Microbial corrosion behavior of pipeline steels in simulation environment of natural gas transportation pipeline.

Bacteria are introduced into natural gas transmission pipelines through water-driven gas extraction, which can exacerbate the occurrence of pipeline corrosion. This study utilized a micro-reactor to design a simulated corrosion environment that mimics natural gas gathering and transportation pipelines. The objective was to investigate the corrosion behavior of X80 pipeline steel under the combined effects of CO2, Cl-, sulfate reducing bacteria (SRB), and iron bacteria (IOB). Additionally, it aimed to elucidate the influence mechanisms of these two microorganisms on corrosion. Under a humid environment with a total pressure of 8.5 MPa and a partial pressure of CO2 at 0.85 MPa, the corrosion rate of X80 pipeline steel was observed to follow the sequence: IOB > control (asepsis) > SRB + IOB > SRB. During the initial stages of corrosion, highly active IOB becomes the primary factor contributing to corrosion. As corrosion progresses, the concentration of dissolved oxygen in the SRB system gradually decreases while SRB activity intensifies, leading to the formation of FeS through the process of corrosion. The corrosion current density (icorr) exhibited a significant decrease, thereby intensifying localized corrosion of the corrosion products beneath the film. This resulted in a maximum pitting depth of 113.5 µm. Research on the behavior of microbial-enhanced corrosion provides significant guidance in the development and implementation of protective coatings.

Cellular Network Fault Diagnosis Method Based on a Graph Convolutional Neural Network.

The efficient and accurate diagnosis of faults in cellular networks is crucial for ensuring smooth and uninterrupted communication services. In this paper, we propose an improved 4G/5G network fault diagnosis with a few effective labeled samples. Our solution is a heterogeneous wireless network fault diagnosis algorithm based on Graph Convolutional Neural Network (GCN). First, the common failure types of 4G/5G networks are analyzed, and then the graph structure is constructed with the data in the network parameter, given data sets as nodes and similarities as edges. GCN is used to extract features from the graph data, complete the classification task for nodes, and finally predict the fault types of cells. A large number of experiments are carried out based on the real data set, which is achieved by driving tests. The results show that, compared with a variety of traditional algorithms, the proposed method can effectively improve the performance of network fault diagnosis with a small number of labeled samples.

Role of Sonazoid enhanced ultrasound assistant laparoscopic radiofrequency ablation in treating liver malignancy-A single-center retrospective cohort study.

AIM: To evaluate the role of Sonazoid enhanced ultrasound assistant laparoscopic radiofrequency ablation in treating liver malignancy. METHODS: Consecutive patients are recruited. Rates of complication and postoperative length of stay are compared between the study and control groups. Progression-free survival (PFS) of colorectal liver metastasis (CRLM) after ablation are compared. Complete ablation rates are compared and optimal tumor size is calculated by ROC curve analysis. Risk factors of incomplete ablation are determined by logistic regression analysis. RESULTS: Totally 73 patients with 153 lesions were included. No significant differences in the rate of complication were found between the study and control groups. PFS of CRLM in laparoscopic, intraoperative CEUS, and laparoscopic CEUS groups are all longer than their control groups. Complete ablation rates of laparoscopic, intraoperative CEUS, and laparoscopic CEUS groups are all higher than in their control groups with statistical significance. A tumor size of 2.15 cm is determined to be the optimal cut-off with the area under the ROC curve of 0.854, 95% CI (0.764, 0.944), p = 0.001. In logistic regression analysis, tumor size [OR 20.425, 95% CI (3.136, 133.045), p = 0.002] and location of segments VII and VIII [OR 9.433, 95% CI (1.364, 65.223), p = 0.023] are calculated to be the risk factors of incomplete ablation, meanwhile, intraoperative CEUS shows to be a protective factor in univariate analysis [OR 0.110, 95% CI (0.013, 0.915), p = 0.041]. CONCLUSION: Sonazoid-enhanced ultrasound assistant laparoscopic radiofrequency ablation is safe and effective to treat liver malignancy. We should pay attention to the ablation planning of larger tumors and tumors in special locations.

Kupffer Phase Radiomics Signature in Sonazoid-Enhanced Ultrasound is an Independent and Effective Predictor of the Pathologic Grade of Hepatocellular Carcinoma.

