Pharmacokinetics and bioequivalence study of candesartan cilexetil tablet in Chinese volunteers under fasting condition: an open-label, randomized-sequence, 2-period crossover study.

Candesartan is an antihypertensive agent that acts on an angiotensin II receptor. Candesartan cilexetil is a prodrug that is converted into the active form of candesartan during intestinal absorption. This study aimed to assess the pharmacokinetics and bioequivalence of a reference and a test formulation of candesartan cilexetil tablets in healthy Chinese volunteers. A randomized, open-label, single-dose, crossover study was conducted with two treatment periods. Forty-eight healthy Chinese volunteers participated under fasted conditions. Qualified subjects were randomly divided into two groups (1:1 ratio) to receive either the test or reference formulation first. A washout period of 14 days separated the administration of the two formulations. Blood samples were collected at specific time points and analyzed for candesartan concentration using Ultra High-Performance Liquid Chromatography Tandem Mass Spectrometry (UPLC-MS/MS). The maximum concentration (Cmax), the AUC from time zero to the last measured time point (AUC0-t) and the AUC from time zero to infinity (AUC0-∞) fell within the bioequivalence range of 80% to 125%. These results suggest that the test and reference formulations of candesartan cilexetil tablets are bioequivalent, meaning they have similar rates and extents of absorption in healthy Chinese volunteers. No serious adverse events or side effects were reported throughout the study.

The Protective Effect of Sulodexide on Acute Lung Injury Induced by a Murine Model of Obstructive Jaundice.

Introduction: The effect of sulodexide (SLX) on obstructive jaundice- (OJ-) induced acute lung injury (ALI) in rats was examined in this study. Methods: In this study, 48 rats were randomly assigned to one of six groups: sham, OJ, OJ+saline, OJ+SLX (0.5 mg/ml/d), OJ+SLX (1 mg/ml/d), and OJ+SLX (2 mg/ml/d). The pathological lung injury was assessed by histological analysis and lung injury grading. ELISA kits were used to evaluate the expression of IL-6, IL-1, TNF-α, and syndecan-1 (SDC-1) in bronchoalveolar lavage fluids (BALFs). Commercial assay kits were performed to evaluate malondialdehyde (MDA) production and catalase (CAT) activity in lung tissues. The apoptosis was assessed by TUNEL assay. The lung microvascular permeability was investigated using Evans blue leakage, lung wet/dry weight (W/D) ratio, and lung permeability index (LPI). SDC-1, claudin-5, ZO-1, and VE cadherin expression levels in lung tissues were measured using Western blot. Results: The OJ-induced ALI rats showed severe lung injury. The value of IL-6, IL-1ß, TNF-α, and SDC-1 in BALFs was remarkedly increased in the OJ group. MDA content, apoptotic area, apoptotic molecules, and SDC-1 level were all higher in the OJ group's lung tissues than in the sham group. CAT activity, Evans blue leakage, W/D ratio, LPI, and expression of claudin-5, ZO-1, and VE cadherin were all lower in the OJ group compared to the sham group. The degenerative alterations in lung tissue improved after 7 days of treatment with 2 mg/ml SLX. The BALFs had lower amounts of IL-6, IL-1, TNF-α, and SDC-1. The SLX therapy reduced MDA levels while restoring CAT activity. In lung tissues, SLX reduced apoptotic area and SDC-1 expression. SLX reduced lung microvascular permeability by raising the expression of Claudin-5, ZO-1, and VE-cadherin in lung tissue when compared to the OJ group. Conclusion: The results suggested that SLX attenuates OJ-induced ALI in rats by protecting the pulmonary microvascular endothelial barrier.

Automatic Pixel-Level Pavement Crack Recognition Using a Deep Feature Aggregation Segmentation Network with a scSE Attention Mechanism Module.

