Evidence map›Paper›PMID 39702080›Full record

ArticleBMC infectious diseases2024

Molecular pathogen profiling of COVID-19 coinfections.

Yanping Dai, Shuan Tao, Huanhuan Ying, Qianqian Fang, Jingping Kong, Fei Guo, Yong Yang, Peng Cao, Ying Zhou, Weijiang Jin and 1 more

Abstract read
In one paragraph

Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Phenotypical and Molecular Characterization ofLife (Basel, Switzerland) · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Yanping Dai *Department of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Shuan Tao *Department of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Huanhuan YingDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Qianqian FangDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Jingping KongDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Fei GuoDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Yong YangDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Peng CaoDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Ying ZhouDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Weijiang JinDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Wei LiangDepartment of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China. hslwys@163.com.

Funding

Novel Coronavirus infection disease science and technology research project XGZ2303
6 · The paper itself

Abstract

objectiveThis 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.

methodsRespiratory 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.

resultsThere 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.

conclusionCOVID-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.

Indexed as

CoinfectionCOVID-19SARS-CoV-2AdultAgedBacteriaFemaleHumansMaleMiddle AgedRespiratory Tract InfectionsCOVID-19EpidemiologyKlebsiella pneumoniae (KP)mecARespiratory pathogens

Identifiers

PMID39702080
PMCPMC11660608

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.