Галерея 3221531

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1 Department of Pharmacology, Tohoku University School of Dentistry, Senda, Japan.

K Onodera et al.

Jpn J Pharmacol .

1988 Jul .

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1 Department of Pharmacology, Tohoku University School of Dentistry, Senda, Japan.

Onodera K, Shinoda H, Watanabe T.
Onodera K, et al.
Jpn J Pharmacol. 1990 Nov;54(3):339-43. doi: 10.1254/jjp.54.339.
Jpn J Pharmacol. 1990.

PMID: 2090843

Onodera K, Maeyama K, Watanabe T.
Onodera K, et al.
Agents Actions. 1989 Apr;27(1-2):120-2. doi: 10.1007/BF02222216.
Agents Actions. 1989.

PMID: 2750584

Meng JS, Okeda R.
Meng JS, et al.
Neuropathology. 2003 Mar;23(1):25-35. doi: 10.1046/j.1440-1789.2003.00481.x.
Neuropathology. 2003.

PMID: 12722923

Onodera K, Yamatodani A, Watanabe T.
Onodera K, et al.
Methods Find Exp Clin Pharmacol. 1993 Sep;15(7):423-7.
Methods Find Exp Clin Pharmacol. 1993.

PMID: 8255120

Van Woert MH, Plaitakis A, Hwang EC, Berl S.
Van Woert MH, et al.
Brain Res. 1979 Dec 21;179(1):103-10. doi: 10.1016/0006-8993(79)90494-3.
Brain Res. 1979.

PMID: 509224

Langlais PJ, McRee RC, Nalwalk JA, Hough LB.
Langlais PJ, et al.
Metab Brain Dis. 2002 Sep;17(3):199-210. doi: 10.1023/a:1019930206196.
Metab Brain Dis. 2002.

PMID: 12322789

Yokoyama H, Onodera K, Maeyama K, Yanai K, Iinuma K, Tuomisto L, Watanabe T.
Yokoyama H, et al.
Naunyn Schmiedebergs Arch Pharmacol. 1992 Jul;346(1):40-5. doi: 10.1007/BF00167568.
Naunyn Schmiedebergs Arch Pharmacol. 1992.

PMID: 1407004

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Histamine levels in thiamine deficient rats were significantly lower in the hippocampus, amygdala, olfactory bulb, thalamus and pons-medulla oblongata than those of normal and pair-fed groups. In the case of the hypothalamus, thiamine deficiency produced a significant increase in histamine levels. These changes observed in the thiamine deficient group were reversed to the normal levels by supplying the normal diet. These data present a new finding that thiamine deficiency affects the central histaminergic neuron system as well as other monoaminergic systems.

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Sun Jan 15, 2023 01:26 PM Last update on: Sun Jan 15, 2023 02:19 PM