Pavement crack detection is essential for safe driving. The traditional manual crack detection method is highly subjective and time-consuming. Hence, an automatic pavement crack detection system is needed to facilitate this progress. However, this is still a challenging task due to the complex topology and large noise interference of crack images. Recently, although deep learning-based technologies have achieved breakthrough progress in crack detection, there are still some challenges, such as large parameters and low detection efficiency. Besides, most deep learning-based crack detection algorithms find it difficult to establish good balance between detection accuracy and detection speed. Inspired by the latest deep learning technology in the field of image processing, this paper proposes a novel crack detection algorithm based on the deep feature aggregation network with the spatial-channel squeeze & excitation (scSE) attention mechanism module, which calls CrackDFANet. Firstly, we cut the collected crack images into 512 × 512 pixel image blocks to establish a crack dataset. Then through iterative optimization on the training and validation sets, we obtained a crack detection model with good robustness. Finally, the CrackDFANet model verified on a total of 3516 images in five datasets with different sizes and containing different noise interferences. Experimental results show that the trained CrackDFANet has strong anti-interference ability, and has better robustness and generalization ability under the interference of light interference, parking line, water stains, plant disturbance, oil stains, and shadow conditions. Furthermore, the CrackDFANet is found to be better than other state-of-the-art algorithms with more accurate detection effect and faster detection speed. Meanwhile, our algorithm model parameters and error rates are significantly reduced.

Computer Vision-Based Bridge Damage Detection Using Deep Convolutional Networks with Expectation Maximum Attention Module.

Cracks and exposed steel bars are the main factors that affect the service life of bridges. It is necessary to detect the surface damage during regular bridge inspections. Due to the complex structure of bridges, automatically detecting bridge damage is a challenging task. In the field of crack classification and segmentation, convolutional neural networks have offer advantages, but ordinary networks cannot completely solve the environmental impact problems in reality. To further overcome these problems, in this paper a new algorithm to detect surface damage called EMA-DenseNet is proposed. The main contribution of this article is to redesign the structure of the densely connected convolutional networks (DenseNet) and add the expected maximum attention (EMA) module after the last pooling layer. The EMA module is obviously helpful to the bridge damage feature extraction. Besides, we use a new loss function which considers the connectivity of pixels, it has been proved to be effective in reducing the break point of fracture prediction and improving the accuracy. To train and test the model, we captured many images from multiple bridges located in Zhejiang (China), and then built a dataset of bridge damage images. First, experiments were carried out on an open concrete crack dataset. The mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and frames per second (FPS) of the EMA-DenseNet are 87.42%, 92.59%, 81.97% and 25.4, respectively. Then we also conducted experiments on a more challenging bridge damage dataset, the MIoU, where MPA, precision and FPS were 79.87%, 86.35%, 74.70% and 14.6, respectively. Compared with the current state-of-the-art algorithms, the proposed algorithm is more accurate and robust in bridge damage detection.

The TRPA1 Channel Mediates Mechanical Allodynia and Thermal Hyperalgesia in a Rat Bone Cancer Pain Model.

Background: Bone cancer pain (BCP) significantly affects patient quality of life, results in great bodily and emotional pain, and creates difficulties in follow-up treatment and normal life. Transient receptor potential ankyrin 1 (TRPA1) is an essential transduction ion channel related to neuropathic and inflammatory pain. However, the role of TRPA1 in BCP remains poorly understood. This study aimed to explore the relationship between TRPA1 and BCP. Methods: A BCP model was induced by Walker256 cells to the left tibia. The sham group was induced by normal saline to the left tibia. Thereafter, pain behaviors and TRPA1 expression between the BCP group and the sham group were observed on the 14th day of modeling. The TRPA1 antagonist A967079 (10 mg/kg) was injected via tail vein. TRPA1 antisense oligodeoxynucleotide (AS-ODN, 5 nmol/10 µl) and missense oligodeoxynucleotide (MS-ODN, 5 nmol/10 µl) were intrathecally delivered via a mini-osmotic pump for 5 consecutive days to assess the effect of TRPA1 on BCP. Behavioral tests were assessed preoperatively and postoperatively. Real-time quantitative PCR and western blot analyses were used to measure TRPA1 levels among the different groups. Results: The BCP model was successfully established via X-ray and pathological sections at 14 days. Compared to the sham group, the BCP group was more sensitive to mechanical stimuli, cool stimuli and hot stimuli. Intravenously injected A967079 can relieve paw mechanical withdrawal threshold and paw withdrawal thermal latency in rats with BCP. Moreover, AS-ODN can relieve paw mechanical withdrawal threshold and paw withdrawal thermal latency in rats with BCP. Additionally, relative mRNA and protein expression of TRPA1 in the BCP group were much higher than those in the sham group (14.55 ± 1.97 vs. 1 ± 0.04, P < 0.01). Compared to the BCP group, the relative mRNA and protein expression of TRPA1 in the BCP+AS-ODN group was reduced (14.55 ± 1.97 vs. 2.59 ± 0.34, P < 0.01). Conclusions: The TRPA1 channel mediates mechanical allodynia and thermal hyperalgesia in a rat BCP model.

