ArticleHeliyon2024
Functional metagenomics highlights varied infection states with dynamics of pathogens and antibiotic resistance in lower respiratory tract infections.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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.
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Who cites it
6 citing papers in PubMed.
- Metagenomic next-generation sequencing for pathology-suspected fungal infections at rare anatomical sites: a case series.Future science OA · 2026Article
- Lower Respiratory Microbiome Dysbiosis Is Associated With Poor Prognosis in Acute Severe Lower Respiratory Tract Infection.MedComm · 2026Article
- Next-generation sequencing in the etiological diagnosis of severe childhood pneumonia.Frontiers in pediatrics · 2026Review
- Article
- Use of the BIOFIREBMC infectious diseases · 2025Article
- Achromobacter in the Conjunctival Sac Microbiota: Potential Association With Acanthamoeba Keratitis Related to Orthokeratology Lenses.Investigative ophthalmology & visual science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Antimicrobial resistance (AMR) amongst pathogenic bacterial species poses significant challenges in treating infections of the lower respiratory tract (LRT), leading to higher hospitalization and mortality rates. Methods: Bronchoalveolar lavage fluid (BALF) from 84 clinically adjudicated LRTI patients were subjected to respiratory pathogen ID/AMR (RPIP) enrichment and sequencing followed by Explify and CZID seq data analysis to identify potential LRTI pathogens and associated AMR genes. Patients were categorized as LRTI-WP (with pneumonia) and LRTI-WoP (without pneumonia). Findings: mNGS achieved 100 % pathogen detection compared to 73 % through clinician-used BioFire panel. Predominant pathogens included Interpretation: The prevalence of common pathogens like
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Registered trials
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