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Post-contrast acute kidney injury (PC-AKI) has emerged as the third leading cause of iatrogenic acute renal failure. Diabetes mellitus not only represents an independent risk factor for PC-AKI but also remains a major cause of chronic kidney disease (CKD). SS-31, an antioxidant peptide targeting mitochondria, is a potential preventive drug for PC-AKI. In this study, we established a PC-AKI model by injecting iodixanol in type 1 diabetic mice. Blood and tissue samples were collected to confirm that iodine injection caused excessive reactive oxygen species (ROS) in the kidney, activated the NLRP3 inflammasome pathway, and subsequently aggravated the development of PC-AKI. We also confirmed that SS-31 can reduce acute kidney injury induced by iodine contrast agent in diabetes by protecting mitochondrial function and inhibiting the ROS-NLRP3 signaling pathway.

This paper selects provincial panel data from 2011–2020 and establishes a spatial error model to quantitatively analyze the impact of green finance on green total factor productivity from an environmental governance perspective. The results show that: green finance has a significant positive and significant direct effect on green total factor productivity improvement; there is significant heterogeneity in the promotion effect of green finance on green total factor productivity, concluding that the impact of green finance on green industries in the eastern and central regions have a greater impact on GTFP enhancement, green finance has a limited impact on the western region, and the effect on low-carbon pilots is greater than the effect on non-low-carbon pilots; with the improvement of environmental governance, the positive promotion relationship between the comprehensive level of green finance and green total factor productivity is strengthened; the comprehensive level of green finance has a There is a direct promotion effect and a single threshold effect, when green finance is smaller than the threshold, the direct promotion effect is stronger; the level of environmental governance promotes the improvement of green total factor productivity by the comprehensive level of green finance, and there is a positive effect and a double threshold effect, as the level of environmental governance improves, the improvement of green total factor productivity by green finance shows a "rising first and then declining "With the improvement of environmental governance, the improvement of green total factor productivity by green finance shows an "inverted U-shaped" change.

Oxidative stress is the crucial pathogenic factor in osteoporosis. Cell autophagy, a major form of self-digestion, plays critical functions in different forms of stress by devouring harmful cytosolic proteins or organelles for the renewal of organelles and to maintain cellular homeostasis. Glucosamine (GlcN) has been widely utilized in treatments for patients with osteoarthritis-related joint pain. It has potential antioxidant effects and its pharmacological effect in osteoblasts remains unclear. The present study aimed to investigate whether autophagy participates in the protective effects of GlcN in osteoblasts under oxidative stress and the possible mechanism. First of all, MC3T3-E1 cells were treated with hydrogen peroxide (H

Background Converging evidence increasingly implicates acute kidney injury (AKI) contribute to a series of brain injury. However, the neurobiological mechanisms of AKI induced brain alterations remains unclear. Examining the neurobiology of AKI induce brain injury and an early detection using non-invasive imaging method is critical for the early diagnosis and target treatment. Methods The AKI Sprague-Dawley male rats and the healthy control group performed the Morris water maze and acquired rs-fMRI images with 9.4-Tesla Bruker BioSpec system at 24, 48 and 72 hours following administering contrast medium or saline. Whole brain amplitude of Low-Frequency Fluctuations (ALFF) at each time course was compared among groups. The AKI rats were then euthanized immediately, followed by H&E staining and immunohistochemistry (IHC). Besides, Flow cytometry analysis, Western blotting and immunofluorescence were performed to evaluate neuron and activated microglia. Results In contrast to HC group, lower ALFF in bilateral Cornu Ammonis (CA1) were observed in AKI-CI group. Specifically, there were significant differences in the ALFF values in the CA1 between the 48 h and 72 h in AKI groups. In addition, the percentages of microglia increased in AKI group at 24 h (range from 15.07 to 41.70%) and dramatically increased after 24h (range from 41.70–82.57%). Moreover, quantitative Flow cytometry analysis of microglial proportion is strongly correlated with the mALFF value (|r|༞0.80, P༜0.001). Conclusions Aberrant functional activity in the hippocampus, mainly CA1, contributes to the AKI induced brain injury. The ALFF of hippocampus may be an effective imaging biomarker for detecting grading of microglial proportion and have implications for early target treatment.