We conduct this study to investigate the value of Kupffer phase radiomics signature of Sonazoid-enhanced ultrasound images (SEUS) for the preoperative prediction of hepatocellular carcinoma (HCC) grade. From November 2019 to October 2021, 68 pathologically confirmed HCC nodules from 54 patients were included. Quantitative radiomic features were extracted from grayscale images and arterial and Kupffer phases of SEUS of HCC lesions. Univariate logistic regression and the maximum relevance minimum redundancy (MRMR) method were applied to select radiomic features best corresponding to pathological results. Prediction radiomic signature was calculated using each of the image types. A predictive model was validated using internal leave-one-out cross validation (LOOCV). For discrimination between poorly differentiated HCC (p-HCC) and well-differentiated HCC/moderately differentiated HCC (w/m-HCC), the Kupffer phase radiomic score (KPRS) achieved an excellent area under the curve (AUC = 0.937), significantly higher than the other two radiomic signatures. KPRS was the best radiomic score based on the highest AUC (AUC = 0.878), which is prior to gray and arterial RS for differentiation between w-HCC and m/p-HCC. Univariate and multivariate analysis incorporating all radiomic signatures and serological variables showed that KPRS was the only independent predictor in both predictions of HCC lesions (p-HCC vs. w/m-HCC, log OR 15.869, P < 0.001, m/p-HCC vs. w-HCC, log OR 12.520, P < 0.05). We conclude that radiomics signature based on the Kupffer phase imaging may be useful for identifying the histological grade of HCC. The Kupffer phase radiomic signature may be an independent and effective predictor in discriminating w-HCC and p-HCC.

Comparing the effects of twice-daily exenatide and insulin on renal function in patients with type 2 diabetes mellitus: secondary analysis of a randomized controlled trial.

This is a secondary analysis of a randomized controlled trial (RCT) on the effects of the glucagon-like peptide-1 receptor agonists exenatide and insulin aspartate 30 injection on carotid intima-media thickness. Here, we report the renal outcomes of the intervention in patients with type 2 diabetes mellitus (T2DM). Data from the RCT study was used to evaluate the effect of exenatide or insulin given for 52 weeks on estimated glomerular filtration rate (eGFR) in patients with T2DM. The primary end point was the change in the eGFR from baseline between the exenatide and insulin groups in normal versus overweight patients and patients with obesity. The secondary end point was the correlation between change in eGFR and oxidative stress, glycemic control, and dyslipidemia. There was a significant difference in eGFR between the insulin and exenatide groups at 52 weeks (p=0.0135). Within the insulin group, the eGFR remained below baseline at 52 weeks in all patients, and there was an increase in body weight in the normal group compared with the overweight patients and patients with obesity. The opposite was observed in the exenatide group. A decrease in body weight was prominent in the exenatide group at 52 weeks (p<0.05), the eGFR was below baseline in overweight patients and patients with obesity and significantly above baseline in the normal group (p<0.05). The eGFR was positively correlated to 8-oxo-7,8-dihydroguanosine in the insulin group (p<0.05) but not the exenatide group. It can be concluded that compared with insulin, exenatide may improve renal function in overweight patients and patients with obesity more than in normal-weight patients with T2DM, but a further RCT is needed to confirm this effect.

Prediction of the disease causal genes based on heterogeneous network and multi-feature combination method.

At present, the prediction of disease causal genes is mainly based on heterogeneous. Research shows that heterogeneous network contains more information and have better prediction results. In this paper, we constructed a heterogeneous network including four node types of disease, gene, phenotype and gene ontology. On this basis, we use a machine learning algorithm to predict disease-causing genes. The algorithm is divided into three steps: preprocess and training sample extraction, features extraction and combination, model training and prediction. In the process of feature extraction and combination, by using network representation method, the representation vectors of nodes are generated as the embedding features of the nodes. We also extracted the structural features of each node in the network and then the embedding features and structure features are combined. The results of training and prediction show that the prediction algorithm based on all features combined together achieves the best prediction performance. Moreover, the combination of each network representation method's embedding features and structural features has also achieved performance improvement. In the process of training samples extraction, we propose three improvement directions according to the network structure and data set distribution. Firstly, a positive sample algorithm based on network connectivity is proposed, we try to keep the connectivity of the whole heterogeneous graph in the sampling process to avoid the negative impact of embedding features' extraction. Moreover, the influence of sample sampling ratio on experimental results was tested in the range of 0-1 with step size of 0.1. The influence of different proportion of positive and negative samples on the results was also tested. These improvements are intended to enhance the balance and robustness of the method. When the positive sample ratio is 0.1 and the proportion of negative and positive samples is 3, the model achieves the optimal result, and its AUC value and accuracy are 0.9887% and 94.55%, respectively, which are significantly higher than other models.

