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  • Hindawi Limited  (7)
  • 1
    Online Resource
    Online Resource
    Clusters of 2019 coronavirus disease (COVID‐19) cases in Chinese tour groups
    Hindawi Limited ; 2021
    In:  Transboundary and Emerging Diseases Vol. 68, No. 2 ( 2021-03), p. 684-691
    Details
    In: Transboundary and Emerging Diseases, Hindawi Limited, Vol. 68, No. 2 ( 2021-03), p. 684-691
    Type of Medium: Online Resource
    ISSN: 1865-1674 , 1865-1682
    URL: Issue
    URL: Article
    DOI: 10.1111/tbed.v68.2
    DOI: 10.1111/tbed.13729
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2414822-2
    SSG: 22
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  • 2
    Online Resource
    Online Resource
    Interleukin 10 Attenuates Angiotensin II-Induced Aortic Remodelling by Inhibiting Oxidative Stress-Induced Activation of the Vascular p38 and NF-κB Pathways
    Hindawi Limited ; 2022
    In:  Oxidative Medicine and Cellular Longevity Vol. 2022 ( 2022-4-26), p. 1-15
    Details
    In: Oxidative Medicine and Cellular Longevity, Hindawi Limited, Vol. 2022 ( 2022-4-26), p. 1-15
    Abstract: Interleukin 10 (IL-10) is a probable anti-inflammatory factor that can attenuate hypertrophic remodelling caused by overloaded pressure and improve cardiac function. In this study, IL-10 was decreased in both the plasma of hypertensive patients and the aortic vessels of angiotensin II (Ang II)-induced hypertensive mice. IL-10 was unable to alter blood pressure in the case of Ang II-induced hypertension. The aortic thickness, collagen deposition, and the levels of fibrosis-associated markers, including collagen type I α 1 (Col1α1), connective tissue growth factor (CTGF), transforming growth factor-β (TGF-β), and matrix metalloproteinase 2 (MMP2), were significantly reduced in the IL-10 treatment group compared with the vehicle group after Ang II treatment. Moreover, IL-10 treatment significantly inhibited the number of CD45+ positive cells and the mRNA expression levels of proinflammatory cytokines in the vascular tissue of Ang II-infused mice. Furthermore, dihydroethidium (DHE) and 4hydroxynonenal (4-HNE) staining showed that IL-10 decreased Ang II-induced vascular oxidative stress and lipid peroxidation. Furthermore, IL-10 suppressed Ang II-induced proliferation, fibrosis, and inflammation of mouse vascular adventitial fibroblasts (mVAFs). Mechanistically, IL-10 suppressed the phosphorylation of p38 mitogen-activated protein (MAP) kinase and nuclear factor-κB (NF-κB) in Ang II-induced vascular fibrosis. In summary, our data indicated that IL-10, as a potential therapeutic target treatment, could limit the progression of Ang II-induced aortic remodelling.
    Type of Medium: Online Resource
    ISSN: 1942-0994 , 1942-0900
    URL: Article
    DOI: 10.1155/2022/8244497
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2455981-7
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  • 3
    Online Resource
    Online Resource
    DL-MRI: A Unified Framework of Deep Learning-Based MRI Super Resolution
    Hindawi Limited ; 2021
    In:  Journal of Healthcare Engineering Vol. 2021 ( 2021-4-9), p. 1-9
    Details
    In: Journal of Healthcare Engineering, Hindawi Limited, Vol. 2021 ( 2021-4-9), p. 1-9
    Abstract: Magnetic resonance imaging (MRI) is widely used in the detection and diagnosis of diseases. High-resolution MR images will help doctors to locate lesions and diagnose diseases. However, the acquisition of high-resolution MR images requires high magnetic field intensity and long scanning time, which will bring discomfort to patients and easily introduce motion artifacts, resulting in image quality degradation. Therefore, the resolution of hardware imaging has reached its limit. Based on this situation, a unified framework based on deep learning super resolution is proposed to transfer state-of-the-art deep learning methods of natural images to MRI super resolution. Compared with the traditional image super-resolution method, the deep learning super-resolution method has stronger feature extraction and characterization ability, can learn prior knowledge from a large number of sample data, and has a more stable and excellent image reconstruction effect. We propose a unified framework of deep learning -based MRI super resolution, which has five current deep learning methods with the best super-resolution effect. In addition, a high-low resolution MR image dataset with the scales of ×2, ×3, and ×4 was constructed, covering 4 parts of the skull, knee, breast, and head and neck. Experimental results show that the proposed unified framework of deep learning super resolution has a better reconstruction effect on the data than traditional methods and provides a standard dataset and experimental benchmark for the application of deep learning super resolution in MR images.
