Table 1_tNGS-based detection of respiratory pathogens in a single center: associations with age, gender, season, and co-infections.docx
Background<p>Respiratory tract infections represent a significant global health challenge. Conventional diagnostic methods frequently fail to detect complex infections or novel pathogens. This study employed Targeted Next-Generation Sequencing to achieve an unbiased and comprehensive identific...
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2025
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| _version_ | 1849927636767211520 |
|---|---|
| author | Qingling Wang (485692) |
| author2 | Dan Wu (5969) Yanzi Zhang (6306962) Qian Zeng (2866592) Juan Wang (115708) Xin Lv (770855) |
| author2_role | author author author author author |
| author_facet | Qingling Wang (485692) Dan Wu (5969) Yanzi Zhang (6306962) Qian Zeng (2866592) Juan Wang (115708) Xin Lv (770855) |
| author_role | author |
| dc.creator.none.fl_str_mv | Qingling Wang (485692) Dan Wu (5969) Yanzi Zhang (6306962) Qian Zeng (2866592) Juan Wang (115708) Xin Lv (770855) |
| dc.date.none.fl_str_mv | 2025-11-25T05:10:51Z |
| dc.identifier.none.fl_str_mv | 10.3389/fcimb.2025.1663234.s001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Table_1_tNGS-based_detection_of_respiratory_pathogens_in_a_single_center_associations_with_age_gender_season_and_co-infections_docx/30703352 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Clinical Microbiology targeted next-generation sequencing respiratory pathogens age distribution seasonal variation co-infection |
| dc.title.none.fl_str_mv | Table 1_tNGS-based detection of respiratory pathogens in a single center: associations with age, gender, season, and co-infections.docx |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Background<p>Respiratory tract infections represent a significant global health challenge. Conventional diagnostic methods frequently fail to detect complex infections or novel pathogens. This study employed Targeted Next-Generation Sequencing to achieve an unbiased and comprehensive identification of respiratory pathogens, as well as to conduct analysis of pathogen distribution across age, gender and seasons.</p>Methods<p>We conducted a retrospective analysis of clinical samples, including throat swabs, sputum, and bronchoalveolar lavage fluid, obtained from symptomatic patients. The analysis utilized targeted next-generation sequencing in conjunction with bioinformatics. Statistical assessments were performed to evaluate associations with age, gender, season, and co-infections, primarily employing Chi-square tests.</p>Results<p>A high pathogen detection rate of 97.08% was achieved among 20059 individuals. Bacteria were the most frequently detected pathogens, accounting for 49.62%, followed by viruses at 43.31%, and special pathogens at 7.07%. Significant age-related differences in pathogen profiles were observed. Although no overall gender effect was detected, variations specific to certain pathogens were noted. Clear seasonal trends emerged for key pathogens. Co-infections were highly prevalent, with bacterial-viral combinations being the most common, affecting 49.03% of patients, which exceeded the rate of bacterial infections alone at 15.69%.</p>Conclusion<p>Targeted next-generation sequencing serves as a robust tool for elucidating the intricate spectrum and epidemiology of respiratory pathogens. This study underscores significant associations with patient age, seasonal variations, and the prevalence of co-infections, providing essential insights for targeted clinical and public health interventions in response to respiratory tract infections.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_0b8563784ea3f5db59d37602c006b139 |
| identifier_str_mv | 10.3389/fcimb.2025.1663234.s001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30703352 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Table 1_tNGS-based detection of respiratory pathogens in a single center: associations with age, gender, season, and co-infections.docxQingling Wang (485692)Dan Wu (5969)Yanzi Zhang (6306962)Qian Zeng (2866592)Juan Wang (115708)Xin Lv (770855)Clinical Microbiologytargeted next-generation sequencingrespiratory pathogensage distributionseasonal variationco-infectionBackground<p>Respiratory tract infections represent a significant global health challenge. Conventional diagnostic methods frequently fail to detect complex infections or novel pathogens. This study employed Targeted Next-Generation Sequencing to achieve an unbiased and comprehensive identification of respiratory pathogens, as well as to conduct analysis of pathogen distribution across age, gender and seasons.</p>Methods<p>We conducted a retrospective analysis of clinical samples, including throat swabs, sputum, and bronchoalveolar lavage fluid, obtained from symptomatic patients. The analysis utilized targeted next-generation sequencing in conjunction with bioinformatics. Statistical assessments were performed to evaluate associations with age, gender, season, and co-infections, primarily employing Chi-square tests.</p>Results<p>A high pathogen detection rate of 97.08% was achieved among 20059 individuals. Bacteria were the most frequently detected pathogens, accounting for 49.62%, followed by viruses at 43.31%, and special pathogens at 7.07%. Significant age-related differences in pathogen profiles were observed. Although no overall gender effect was detected, variations specific to certain pathogens were noted. Clear seasonal trends emerged for key pathogens. Co-infections were highly prevalent, with bacterial-viral combinations being the most common, affecting 49.03% of patients, which exceeded the rate of bacterial infections alone at 15.69%.</p>Conclusion<p>Targeted next-generation sequencing serves as a robust tool for elucidating the intricate spectrum and epidemiology of respiratory pathogens. This study underscores significant associations with patient age, seasonal variations, and the prevalence of co-infections, providing essential insights for targeted clinical and public health interventions in response to respiratory tract infections.</p>2025-11-25T05:10:51ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/fcimb.2025.1663234.s001https://figshare.com/articles/dataset/Table_1_tNGS-based_detection_of_respiratory_pathogens_in_a_single_center_associations_with_age_gender_season_and_co-infections_docx/30703352CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/307033522025-11-25T05:10:51Z |
| spellingShingle | Table 1_tNGS-based detection of respiratory pathogens in a single center: associations with age, gender, season, and co-infections.docx Qingling Wang (485692) Clinical Microbiology targeted next-generation sequencing respiratory pathogens age distribution seasonal variation co-infection |
| status_str | publishedVersion |
| title | Table 1_tNGS-based detection of respiratory pathogens in a single center: associations with age, gender, season, and co-infections.docx |
| title_full | Table 1_tNGS-based detection of respiratory pathogens in a single center: associations with age, gender, season, and co-infections.docx |
| title_fullStr | Table 1_tNGS-based detection of respiratory pathogens in a single center: associations with age, gender, season, and co-infections.docx |
| title_full_unstemmed | Table 1_tNGS-based detection of respiratory pathogens in a single center: associations with age, gender, season, and co-infections.docx |
| title_short | Table 1_tNGS-based detection of respiratory pathogens in a single center: associations with age, gender, season, and co-infections.docx |
| title_sort | Table 1_tNGS-based detection of respiratory pathogens in a single center: associations with age, gender, season, and co-infections.docx |
| topic | Clinical Microbiology targeted next-generation sequencing respiratory pathogens age distribution seasonal variation co-infection |