Efficacy of Exenatide Administered Twice Daily in Body Mass Index Reduction in Patients with Type 2 Diabetes Mellitus.

Background: Exenatide is a glucagon-like peptide-1 receptor agonist that can reduce body weight. This study aimed to determine the efficacy of exenatide on body mass index (BMI) reduction in patients with type 2 diabetes mellitus (T2DM) with differing baseline body weight, blood glucose, and atherosclerotic status and to determine if there is a correlation between BMI reduction and cardiometabolic indices in these patients. Methods: This retrospective cohort study used data from our randomized controlled trial. A total of 27 T2DM patients treated with combination therapy of exenatide twice daily and metformin for 52 weeks were included. The primary endpoint was a change in the BMI from the baseline to week 52. The secondary endpoint was a correlation between BMI reduction and cardiometabolic indices. Findings. The BMIs of overweight and obesity patients and those with glycated hemoglobin (HbA1c) ≥ 9% significantly decreased -1.42 ± 1.48 kg/m2(P=0.015) and -0.87 ± 0.93 kg/m2(P=0.003), respectively, at the baseline after 52 weeks of treatment. There was no reduction in BMI in patients with normal weight, HbA1c <9%, the nonatherosclerosis group, and the atherosclerosis group. The decrease in BMI was positively correlated with changes in blood glucose, high-sensitivity C-reactive protein (hsCRP), and systolic blood pressure (SBP). Conclusion: BMI scores improved after exenatide treatment for 52 weeks in T2DM patients. Weight loss was affected by baseline body weight and blood glucose level. In addition, BMI reduction from the baseline to 52 weeks was positively correlated with baseline HbA1c, hsCRP, and SBP. Trial Registration. Chinese Clinical Trial Registry (ChiCTR-1800015658).

Contrast-Enhanced Intraoperative Ultrasonography with Kupffer Phase May Change Treatment Strategy of Metastatic Liver Tumors - A Single-Centre Prospective Study.

AIM: To compare the diagnostic performance of contrast-enhanced intraoperative ultrasonography (CE-IOUS) with Kupffer phase in metastatic liver tumours. METHODS: Twenty-seven consecutive patients with liver metastasis were prospectively recruited from November 2019 to July 2020 in the Department of HPB, Beijing Hospital. MRI and Contrast Enhanced Ultrasonography (CEUS) were obtained preoperatively, and the diagnosis was made by radiologists independently and blindly. Intraoperative ultrasonography (IOUS) and CE-IOUS with Sonazoid were done by the same sophisticated surgeon and sonographer and Kupffer phase was used to detect lesions. The sensitivity and specificity to detect lesions were compared between different radiologic methods. Then, the changes in treatment strategy due to CE-IOUS with Sonazoid were analysed. RESULTS: Twenty-seven patients were included. In MRI, 91 lesions were detected with sensitivity 93.3% (70/75) and specificity 68.8% (11/16). In CEUS, it was 97.1% (68/70) and 86.7% (13/15) in 85 lesions. Meanwhile, in the Kupffer phase in CE-IOUS, 99 lesions were found and 8 new lesions were discovered in 7 cases, with sensitivity 97.5% (80/82) and specificity 94.1% (16/17). The four imaging methods showed no statistic significance in sensitivity and specificity in detecting lesions (Cochran's Q 10.825, P=0.055). Treatment strategies were altered in 7 patients, 6 achieved R0 resection or ablation, and 1 patient changed from planned R0 resection to palliative surgery. CONCLUSION: CE-IOUS may play a similar or even better role than other radiological methods in diagnosing liver metastasis. The CE-IOUS using Sonazoid demonstrated a high sensitivity and specificity for finding occult metastases intraoperatively and changing the treatment strategy.