Edge extraction of weld pool image is the key to realize on-line detection and control of laser cladding during the process of industrial laser manufacturing,and the accuracy of on-line control of cladding layer quality is largely determined by the accuracy of extracted weld pool edge.In this paper, a laser cladding pool image detection system based on machine vision is built to collect the molten pool image in the process of laser cladding. According to the characteristics of laser cladding pool image, the image is preprocessed. In this paper, an improved multi-structure, multi-scale and multi-directional morphological operator is proposed to process the molten pool image, and the accuracy and anti-noise ability of different edge detection methods are compared and analyzed. the improved mathematical morphology edge detection method can not only suppress noise but also extract the edge of laser cladding pool image more accurately.

Aiming at the images of relevant monitored objects in the process of laser cladding, a super resolution algorithm technology was proposed to optimize and enhance the key details of the images, and the enhanced image content was segmented, extracted and counted. First, construct a training of a sub-resolution convolutional neural network (SRCNN) model; the original low resolution is predicted by the weight after training, the image quality evaluation results: peak signal-to-noise ratio (PSNR) is 30.198212, structural similarity (SSIM) is 0.969966; the most based on the maximum entropy dual threshold split algorithm combined with image processing, extracting and statistics on the powder object in the segmentation result image, the number of effective powders and proportion of the original wandering map and the predicted output delay image is [112, 33.6%] and [240, 40.6%]. The research results show that the cladding image output from the original image after the super resolution model has been significantly improved in terms of clarity and quality as well as the optimization and enhancement of the details of the monitored object.

A time-series prediction method for the geometric characteristics (length, length, area) of the molten pool in the laser cladding process is studied. This method uses the historical data of the change of the geometric characteristics of the molten pool to predict the geometric characteristics of the molten pool at the next moment, so as to promote the application in the control of the shape of the molten pool and the estimation of the state of the molten pool. In view of the time correlation shown by the time series of the geometric characteristics of the molten pool, a time series prediction method for the geometric characteristics of the molten pool was developed based on the Autoregressive Integrated Moving Average (ARIMA) model, and the dynamic stability index of the molten pool was proposed. The results show that the area of the molten pool has the best prediction accuracy, and the Mean Absolute Percentage Error (MAPE) is only 3.105, while the predicted MAPE of the width of the molten pool is 3.464 and the length of the predicted MAPE is 4.048. The dynamic stability index proposed can reflect the fluctuation of the molten pool.

BACKGROUND Acute pancreatitis (AP) is a common acute abdominal disease. Rapid evaluation of the severity is important for AP prognosis and treatment. Free triiodothyronine (fT3) level is associated with the prognosis of AP patients. This study aimed to investigate the fT3 level in patients with acute pancreatitis; early warning signs of inflammation, including interleukin-6 (IL-6) and interleukin-10 (IL-10); and the correlation of fT3 level with illness severity. MATERIAL AND METHODS Enrolled AP patients (N=312) were divided into an SAP group (N=92) and a non-SAP group (N=220) according to the Revision of Atlanta classification. Blood or tissue samples and baseline clinical characteristics were recorded. The t test and chi-square test were used to evaluate differences between the 2 groups. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curves were used to investigate protective factors. One-way repeated measures analysis of variance was used to evaluate the prognosis of SAP patients. RESULTS In our study, compared with APACHII score (AUC 0.829 [95% CIs 0.769-0.889]) and Ranson score (AUC 0.629 [95% CIs 0.542-0.715]), our predictive model (AUC 0.918 [95% CIs 0.875-0.961]) showed better prognostic performance in predicting poor patient outcomes. In the SAP group, changes in fT3 level were significantly associated with prognosis (P0.05). CONCLUSIONS The predictive model can improve the diagnostic accuracy and prediction of the severity of disease. FT3 level could be used as an independent risk factor to predict the mortality of SAP patients.