    Type of Medium: Online Resource
    ISSN: 2040-2309 , 2040-2295
    URL: Article
    DOI: 10.1155/2021/5594649
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2545054-2
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  • 4
    Online Resource
    Online Resource
    PSR: Unified Framework of Parameter-Learning-Based MR Image Superresolution
    Hindawi Limited ; 2021
    In:  Journal of Healthcare Engineering Vol. 2021 ( 2021-4-21), p. 1-14
    Details
    In: Journal of Healthcare Engineering, Hindawi Limited, Vol. 2021 ( 2021-4-21), p. 1-14
    Abstract: Magnetic resonance imaging has significant applications for disease diagnosis. Due to the particularity of its imaging mechanism, hardware imaging suffers from resolution and reaches its limit, and higher radiation intensity and longer radiation time will cause damage to the human body. The problem is expected to be solved by a superresolution algorithm, especially the image superresolution based on sparse reconstruction has good performance. Dictionary generation is a key issue that affects the performance of superresolution algorithms, and dictionary performance is affected by dictionary construction parameters: balance parameters, dictionary size, overlapping block size, and a number of training sample blocks. In response to this problem, we propose an optimal dictionary construction parameter search method through the experiment to find the optimal dictionary construction parameters on the MR image and compare them with the dictionary obtained by multiple sets of random dictionary construction parameters. The dictionary we searched for the optimal parameters of the dictionary construction training has more powerful feature expressions, which can improve the superresolution effect of MR images.
    Type of Medium: Online Resource
    ISSN: 2040-2309 , 2040-2295
    URL: Article
    DOI: 10.1155/2021/5591660
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2545054-2
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  • 5
    Online Resource
    Online Resource
    Traditional Chinese Medicine Syndromes are Associated with Driver Gene Mutations and Clinical Characteristics in Patients with Lung Adenocarcinoma
    Hindawi Limited ; 2022
    In:  Evidence-Based Complementary and Alternative Medicine Vol. 2022 ( 2022-10-7), p. 1-8
    Details
    In: Evidence-Based Complementary and Alternative Medicine, Hindawi Limited, Vol. 2022 ( 2022-10-7), p. 1-8
    Abstract: This study aimed to investigate the associations between traditional Chinese medicine (TCM) syndromes and driver gene mutations as well as the clinical characteristics of patients with lung adenocarcinoma. We performed a cross-sectional study in patients with lung adenocarcinoma between June 2020 and October 2021. The patient characteristics, such as age, sex, smoking history, clinical stage, metastasis, driver gene mutations, and the type of traditional Chinese medicine syndrome/element, were collected. The associations between each TCM syndrome and sex, smoking history, clinical stage, metastasis, and driver gene mutations were analyzed. The present study included 127 patients. The most frequent TCM syndromes were Qi and Yin deficiency (39, 30.7%) and lung-spleen Qi deficiency (32, 25.2%). Eighty-one (63.8%) patients had mutations in driver genes, especially in the EGFR gene (64, 79.0%). There was a statistically significant association between a driver gene mutation and TCM syndrome ( P 〈 0.05 ). Genetic mutations presented more frequently in patients with Qi and Yin deficiency (37.0%), lung-spleen Qi deficiency (30.0%), or the cold element (59.3%). Male patients were more likely to have Qi stagnation and blood stasis, whereas female patients were more likely to have lung-spleen Qi deficiency or Qi and Yin deficiency. The patients with lung-spleen Qi deficiency were usually younger than those with Qi and Yin deficiency or Qi stagnation and blood stasis ( P 〈 0.05 ). Compared with the patients with other TCM syndromes, the patients with Yin and Yang deficiency were more likely to have bone metastasis. TCM syndromes were associated with driver gene mutations, sex, age, and bone metastasis in patients with lung adenocarcinoma.
