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Liu K, Zhang X, Hu Y, Chen W, Kong X, Yao P, Cong J, Zuo H, Wang J, Li X, Wei B. What, Where, When and How of COVID-19 Patents Landscape: A Bibliometrics Review. Front Med (Lausanne) 2022; 9:925369. [PMID: 35847804 PMCID: PMC9283760 DOI: 10.3389/fmed.2022.925369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 06/14/2022] [Indexed: 12/12/2022] Open
Abstract
Two years after COVID-19 came into being, many technologies have been developed to bring highly promising bedside methods to help fight this epidemic disease. However, owing to viral mutation, how far the promise can be realized remains unclear. Patents might act as an additional source of information for informing research and policy and anticipating important future technology developments. A comprehensive study of 3741 COVID-19-related patents (3,543 patent families) worldwide was conducted using the Derwent Innovation database. Descriptive statistics and social network analysis were used in the patent landscape. The number of COVID-19 applications, especially those related to treatment and prevention, continued to rise, accompanied by increases in governmental and academic patent assignees. Although China dominated COVID-19 technologies, this position is worth discussing, especially in terms of the outstanding role of India and the US in the assignee collaboration network as well as the outstanding invention portfolio in Italy. Intellectual property barriers and racist treatment were reduced, as reflected by individual partnerships, transparent commercial licensing and diversified portfolios. Critical technological issues are personalized immunity, traditional Chinese medicine, epidemic prediction, artificial intelligence tools, and nucleic acid detection. Notable challenges include balancing commercial competition and humanitarian interests. The results provide a significant reference for decision-making by researchers, clinicians, policymakers, and investors with an interest in COVID-19 control.
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Affiliation(s)
- Kunmeng Liu
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, China
| | - Xiaoming Zhang
- Department of Cardiovascular Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yuanjia Hu
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, Macao SAR, China
| | - Weijie Chen
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, Macao SAR, China
| | - Xiangjun Kong
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, Macao SAR, China
| | - Peifen Yao
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, Macao SAR, China
| | - Jinyu Cong
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, China
| | - Huali Zuo
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Hong Kong SAR, China
| | - Jian Wang
- Science College, Shandong Jiaotong University, Jinan, China
| | - Xiang Li
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, China
| | - Benzheng Wei
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, China
- *Correspondence: Benzheng Wei,
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Douthwaite JA, Brown CA, Ferdinand JR, Sharma R, Elliott J, Taylor MA, Malintan NT, Duvoisin H, Hill T, Delpuech O, Orton AL, Pitt H, Kuenzi F, Fish S, Nicholls DJ, Cuthbert A, Richards I, Ratcliffe G, Upadhyay A, Marklew A, Hewitt C, Ross-Thriepland D, Brankin C, Chodorge M, Browne G, Mander PK, DeWildt RM, Weaver S, Smee PA, van Kempen J, Bartlett JG, Allen PM, Koppe EL, Ashby CA, Phipps JD, Mehta N, Brierley DJ, Tew DG, Leveridge MV, Baddeley SM, Goodfellow IG, Green C, Abell C, Neely A, Waddell I, Rees S, Maxwell PH, Pangalos MN, Howes R, Clark R. Improving the efficiency and effectiveness of an industrial SARS-CoV-2 diagnostic facility. Sci Rep 2022; 12:3114. [PMID: 35210470 PMCID: PMC8873195 DOI: 10.1038/s41598-022-06873-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 02/07/2022] [Indexed: 11/24/2022] Open
Abstract
On 11th March 2020, the UK government announced plans for the scaling of COVID-19 testing, and on 27th March 2020 it was announced that a new alliance of private sector and academic collaborative laboratories were being created to generate the testing capacity required. The Cambridge COVID-19 Testing Centre (CCTC) was established during April 2020 through collaboration between AstraZeneca, GlaxoSmithKline, and the University of Cambridge, with Charles River Laboratories joining the collaboration at the end of July 2020. The CCTC lab operation focussed on the optimised use of automation, introduction of novel technologies and process modelling to enable a testing capacity of 22,000 tests per day. Here we describe the optimisation of the laboratory process through the continued exploitation of internal performance metrics, while introducing new technologies including the Heat Inactivation of clinical samples upon receipt into the laboratory and a Direct to PCR protocol that removed the requirement for the RNA extraction step. We anticipate that these methods will have value in driving continued efficiency and effectiveness within all large scale viral diagnostic testing laboratories.