The Gauss-Newton algorithm has been widely used in GNSS positioning and indoor positioning applications, but it requires an initial guess. An unfavorable initial guess may cause the iteration divergence. This issue is particularly important in indoor positioning scenarios but has not attracted enough attention. The non-iterative closed-form algorithms are suitable to provide the initial guess since they are divergence-free but sub-optimal. This study systematically evaluated performance of four representative non-iterative closed-form algorithms, namely the differenced squared range (DSR) algorithm, the Bancroft algorithm, and constrained virtual parameter (CVP) algorithm in terms of positioning precision, robustness, and computation efficiency. The performance of these algorithms is evaluated with both simulative data and real data. The results indicate that the CVP algorithm achieves 0.2–0.6 m accuracy in the static scenarios and about 2 m accuracy in the kinematic scenario, which outperforms the Gauss-Newton algorithm and the other two non-iterative approaches. The Gauss-Newton approach also achieves promising results in the LOS scenario, but it is more vulnerable to the NLOS observations. The DSR algorithm and the Bancroft algorithms are sub-optimal, robust to NLOS biases, but their performance in real data test depends on the test scenario. The CVP algorithm is free of divergence issue, robust to NLOS bias, and computationally efficient and achieves the best positioning accuracy in all four algorithms, thus should be recommended in highly non-linear localization estimation scenarios, such as the indoor positioning applications.

Atrial fibrillation (AF) is a common clinical arrhythmia with a lifetime risk and has become an attractive topic of clinical research and health management. Although the 12-lead classical ECG configuration is adequate for the detection of AF, its configuration is suboptimal for constructing surface potential mapping or extracting other diagnostic features during AF. Body surface potential mapping (BSPM) has a broad application prospect in AF study, but the electrode wiring is complicated and the hardware cost is high. This paper proposes a method to improve the performance of leads selection algorithm from a 128-lead BSPM system with equidistantly spaced electrodes. 128-lead BSPM data obtained from 8 AF patients were used in the study. Leads selection was performed by a sequential selection based on an adapted Botteron’s approach and multiple linear regression. The result showed that the proposed method has significant improvement on the performance of the accurate reconstruction of dominant frequency on the patient-specific dataset. The proposed method may not only improve the performance on the spectrum characteristic but also effectively improve the performance of time domain criteria when the number of selected leads is above 12.

With numerous unique properties, anisotropic particles which have non-spherical morphologies have intriguing great attention in recent years. Here, anisotropic microparticles with the shape of peanuts were fabricated through a novel microfluidic strategy, including microfluidic generation of droplet-containing fiber matrices and deformation of the droplets induced by stretching the fiber matrices. Droplets were emulsified in the microfluidic device system, in which flow rates were precisely controlled to determine droplet size and structure. Through stretching the elastic fibers, encapsulated droplets were able to be deformed into peanut-like shapes, with their interconnected cores well-aligned in correspondence to the stretching direction. Magnetic peanut-shaped particles could be obtained by incorporating magnetic nanoparticles , and showed extraordinary controllable ability under a changing magnetic field. In addition, the microparticles containing platinum and magnetic nanoparticles were generated and found being able to work as microstirrers. These features strongly attested the versatility and promising values of the peanut-inspired anisotropic microparticles in a broad range of application fields.

