signals, fast Fourier transform (FFT), wavelet packet decomposition (WPD) and time domain indexes (TDI) are combined into a fused feature extraction method to extract the eigenvalues of the vibration signals, so that the fused eigenvectors of the signals can be constructed. Considering the excellent performance of deep learning in classification, the deep belief network is used to classify the signals’ eigenvectors. To effectively improve the network classification efficiency, the sparrow search algorithm was introduced to build a mathematical model based on the behavioral characteristics of sparrow populations and combine the model with a deep belief network, so as to achieve adaptive parameter optimization of the network and accurate classification of the signals’ eigenvectors. The proposed method is applied to a 500 kV converter transformer for experimental verification. The experimental results show that the fused feature extraction method was able to fully extract the features of the vibration signal, and the deep belief network optimization algorithm had higher classification accuracy and better operational efficiency, and was able to effectively achieve accurate identification of the operation state of the converter transformer. In addition, the method achieved a precision response to the detection results of the vibration sensors, contributing to future improvements in converter transformer manufacturing technology.

Most power transformer faults are caused by iron core and winding faults. At present, the method that is most widely used for transformer iron core and winding faults identification is the vibration analysis method. The vibration analysis method generally determines the degree of fault by analyzing the energy spectrum of the transformer vibration signal. However, the noise reduction step in this method is complicated and costly, and the effect of denoising needs to be further improved to make the fault identification results more accurate. In addition, it is difficult to perform an accurate determination of the early mild failure of the transformer due to the effect of noise on the results. This paper presents a novel mathematical statistics method based on the vibration signal to optimize the vibration analysis method for the short-circuit failure of the transformer winding. The proposed method was used for linear analysis of the transformer vibration signal with different degrees of short-circuit failure of the transformer winding. By comparing the slope value of the transformer vibration signal cumulative probability distribution curve and analyzing the energy spectrum of the signal, the degree of short-circuit failure of the transformer winding was identified quickly and accurately. This method also simplified the signal denoising process in transformer fault detection, improved the accuracy of fault detection, reduced the time of fault detection, and provided good predictability for early mild faults of the transformer, thereby reducing the hidden hazards of operating the power transformer. The proposed optimization procedure offers a new research idea in transformer fault identification.