Age-Related Changes in Shear Wave Elastography Parameters of the Gastrocnemius Muscle in Association with Physical Performance in Healthy Adults.

BACKGROUND/AIMS: to investigate new indicators for early recognition of physical performance decline. Shear wave elastrography, a new ultrasound technique, was discussed in this study. METHODS: Gastrocnemius muscle thickness and muscle stiffness were detected by traditional ultrasound and shear wave elastrography in 108 Chinese aged 20-85 years, and then analyzed with physical performance together. RESULTS: After 70 years old, the decline rate of muscle stiffness under contractive state was significantly faster than that of muscle thickness, muscle relaxed stiffness, and physical performance indicators. The correlation analysis showed that gastrocnemius contractive stiffness was positively related with handgrip strength, step length, and fast gait speed after adjusted by age and gender. Among physical performance variants, step length had closer relationship with muscle strength than repeated chair stands. CONCLUSIONS: The detection of gastrocnemius muscle by shear wave elastography reflected the change of lower-limb muscle stiffness with aging. Muscle contractive stiffness and step length measurement supplied novel ways for muscle performance and motor function assessment.

Pan-Cancer Classification Based on Self-Normalizing Neural Networks and Feature Selection.

Cancer is a one of the severest diseases and cancer classification plays an important role in cancer diagnosis and treatment. Some different cancers even have similar molecular features such as DNA copy number variant. Pan-cancer classification is still non-trivial at molecular level. Herein, we propose a computational method to classify cancer types by using the self-normalizing neural network (SNN) for analyzing pan-cancer copy number variation data. Since the dimension of the copy number variation features is high, the Monte Carlo feature selection method was used to rank these features. Then a classifier was built by SNN and feature selection method to select features. Three thousand six hundred ninety-four features were chosen for the prediction model, which yields the accuracy value is 0.798 and macro F1 is 0.789. We compared our model to random forest method. Results show the accuracy and macro F1 obtained by our classifier are higher than those obtained by random forest classifier, indicating the good predictive power of our method in distinguishing four different cancer types. This method is also extendable to pan-cancer classification for other molecular features.

Comparison of the effects of twice-daily exenatide and insulin on carotid intima-media thickness in type 2 diabetes mellitus patients: a 52-week randomized, open-label, controlled trial.

BACKGROUND: Exenatide, a glucagon like peptide 1 analog, has been suggested to reduce the cardiovascular disease risk factors, such as body weight, blood pressure and subclinical atherosclerosis in patients with type 2 diabetes mellitus (T2DM). This was the first randomized, open-label, controlled trial to compare the effects of exenatide versus insulin on subclinical atherosclerosis, as assessed by carotid-intima media thickness (CIMT), in patients with T2DM. METHODS: A total of 66 patients with T2DM admitted from March 10, 2015 to June 20, 2017 in the Department of Endocrinology, Beijing Hospital were randomized to receive twice-daily exenatide or aspartate 70/30 insulin for 52 weeks. The primary endpoint was change from baseline in CIMT, and secondary endpoints included changes at week 52 from baseline in body weight, glycemic markers, lipid metabolism markers, blood pressure, C-reactive protein, fibrinogen, 8-hydroxydeoxyguanosine, irisin, and brain natriuretic peptide. RESULTS: Exenatide more significantly reduced the CIMT from baseline compared with insulin after 52 weeks, with a mean difference of - 0.14 mm (95% interval confidence: - 0.25, - 0.02; P = 0.016). Weight and body mass index were both significantly reduced in the exenatide group over 52 weeks. Exenatide reduced total lipoprotein and low-density lipoprotein cholesterol levels more significantly than insulin at weeks 16 and 40. Correlation analyses showed that CIMT was positively correlated with low-density lipoprotein cholesterol. CONCLUSIONS: Twice-daily exenatide could prevent atherosclerosis progression in patients with T2DM over a 52-week treatment period compared with insulin therapy. Trial registration Chinese Clinical Trial Registry ChiCTR-1800015658.