BACKGROUND The composition of the intestinal microbiota and its effect on septic shock patients in the intensive care unit (ICU) is unknown. In the present study we explored the hypothesis that bacterial diversity is decreased in septic shock patients and that this diversity may be improved by use of probiotics or enteral nutrition. MATERIAL AND METHODS A total of 15 stool samples were collected prospectively from septic shock patients in the ICU, while 15 samples from healthy subjects served as controls. Bacterial DNA was submitted for 16S rDNA gene sequencing. The relationship between intestinal microbiota and prognosis was evaluated. RESULTS Significantly lower bacterial diversity was found in septic shock patients compared with healthy subjects (p

This paper demonstrates the design, synthesis, simulation, and testing of three distinct geometries of plasmonic gold nanoparticles for on-chip DNA screening towards liquid biopsy. By employing a seed-mediated growth method, we have synthesized gold nanospheres, nanorods, and nanobipyramids. In parallel, we developed numerical simulations to understand the effects of nanoparticle geometry on the resonance features and refractive index sensitivity. Both experimental and simulation results were compared through a series of studies including in-solution and on-chip tests. We have thoroughly characterized the impact of nanoparticle geometry on the sensitivity to circulating tumor DNA, with immediate implications for liquid biopsy. The results agree well with theoretical predictions and simulations, including both bulk refractive index sensitivity and thin film sensitivity. Importantly, this work quantitatively establishes the link between nanoparticle geometry and efficacy in detecting rare circulating biomarkers. The nanobipyramids provided the highest sensitivity, approximately doubling the sensitivity compared to nanorods. To the best of our knowledge this is the first report carrying through geometric effects of simulation to clinically relevant biosensing. We put forth here synthesis and testing of three nanoparticle geometries, and a framework for both experimental and theoretical validation of plasmonic sensitivities towards liquid biopsy.

To investigate the function of gasdermin D (GSDMD) in intestinal damage of mice with severe acute pancreatitis (SAP).The healthy C57BL/6 mice were divided into four groups randomly, including normal saline (NS) group, small interfering RNA (siRNA)-NS group, SAP model group and siRNA-SAP group, with 6 mice in each group. The SAP mouse model was reproduced by intraperitoneal injection of caerulein 50 μg/kg combined with lipopolysaccharide (LPS) 10 mg/kg; the NS group was given the same amount of NS; in the siRNA-SAP group and siRNA-NS group, siRNA 50 mg/kg was injected through the tail vein three times before modeling or injection of NS. The blood of mice eyeball in each group was taken 12 hours after modeling, and serum interleukins (IL-1β, IL-18) levels were detected by enzyme linked immunosorbent assay (ELISA). The mice were sacrificed to observe the general changes in abdominal cavity, the pancreas and ileum tissues were taken to observe the pathological changes under a light microscope. The expression of long-chain non-coding RNA uc.173 (lnc uc.173) was detected by reverse transcription-polymerase chain reaction (RT-PCR). Immunohistochemical method was used to detect the expression of tight junction proteins zonula occluden-1 (ZO-1) and Occludin in intestinal mucosal epithelial cells. Western blotting was used to detect the GSDMD protein expression level in the intestinal tissue.The serum levels of IL-1β and IL-18 in the SAP model group were significantly higher than those in the NS group and the siRNA-NS group [IL-1β (ng/L): 146.66±1.40 vs. 44.48±5.76, 81.49±10.75, IL-18 (ng/L): 950.47±177.09 vs. 115.43±16.40, 84.84±21.90, all P0.05]; and the levels of IL-1β and IL-18 in the siRNA-SAP group were significantly lower than those in the SAP model group [IL-1β (ng/L): 116.26±15.54 vs. 146.66±1.40, IL-18 (ng/L): 689.96±126.08 vs. 950.47±177.09, both P0.05]. General observation showed that there were no obvious abnormalities in the abdominal cavity of the mice in the NS and siRNA-NS groups; the mice in the SAP model group and the siRNA-SAP group had different degrees of edema and congestion in the intestine; compared with the SAP model group, the abnormalities in the siRNA-SAP group was significantly reduced. Under light microscope, there were no obvious changes in the pancreas and intestinal mucosa in the NS group and the siRNA-NS group; the pancreatic tissue of the SAP model group and the siRNA-SAP group had different degrees of edema, inflammatory cell infiltration, and lobular structure damage, and the intestinal mucosa was damaged to a certain degree, and the villi were broken to varying degrees, but the damage in the siRNA-SAP group was lighter. The results of RT-PCR showed that the expression of lnc uc.173 in the intestinal tissues of the model SAP group was significantly lower than that of the NS group and the siRNA-NS group (2The systemic inflammatory response and intestinal mucosal barrier damage of SAP mice may be related to the increase of GSDMD expression in intestinal tissues. GSDMD mediates cell pyrolysis to promote the release of inflammatory factors, cause intestinal injury, and down-regulate the expression of intestinal epithelial cell tight junction proteins such as ZO-1 and Occludin, resulting in intestinal mucosal damage.

