Molecular pathogen profiling of COVID-19 coinfections
Abstract Objective This study aims to investigate the prevalence, pathogen spectrum, clinical characteristics, and prognosis-related factors of other respiratory pathogens in COVID-19-infected patients, and to explore the application of molecular detection methods in the epidemiological investigatio...
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| Format: | Article |
| Language: | English |
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BMC
2024-12-01
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| Series: | BMC Infectious Diseases |
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| Online Access: | https://doi.org/10.1186/s12879-024-10347-7 |
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| author | Yanping Dai Shuan Tao Huanhuan Ying Qianqian Fang Jingping Kong Fei Guo Yong Yang Peng Cao Ying Zhou Weijiang Jin Wei Liang |
| author_facet | Yanping Dai Shuan Tao Huanhuan Ying Qianqian Fang Jingping Kong Fei Guo Yong Yang Peng Cao Ying Zhou Weijiang Jin Wei Liang |
| author_sort | Yanping Dai |
| collection | DOAJ |
| description | Abstract Objective This study aims to investigate the prevalence, pathogen spectrum, clinical characteristics, and prognosis-related factors of other respiratory pathogens in COVID-19-infected patients, and to explore the application of molecular detection methods in the epidemiological investigation of multiple pathogen infections. Methods Respiratory samples and clinical data from 384 patients with outpatient and inpatient respiratory infections were collected and analyzed. Multiplex PCR and capillary electrophoresis were conducted to detect the distribution characteristics of 26 pathogen species, comprising 13 viruses, 13 bacteria. Statistical analysis explored the relationship between pathogen distribution with COVID-19 development. Results There was no statistical difference in prognosis between the 230 COVID-19-positive patients and the 154 COVID-19-positive patients. COVID-19 cases co-infected with other pathogens do not correlate with patient’s age and gender. The main distribution of pathogens was mainly mecA (n = 62, 26.96%), followed by SPN(n = 61; 26.52%) and KP(n = 22; 9.57%). Compared with non-COVID-19 cases, COVID-19 infected patients showed a significantly higher mecA carrying rate (26.96% vs. 15.58%, P < 0.01), while there was no statistical difference for other pathogens. Regression analysis found that mecA and KP were independent risk factors for severe illnesses (mechanical ventilation, endotracheal intubation, ECOMO, etc.) or death in COVID-19 patients had 2.391 times and 3.722 times risk of severe disease or death compared with COVID-19 patients without mecA and KP. Conclusion COVID-19 patients show a higher mecA carrier rate than non-COVID-19 patients, and mecA and KP may increase the risk of severe disease or death in COVID-19 patients, which requires close attention. |
| format | Article |
| id | doaj-art-b8b30cf940cc44b59672f1c9a867cdd3 |
| institution | Kabale University |
| issn | 1471-2334 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj-art-b8b30cf940cc44b59672f1c9a867cdd32024-12-22T12:17:35ZengBMCBMC Infectious Diseases1471-23342024-12-012411810.1186/s12879-024-10347-7Molecular pathogen profiling of COVID-19 coinfectionsYanping Dai0Shuan Tao1Huanhuan Ying2Qianqian Fang3Jingping Kong4Fei Guo5Yong Yang6Peng Cao7Ying Zhou8Weijiang Jin9Wei Liang10Department of Laboratory Medicine, The First Affiliated Hospital of Ningbo UniversityDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo UniversityDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo UniversityDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo UniversityDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo UniversityDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo UniversityDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo UniversityDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo UniversityDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo UniversityDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo UniversityDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo UniversityAbstract Objective This study aims to investigate the prevalence, pathogen spectrum, clinical characteristics, and prognosis-related factors of other respiratory pathogens in COVID-19-infected patients, and to explore the application of molecular detection methods in the epidemiological investigation of multiple pathogen infections. Methods Respiratory samples and clinical data from 384 patients with outpatient and inpatient respiratory infections were collected and analyzed. Multiplex PCR and capillary electrophoresis were conducted to detect the distribution characteristics of 26 pathogen species, comprising 13 viruses, 13 bacteria. Statistical analysis explored the relationship between pathogen distribution with COVID-19 development. Results There was no statistical difference in prognosis between the 230 COVID-19-positive patients and the 154 COVID-19-positive patients. COVID-19 cases co-infected with other pathogens do not correlate with patient’s age and gender. The main distribution of pathogens was mainly mecA (n = 62, 26.96%), followed by SPN(n = 61; 26.52%) and KP(n = 22; 9.57%). Compared with non-COVID-19 cases, COVID-19 infected patients showed a significantly higher mecA carrying rate (26.96% vs. 15.58%, P < 0.01), while there was no statistical difference for other pathogens. Regression analysis found that mecA and KP were independent risk factors for severe illnesses (mechanical ventilation, endotracheal intubation, ECOMO, etc.) or death in COVID-19 patients had 2.391 times and 3.722 times risk of severe disease or death compared with COVID-19 patients without mecA and KP. Conclusion COVID-19 patients show a higher mecA carrier rate than non-COVID-19 patients, and mecA and KP may increase the risk of severe disease or death in COVID-19 patients, which requires close attention.https://doi.org/10.1186/s12879-024-10347-7COVID-19Respiratory pathogensmecAKlebsiella pneumoniae (KP)Epidemiology |
| spellingShingle | Yanping Dai Shuan Tao Huanhuan Ying Qianqian Fang Jingping Kong Fei Guo Yong Yang Peng Cao Ying Zhou Weijiang Jin Wei Liang Molecular pathogen profiling of COVID-19 coinfections BMC Infectious Diseases COVID-19 Respiratory pathogens mecA Klebsiella pneumoniae (KP) Epidemiology |
| title | Molecular pathogen profiling of COVID-19 coinfections |
| title_full | Molecular pathogen profiling of COVID-19 coinfections |
| title_fullStr | Molecular pathogen profiling of COVID-19 coinfections |
| title_full_unstemmed | Molecular pathogen profiling of COVID-19 coinfections |
| title_short | Molecular pathogen profiling of COVID-19 coinfections |
| title_sort | molecular pathogen profiling of covid 19 coinfections |
| topic | COVID-19 Respiratory pathogens mecA Klebsiella pneumoniae (KP) Epidemiology |
| url | https://doi.org/10.1186/s12879-024-10347-7 |
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