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Database Description: Chest X-Ray Images
An example of the Vision Transformer's Attention Maps on a COVID-19 chest X-ray image.
Abstract: Coronavirus disease (COVID-19) was confirmed as a pandemic disease on February 11, 2020. The pandemic has already caused thousands of victims and infected several million... View more
Coronavirus disease (COVID-19) was confirmed as a pandemic disease on February 11, 2020. The pandemic has already caused thousands of victims and infected several million people around the world. The aim of this work is to provide a Covid-19 infection screening tool. Currently, the most widely used clinical tool for detecting the presence of infection is the reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less sensitive and requires the resource of specialized medical personnel. The use of X-ray images represents one of the latest challenges for the rapid diagnosis of the Covid-19 infection. This work involves the use of advanced artificial intelligence techniques for diagnosis using algorithms for classification purposes. The goal is to provide an automatic infection detection method while maximizing detection accuracy. A public database was used which includes images of COVID-19 patients, patients with viral pneumonia, patients with pulmonary opacity, and healthy patients. The methodology used in this study is based on transfer learning of pre-trained networks to alleviate the complexity of calculation. In particular, three different types of convolutional neural networks, namely, InceptionV3, ResNet50 and Xception, and the Vision Transformer are implemented. Experimental results show that the Vision Transformer outperforms convolutional architectures with a test accuracy of 99.3% vs 85.58% for ResNet50 (best among CNNs). Moreover, it is able to correctly distinguish among four different classes of chest X-ray images, whereas similar works only stop at three categories at most. The high accuracy of this computer-assisted diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis.
Published in: IEEE Access ( Volume: 10 )
Date of Publication: 11 November 2022
An example of the Vision Transformer's Attention Maps on a COVID-19 chest X-ray image.
TABLE 1
Performance comparison between vision transformer and convolutional neural network architectures
TABLE 2
Precision, recall and f1-score for vision transformer
E. J. Lefkowitz, D. M. Dempsey, R. C. Hendrickson, R. J. Orton, S. G. Siddell and D. B. Smith, "Virus taxonomy: The database of the international committee on taxonomy of viruses (ICTV)", Nucleic Acids Res. , vol. 46, no. D1, pp. D708-D717, Jan. 2018.
A. Abbas, M. M. Abdelsamea and M. M. Gaber, "Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network", Int. J. Speech Technol. , vol. 51, no. 2, pp. 854-864, Sep. 2020.
A. Borakati, A. Perera, J. Johnson and T. Sood, "Diagnostic accuracy of X-ray versus CT in COVID-19: A propensity-matched database study", BMJ Open , vol. 10, no. 11, Nov. 2020.
C. Schaefer-Prokop and M. Prokop, "Chest radiography in COVID-19: No role in asymptomatic and oligosymptomatic disease", Radiology , vol. 298, no. 3, pp. E156-E157, Mar. 2021.
D. Dong, Z. Tang, S. Wang, H. Hui, L. Gong, Y. Lu, et al., "The role of imaging in the detection and management of COVID-19: A review", IEEE Rev. Biomed. Eng. , vol. 14, pp. 16-29, 2021.
A. M. Ismael and A. Şengür, "Deep learning approaches for COVID-19 detection based on chest X-ray images", Expert Syst. Appl. , vol. 164, Feb. 2021.
I. Katsamenis, E. Protopapadakis, A. Voulodimos, A. Doulamis and N. Doulamis, "Transfer learning for COVID-19 pneumonia detection and classification in chest X-ray images", Proc. 24th Pan-Hellenic Conf. Informat. , pp. 170-174, Nov. 2020.
A. Shazia, T. Z. Xuan, J. H. Chuah, J. Usman, P. Qian and K. W. Lai, "A comparative study of multiple neural network for detection of COVID-19 on chest X-ray", EURASIP J. Adv. Signal Process. , vol. 2021, no. 1, pp. 50, Dec. 2021.
S. Vineth Ligi, S. S. Kundu, R. Kumar, R. Narayanamoorthi, K. W. Lai and S. Dhanalakshmi, "Radiological analysis of COVID-19 using computational intelligence: A broad gauge study", J. Healthcare Eng. , vol. 2022, pp. 1-25, Feb. 2022.
W. C. S. Low, J. H. Chuah, C. A. T. H. Tee, S. Anis, M. A. Shoaib, A. Faisal, et al., "An overview of deep learning techniques on chest X-ray and CT scan identification of COVID-19", Comput. Math. Methods Med. , vol. 2021, pp. 1-17, Jun. 2021.
K. S. Krishnan and K. S. Krishnan, "Vision transformer based COVID-19 detection using chest X-rays", Proc. 6th Int. Conf. Signal Process. Comput. Control (ISPCC) , pp. 644-648, Oct. 2021.
D. Shome, T. Kar, S. Mohanty, P. Tiwari, K. Muhammad, A. AlTameem, et al., "COVID-transformer: Interpretable COVID-19 detection using vision transformer for healthcare", Int. J. Environ. Res. Public Health , vol. 18, no. 21, Oct. 2021.
A. K. Mondal, A. Bhattacharjee, P. Singla and A. P. Prathosh, "XViTCOS: Explainable vision transformer based COVID-19 screening using radiography", IEEE J. Transl. Eng. Health Med. , vol. 10, pp. 1-10, 2022.
S. Park, G. Kim, Y. Oh, J. B. Seo, S. M. Lee, J. H. Kim, et al., "Multi-task vision transformer using low-level chest X-ray feature corpus for COVID-19 diagnosis and severity quantification", Med. Image Anal. , vol. 75, Jan. 2022.
Y. E. Almalki, A. Qayyum, M. Irfan, N. Haider, A. Glowacz, F. M. Alshehri, et al., "A novel method for COVID-19 diagnosis using artificial intelligence in chest X-ray images", Healthcare , vol. 9, no. 5, pp. 522, Apr. 2021.
M. E. H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M. A. Kadir, Z. B. Mahbub, et al., "Can AI help in screening viral and COVID-19 pneumonia?", IEEE Access , vol. 8, pp. 132665-132676, 2020.
T. Rahman, A. Khandakar, Y. Qiblawey, A. Tahir, S. Kiranyaz, S. B. A. Kashem, et al., "Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images", Comput. Biol. Med. , vol. 132, May 2021.
A. M. Freeman and J. R. T. Leigh, "Viral pneumonia", Encycl. Respir. Med. Four , pp. 456-466, Jul. 2021.
Y. LeCun, "Backpropagation applied to handwritten zip code recognition", Neural Comput. , vol. 1, no. 4, pp. 541-551, 1989.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., "Attention is all you need", arXiv:1706.03762 , 2017.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, et al., "An image is worth 16×16 words: Transformers for image recognition at scale", arXiv:2010.11929 , 2020.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, et al., "Going deeper with convolutions", Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 1-9, Jun. 2015.
F. Chollet, "Xception: Deep learning with depthwise separable convolutions", arXiv:1610.02357 , 2016.
K. He, X. Zhang, S. Ren and J. Sun, "Deep residual learning for image recognition", arXiv:1512.03385 , 2015.
J. Devlin, M.-W. Chang, K. Lee and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding", arXiv:1810.04805 , 2018.
D. P. Mitchell and A. N. Netravali, "Reconstruction filters in computer-graphics", Proc. 15th Annu. Conf. Comput. Graph. Interact. Techn. (SIGGRAPH) , pp. 221-228, 1988.
A. Hassani, S. Walton, N. Shah, A. Abuduweili, J. Li and H. Shi, "Escaping the big data paradigm with compact transformers", arXiv:2104.05704 , 2021.