Objective. To explore the autonomic nerve rhythm and the correlation between palpitations below the heart (PBTH) and autonomic nerve function in patients with PBTH based on heart rate variability (HRV). Methods. The outpatients or ward patients of Wenzhou Hospital of Traditional Chinese Medicine were collected and divided into two groups: the PBTH group and the normal group. The HRV of each group was detected. Single-factor statistical methods, Spearman correlation analysis, and logistic regression were used to describe and analyze the rhythm and characteristics of autonomic nerves in patients with PBTH and the correlation between PBTH and autonomic nerve function. Results. (1) In the comparison of HRV in different time periods in the same group, the SDNN, RMSSD, pNN50, TP, and HF in the PBTH group at night were significantly higher than those in the daytime ( P < 0.01 ), while the LF/HF ratio was significantly lower than that in the daytime ( P < 0.01 ). (2) In the comparison of HRV between the two groups in the same time period, the RMSSD and pNN50 of the PBTH group during the daytime period were significantly higher than those of the normal control group ( P < 0.05 ), and the LF/HF was significantly lower than that of the normal group ( P < 0.05 ). (3) In the Spearman correlation analysis, PBTH was significantly correlated with RMSSD, pNN50, and LF/HF ratio in the daytime period, with correlation coefficients of 0.424, 0.462, and −0.524, respectively ( P < 0.05 ). (4) Logistic regression analysis showed that the decrease of LF/HF ratio during the daytime period was an independent risk factor for PBTH in TCM (OR = 0.474, 95% CI: 0.230–0.977, P < 0.05 ). Conclusions. The changes in parasympathetic nerve function in patients with PBTH have a circadian rhythm, which is characterized by increased activity during the nighttime. At the same time, the autonomic nerve activity of people with PBTH during the daytime is unbalanced, and the decrease of LF/HF ratio during the day is an independent high risk factor for PBTH.

Publisher Copyright: © 2021 Elsevier Ltd With the diversification of commercial energy storage technologies, choosing a suitable technology is becoming a complex decision-making process. The complexity is rooted in the many decision criteria such as technology, brand reputation, energy capacity, volume, weight, aging, and warranty among many others. As such, for non-expert users, particularly small households or enterprises, the act of energy storage adoption is becoming growingly cumbersome. To address this problem, this paper introduces a decision support tool for the evaluation of commercial (small-scale) energy storage products. It then identifies the most suitable option(s) based on the users' preferences. For the reasons elaborated in the paper, nine multi-criteria decision-making (MCDM) methodologies have been employed. Altogether, 19 attributes are identified for the evaluation of (battery) energy storage technologies. The decision support tool is developed in the Matlab environment and includes a graphical user interface for easier interaction of non-expert users. For the demonstration, three scenario cases have been studied for users with different preferences. The ranking results clearly show the marked impact of users preferences on the recommended energy storage technologies. This implies that a tool like this can help small users in the selection of their right technology and avoid resource loss due to inappropriate technology selection, which can be neither economical nor sustainable.