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Affiliation(s)
| | | | - John R Ferdinand
- Charles River Laboratories, Chesterford Research Park, Saffron Walden, UK.,Department of Medicine, University of Cambridge, Cambridge, UK
| | - Rahul Sharma
- Charles River Laboratories, Chesterford Research Park, Saffron Walden, UK.,Department of Medicine, University of Cambridge, Cambridge, UK
| | - Jane Elliott
- BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | | | | | | | - Thomas Hill
- Charles River Laboratories, Chesterford Research Park, Saffron Walden, UK
| | | | | | - Haidee Pitt
- BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Fred Kuenzi
- Charles River Laboratories, Chesterford Research Park, Saffron Walden, UK
| | - Simon Fish
- Charles River Laboratories, Chesterford Research Park, Saffron Walden, UK.,GSK R&D Tech, Stevenage, UK
| | | | | | - Ian Richards
- Charles River Laboratories, Chesterford Research Park, Saffron Walden, UK
| | - Giles Ratcliffe
- Charles River Laboratories, Chesterford Research Park, Saffron Walden, UK
| | | | - Abigail Marklew
- Charles River Laboratories, Chesterford Research Park, Saffron Walden, UK
| | - Craig Hewitt
- BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ian G Goodfellow
- Division of Virology, Department of Pathology, University of Cambridge, Cambridge, UK
| | - Clive Green
- BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Chris Abell
- Vice Chancellor's Office, University of Cambridge, Cambridge, UK
| | - Andy Neely
- Vice Chancellor's Office, University of Cambridge, Cambridge, UK
| | - Ian Waddell
- Charles River Laboratories, Chesterford Research Park, Saffron Walden, UK
| | - Steve Rees
- BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | | | | | - Rob Howes
- BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Roger Clark
- Charles River Laboratories, Chesterford Research Park, Saffron Walden, UK
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Borges LP, Nascimento LC, Heimfarth L, Souza DRV, Martins AF, de Rezende Neto JM, Dos Santos KA, Matos ILS, da Invenção GB, Oliveira BM, Santos AA, Souza NAA, de Jesus PC, Dos Santos CA, Goes MAO, de Souza MSF, Guimarães AG. Estimated SARS-CoV-2 Infection and Seroprevalence in Firefighters from a Northeastern Brazilian State: A Cross-Sectional Study. Int J Environ Res Public Health 2021; 18:8148. [PMID: 34360442 DOI: 10.3390/ijerph18158148] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/15/2021] [Accepted: 07/28/2021] [Indexed: 11/16/2022]
Abstract
The new coronavirus has been affecting health worldwide and essential service workers are continually exposed to this infectious agent, increasing the chance of infection and the development of the disease. Thus, this study aimed to estimate the frequency of infection and seroprevalence for SARS-CoV-2 in military firefighters in a city in Northeastern Brazil in January 2021. An observational cross-sectional study was carried out with 123 firefighters who answered a brief questionnaire to collect socio-epidemiological data and underwent RT-PCR and immunofluorescence test (IgM and IgG). The results found reveal a positive seroprevalence, with a high rate of infection in this class of workers, since they are essential service professionals who are exposed to risk due to their working hours, in addition to sharing some spaces and work materials. Besides, there were significant associations between positivity for IgG and IgM, as well as for positive RT-PCR prior to the study and the presence of IgG, with odd ratios of 3.04 and 4.9, respectively. These findings reinforce the need for immunization in this category, whose line of service hinders the adoption of distancing measures, since in many situations physical contact is inevitable.