Propofol inhibits the expression of Abelson nonreceptor tyrosine kinase without affecting learning or memory function in neonatal rats.

OBJECTIVE: Propofol is one of the most commonly used intravenous drugs to induce and maintain general anesthesia. In vivo and in vitro studies have shown that propofol can affect neuronal growth, leading to apoptosis and impairing cognitive function. The Abelson nonreceptor tyrosine kinase (c-Abl) is associated with both neuritic plaques and neurofibrillary tangles in the brains of patients with Alzheimer's disease and other neurodegenerative diseases. This study aimed to explore the effect of propofol on apoptosis and neurocognition through its regulation of c-Abl expression in vivo and in vitro. MATERIALS AND METHODS: In this study, primary hippocampal neurons were cultured and exposed to propofol at different concentrations. Protein expression was measured by Western blotting and coimmunoprecipitation. The c-Abl transcription level was verified by fluorescence quantitative PCR. Reactive oxygen species (ROS) levels were detected by flow cytometry. In addition, an animal experiment was conducted to assess neuronal apoptosis by immunofluorescence staining for caspase-3 and to evaluate behavioral changes by the Morris water maze (MWM) test. RESULTS: The in vitro experiment showed that propofol significantly decreased c-Abl expression and ROS levels. In addition, propofol has no cytotoxic effect and does not affect cell activity. Moreover, in the animal experiment, intraperitoneal injection of 50 mg/kg propofol for 5 days obviously decreased the expression of c-Abl in the neonatal rat brain (p < .05) but did not significantly increase the number of caspase-3-positive cells. Propofol treatment did not significantly reduce the number of platform crossings (p > .05) or prolong the escape latency of neonatal rats (p > .05) in the MWM test. CONCLUSIONS: The present data suggest that reduced expression of this nonreceptor tyrosine kinase through consecutive daily administration of propofol did not impair learning or memory function in neonatal rats.

Automatic Tunnel Crack Detection Based on U-net and a Convolutional Neural Network with Alternately Updated Clique.

Regular crack inspection of tunnels is essential to guarantee their safe operation. At present, the manual detection method is time-consuming, subjective and even dangerous, while the automatic detection method is relatively inaccurate. Detecting tunnel cracks is a challenging task since cracks are tiny, and there are many noise patterns in the tunnel images. This study proposes a deep learning algorithm based on U-Net and a convolutional neural network with alternately updated clique (CliqueNet), called U-CliqueNet, to separate cracks from background in the tunnel images. A consumer-grade DSC-WX700 camera (SONY, Wuxi, China) was used to collect 200 original images, then cracks are manually marked and divided into sub-images with a resolution of 496 × 496 pixels. A total of 60,000 sub-images were obtained in the dataset of tunnel cracks, among which 50,000 were used for training and 10,000 were used for testing. The proposed framework conducted training and testing on this dataset, the mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and F1-score are 92.25%, 86.96%, 86.32% and 83.40%, respectively. We compared the U-CliqueNet with fully convolutional networks (FCN), U-net, Encoder-decoder network (SegNet) and the multi-scale fusion crack detection (MFCD) algorithm using hypothesis testing, and it's proved that the MIoU predicted by U-CliqueNet was significantly higher than that of the other four algorithms. The area, length and mean width of cracks can be calculated, and the relative error between the detected mean crack width and the actual mean crack width ranges from -11.20% to 18.57%. The results show that this framework can be used for fast and accurate crack semantic segmentation of tunnel images.

Modified American Joint Committee on Cancer Tumor-Node-Metastasis Staging System Based on the Node Ratio Can Further Improve the Capacity of Prognosis Assessment for Gastric Cancer Patients.