Limited biomarkers have been identified as prognostic predictors for stage III colon cancer. To combat this shortfall, we developed a computer-aided approach which combing convolutional neural network with machine classifier to predict the prognosis of stage III colon cancer from routinely haematoxylin and eosin (H&E) stained tissue slides. We trained the model by using 101 cancers from West China Hospital (WCH). The predictive effectivity of the model was validated by using 67 cancers from WCH and 47 cancers from The Cancer Genome Atlas Colon Adenocarcinoma database. The selected model (Gradient Boosting-Colon) provided a hazard ratio (HR) for high- vs. low-risk recurrence of 8.976 (95% confidence interval (CI), 2.824–28.528; P, 0.000), and 10.273 (95% CI, 2.177–48.472; P, 0.003) in the two test groups, from the multivariate Cox proportional hazards analysis. It gave a HR value of 10.687(95% CI, 2.908–39.272; P, 0.001) and 5.033 (95% CI,1.792–14.132; P, 0.002) for the poor vs. good prognosis groups. Gradient Boosting-Colon is an independent machine prognostic predictor which allows stratification of stage III colon cancer into high- and low-risk recurrence groups, and poor and good prognosis groups directly from the H&E tissue slides. Our findings could provide crucial information to aid treatment planning during stage III colon cancer.

This paper empirically studies the effect of central city economic growth on the peripheral city economic growth in urban agglomeration. Using the panel data of 120 Chinese cities from 2000 to 2012, we find that the central city economic growth significantly and substantially increases the peripheral city economic growth. These findings not only enrich the relative researches on Chinese urban agglomeration, but also strengthen the understanding of the key role of the central city under the background of promoting urbanization in China.

Objective Atrial fibrillation inducibility has recently been shown to be associated with the increased clinical recurrence after ablation. Previous studies have identified unstable sinus rhythm before the occurrence of paroxysmal AF, yet earlier subtle changes, related to AF inducibility, are less be investigated. The purposed of this study is to predict AF inducibility, here we established a controllable canine model and prepared various sinus rhythms with different AF inducibility to study atrial electrophysiological changes. Methods The data were derived from acetylcholine induced acute canine AF models (n = 5, data samples = 90) through epicardial mapping system using 64 electrodes attached to the left and right atrial appendages. Signal preprocessing consists of three steps: noise reduction, removal of ventricular artifact and extraction of activation time (AT). Two classifiers based on activating rule and spatiotemporal features, visualized by network mapping, were established by binary logical regression analysis. Results The sensitivity, specificity and accuracy of activation analysis are 83.30%, 91.70% and 87.80% respectively, while those of spatiotemporal analysis are apparently increased to 97.60%, 93.80% and 95.56%. We observed that the atria (in sinus rhythm) with more disordered electrophysiological activity was more vulnerable to AF. Conclusion The high accuracies indicate the feasibility of AF inducibility prediction using sinus rhythm electrocardiogram, especially with the aid of superior spatiotemporal analysis. Significance To our knowledge, this is the first study that demonstrates the possibility of predicting AF inducibility using network mapping. With this study, accurate AF inducibility forecasting may help to evaluate the recurrence of AF after ablation.

Atrial fibrillation (AF) is one of the most common arrhythmias. The automatic AF detection is of great clinical significance but at the same time it remains a big problem to researchers. In this study, a novel deep learning method to detect AF was proposed. For a 10 s length single lead electrocardiogram (ECG) signal, the continuous wavelet transform (CWT) was used to obtain the wavelet coefficient matrix, and then a convolutional neural network (CNN) with a specific architecture was trained to discriminate the rhythm of the signal. The ECG data in multiple databases were divided into 4 classes according to the rhythm annotation: normal sinus rhythm (NSR), atrial fibrillation (AF), other types of arrhythmia except AF (OTHER), and noise signal (NOISE). The method was evaluated using three different wavelet bases. The experiment showed promising results when using a Morlet wavelet, with an overall accuracy of 97.56%, an average sensitivity of 97.56%, an average specificity of 99.19%. Besides, the area under curve (AUC) value is 0.9983, which showed that the proposed method was effective for detecting AF.