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Feng R, Hu Q, Jiang Y. Unknown Disease Outbreaks Detection: A Pilot Study on Feature-Based Knowledge Representation and Reasoning Model. Front Public Health 2021; 9:683855. [PMID: 34055732 PMCID: PMC8155365 DOI: 10.3389/fpubh.2021.683855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 04/14/2021] [Indexed: 01/08/2023] Open
Abstract
Background: The outbreak of COVID-19 in 2019 has rapidly swept the world, causing irreparable loss to human beings. The pandemic has shown that there is still a delay in the early response to disease outbreaks and needs a method for unknown disease outbreak detection. The study's objective is to establish a new medical knowledge representation and reasoning model, and use the model to explore the feasibility of unknown disease outbreak detection. Methods: The study defined abnormal values with diagnostic significances from clinical data as the Features, and defined the Features as the antecedents of inference rules to match with knowledge bases, achieved in detecting known or emerging infectious disease outbreaks. Meanwhile, the study built a syndromic surveillance base to capture the target cases' Features to improve the reliability and fault-tolerant ability of the system. Results: The study combined the method with Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and early COVID-19 outbreaks as empirical studies. The results showed that with suitable surveillance guidelines, the method proposed in this study was capable to detect outbreaks of SARS, MERS, and early COVID-19 pandemics. The quick matching accuracies of confirmed infection cases were 89.1, 26.3-98%, and 82%, and the syndromic surveillance base would capture the Features of the remaining cases to ensure the overall detection accuracies. Based on the early COVID-19 data in Wuhan, this study estimated that the median time of the early COVID-19 cases from illness onset to local authorities' responses could be reduced to 7.0-10.0 days. Conclusions: This study offers a new solution to transfer traditional medical knowledge into structured data and form diagnosis rules, enables the representation of doctors' logistic thinking and the knowledge transmission among different users. The results of empirical studies demonstrate that by constantly inputting medical knowledge into the system, the proposed method will be capable to detect unknown diseases from existing ones and perform an early response to the initial outbreaks.
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Affiliation(s)
- Rui Feng
- School of Computer Science, Wuhan University, Wuhan, China
| | - Qiping Hu
- School of Computer Science, Wuhan University, Wuhan, China
| | - Yingan Jiang
- Department of Infectious Diseases, Renmin Hospital of Wuhan University, Wuhan, China
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Liu K, Gu Z, Islam MS, Scherngell T, Kong X, Zhao J, Chen X, Hu Y. Global landscape of patents related to human coronaviruses. Int J Biol Sci 2021; 17:1588-1599. [PMID: 33907523 PMCID: PMC8071764 DOI: 10.7150/ijbs.58807] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/26/2021] [Indexed: 12/24/2022] Open
Abstract
At present, the COVID-19 pandemic is running rampant, having caused 2.18 million deaths. Characterizing the global patent landscape of coronaviruses is essential not only for informing research and policy, given the current pandemic crisis, but also for anticipating important future developments. While patents are a promising indicator of technological knowledge production widely used in innovation research, they are often an underused resource in biological sciences. In this study, we present a patent landscape for the seven coronaviruses known to infect humans. The information included in this paper provides a strong intellectual groundwork for the ongoing development of therapeutic agents and vaccines along with a deeper discussion of intellectual property rights under epidemic conditions. The results show that there has been a rapid increase in human coronavirus patents, especially COVID-19 patents. China and the United States play an outstanding role in global cooperation and patent application. The leading role of academic institutions and government is increasingly apparent. Notable technological issues related to human coronaviruses include pharmacochemical treatment, diagnosis of viral infection, viral-vector vaccines, and traditional Chinese medicine. Furthermore, a critical challenge lies in balancing commercial competition, enterprise profit, knowledge sharing, and public interest.
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Affiliation(s)
- Kunmeng Liu
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau, China
| | - Zixuan Gu
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau, China
| | - Md Sahidul Islam
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau, China
| | - Thomas Scherngell
- Innovation Systems & Policy, AIT Austrian Institute of Technology, Vienna, Austria
| | - Xiangjun Kong
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau, China
| | - Jing Zhao
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau, China
| | - Xin Chen
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau, China
| | - Yuanjia Hu
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau, China
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