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On December 31st, 2019, Chinese health authorities reported an outbreak of pneumonia cases of unknown aetiology in the city of Wuhan (Hubei Province, China). Shortly thereafter, on January 9th, 2020, the China CDC (the Center for Disease Control and Prevention of China) identified a new coronavirus (tentatively named 2019-nCoV) as the etiological cause of these diseases. Chinese health authorities have also confirmed the inter-human transmission of the virus. On 11th February, the World Health Organization (WHO) announced that the disease transmitted since 2019-nCoV has been called COVID-19 (Corona Virus Disease). The Coronavirus Study Group (CSG) of the International Committee on Taxonomy of Viruses has officially classified with the name of SARS-CoV-2 the virus provisionally named by the international health authorities 2019-nCoV and responsible of cases of COVID-19 (Corona Virus Disease). The CSG - responsible for defining the official classification of viruses and the taxonomy of the Corona viridae family, after evaluating the novelty of the human pathogen and on the basis of phylogeny, taxonomy and established practice, has formally associated this virus with the coronavirus it causes severe acute respiratory syndrome (SARS-CoVs, Severe acute respiratory syndrome coronaviruses) classifying it as Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) [1] . After assessing the severity levels and global spread of the SARS-CoV-2 infection, WHO declared that the COVID-19 epidemic can be considered a pandemic.
After the World Health Organization (WHO) declared the rapid spread of the aggressive COVID-19 virus, the world of scientific research went to great lengths to propose a solution for the early diagnosis of the virus [2] . Indeed, the rapid detection of COVID-19 can help control the spread of the disease.
Nowadays, the most used and most reliable method of diagnosing infection is the Reverse Transcription-Polymerase Chain Reaction (RTPCR). A sample is taken by nose / mouth and pharyngeal swab and analysed by real-time molecular methods through the amplification of the viral genes most expressed during the infection. This analysis can only be carried o
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Галерея 3221531

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