    Type of Medium: Online Resource
    ISSN: 1741-4288 , 1741-427X
    URL: Article
    DOI: 10.1155/2022/9905868
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2148302-4
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  • 6
    Online Resource
    Online Resource
    Sparse Dictionary-Based Magnetic Resonance Superresolution Imaging with Joint Loss Function Learning
    Hindawi Limited ; 2022
    In:  Journal of Healthcare Engineering Vol. 2022 ( 2022-8-29), p. 1-17
    Details
    In: Journal of Healthcare Engineering, Hindawi Limited, Vol. 2022 ( 2022-8-29), p. 1-17
    Abstract: Magnetic resonance image has important application value in disease diagnosis. Due to the particularity of its imaging mechanism, the resolution of hardware imaging needs to be improved by increasing radiation intensity and radiation time. Excess radiation can cause the body to overheat and, in severe cases, inactivate the protein. This problem is expected to be solved by the image superresolution method based on joint dictionary learning, which has good superresolution performance. In the process of dictionary learning, the loss function will directly affect the dictionary performance. The general method only uses the cascade error as the optimization function in dictionary training, and the method does not consider the individual reconstruction error of high- and low-resolution image dictionary. In order to solve the above problem, In this paper, the loss function of dictionary learning is optimized. While ensuring that the coefficients are sufficiently sparse, the high- and low-resolution dictionaries are trained separately to reduce the error generated by the joint high- and low-resolution dictionary block pair and increase the high-resolution reconstruction error. Experiments on neck and ankle MR images show that the proposed algorithm has better superresolution reconstruction performance on ×2 and ×4 compared with bicubic interpolation, nearest neighbor, and original dictionary learning algorithms.
    Type of Medium: Online Resource
    ISSN: 2040-2309 , 2040-2295
    URL: Article
    DOI: 10.1155/2022/2206454
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2545054-2
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  • 7
    Online Resource
    Online Resource
    Parallel CNN Network Learning-Based Video Object Recognition for UAV Ground Detection
    Hindawi Limited ; 2022
    In:  Wireless Communications and Mobile Computing Vol. 2022 ( 2022-4-14), p. 1-19
    Details
    In: Wireless Communications and Mobile Computing, Hindawi Limited, Vol. 2022 ( 2022-4-14), p. 1-19
    Abstract: Video object recognition for UAV ground detection is widely used in target search, daily patrol, environmental reconnaissance, and other fields. So, we propose the novel parallel deep learning network with the ability of the global and local joint feature extraction for the UAV video target detection. This paper focuses on solving the problems of feature extraction and target background discrimination required by target discovery to realize target discovery. Break through the key problems of real-time target recognition, such as multiscale targets, high background complexity, many small targets, dense target arrangement, and multidirection, and put forward an optimized network scheme, aiming at the problem of multiscale of image target and aiming at the problem of large change of target scale in image. In the network, the corresponding targets with different sizes and different aspect ratios are matched to make the different targets match the closest, and then, the position of the detection box is fine-tuned by regression. For the special problem of image viewing angle and for the rotation invariance of the airborne down looking image of the target, the usual solution is through data enhancement; that is, through the rotation transformation of the training data, the neural network can learn the rotation invariance of the target. Aiming at the problem of multi-directional image target and aiming at the problems of large target aspect ratio, large target tilt angle, and changeable direction in the target, we propose to use the tilt detection frame instead of the ordinary rectangular detection frame. Aiming at the problem of dense arrangement of image targets and aiming at a large number of densely arranged targets in the image, a feature refining module is proposed, which can effectively improve the detection performance of the detector for densely arranged targets. The experimental results shows that the proposed algorithm achieves more than 10% on the target detection accuracy with focal length change of 1-10 times. The detection accuracy meets the requirements of practical application.
    Type of Medium: Online Resource
    ISSN: 1530-8677 , 1530-8669
    URL: Article
    DOI: 10.1155/2022/2701217
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2045240-8
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