Background and Objectives: Our aim was to investigate whether the modified American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system based on the node ratio can further improve the capacity of prognosis assessment for gastric cancer (GC) patients regardless of the number of lymph nodes examined (eLNs). Methods: A total of 17,187 GC patients in the Surveillance, Epidemiology, and End Results (SEER) database were included. On the basis of a training set of 7,660 GC patients, we built the tumor-node ratio-metastasis (TNrM) staging system, which was then externally validated with a validation set of 9,527 GC patients. Results: For the training set, the C-index value of the TNrM staging system was significantly higher than that of the AJCC 8th TNM staging system to predict survival for GC patients (C-index: 0.688 vs. 0.671, P < 0.001). Moreover, the C-index value of the TNrM staging system was significantly higher than that of the 8th TNM staging system to predict survival for GC patients with ≤15 eLNs (C-index: 0.682 vs. 0.673, P < 0.001), as well as for GC patients with >15 eLNs (C-index: 0.700 vs. 0.694, P < 0.001). Similar results were found in the validation set. Conclusions: The TNrM staging system predicted survival more accurately and discriminatively than the AJCC 8th TNM staging system for GC patients regardless of the number of eLNs.

Safety of Sulfur Hexafluoride Microbubbles in Sonography of Abdominal and Superficial Organs: Retrospective Analysis of 30,222 Cases.

OBJECTIVES: The purpose of this study was to investigate the safety of the sulfur hexafluoride microbubble contrast agent SonoVue (Bracco SpA, Milan, Italy) and to implement precautions with the intent of further improving the safety of this contrast agent. METHODS: A total of 30,222 patients undergoing contrast-enhanced sonography of abdominal and superficial organs in our hospital from January 2005 to December 2014 were retrospectively investigated. SonoVue was used as the ultrasound contrast agent. The symptoms and treatments of adverse reactions occurring during the contrast-enhanced sonographic examinations were reviewed and analyzed. RESULTS: No patient died as a result of any adverse reaction. Six patients (0.020%) had adverse reactions of varying degrees, including 2 patients (0.007%) who had signs of early anaphylactic shock (chest tightness, palpitations, sweating, and rapid and weak pulse, followed by cyanosis, a disappearing pulse, and a drop in blood pressure) that improved after active rescue. The remaining 4 patients developed the following: redness and a rash on the arm above the injection site, nasal bleeding and nausea, nausea and vomiting, and back pain with numbness of the lips and limbs. Symptoms in these 4 patients self-resolved after a period of rest. CONCLUSIONS: Contrast-enhanced sonography with sulfur hexafluoride microbubbles had good clinical safety, but rare adverse reactions were observed. A comprehensive emergency plan and rescue measures for adverse reactions should be prepared and made available to minimize the occurrence of negative clinical outcomes.

ß-Arabinofuranosylation using 5-O-(2-quinolinecarbonyl) substituted ethyl thioglycoside donors.

A new ß-stereoselective D- and L-arabinofuranosylation method has been developed employing 5-O-(2-quinolinecarbonyl) substituted arabinosyl ethyl thioglycosides as glycosyl donors. The approach allows a wide range of acceptor substrates to be used; the ß-selectivity is good-to-excellent. Stereoselective synthesis of a mannose-capped octasaccharide portion from a mycobacterial cell wall polysaccharide was then carried out to demonstrate the utility of this methodology.

One-pot synthesis of branched oligosaccharides by use of galacto- and mannopyranosyl thioglycoside diols as key glycosylating agents.

We describe in this paper the efficient four-component one-pot synthesis of three fully protected oligosaccharides 22, 36, and 50 with di-branched structures by employing D-galacto- and mannopyranosyl thioglycoside diols as central glycosylating agents. After global deprotection, they were converted respectively into the 3-aminopropyl linker-containing free oligosaccharide fragments 14, 24, and 38 structurally related to cell wall oligosaccharides from Atractylodes lancea DC, the marine fungus Lineolata rhizophorae and pathogenic Mycobacterium tuberculosis. The 3-aminopropyl linker at the anomeric carbon can enable conjugation of these synthetic oligomers to a suitable protein carrier.