ArticleNPJ digital medicine2025
Metagenomic fingerprints in bronchoalveolar lavage differentiate pulmonary diseases.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- A diagnostic model based on pulmonary microbiota and host gene expression to distinguish colonization from pneumonia.Scientific reports · 2026Article
- Metatranscriptomics uncovers host immune and microbiome signatures specific to and shared between human metapneumovirus and respiratory syncytial virus infections in children.Virologica Sinica · 2026Article
- LungMicroHostR: an R package for integrated host-microbiome analysis of bronchoalveolar lavage fluid metagenomic sequencing data.Frontiers in bioinformatics · 2026Article
- Review
- Metagenomic and metatranscriptomic profiling of bronchoalveolar lavage fluid identifies microbial and host biomarkers of drug-resistant tuberculosis.Frontiers in cellular and infection microbiology · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
Abstract
Recent advances in unbiased metagenomic next-generation sequencing (mNGS) enable simultaneous examination of microbial and host genetic material. We developed a multimodal machine learning-based diagnostic approach to differentiate lung cancer and pulmonary infections by analyzing 402 bronchoalveolar lavage fluid (BALF) mNGS datasets, including lung cancer (n = 123), bacterial infections (n = 114), fungal infections (n = 79), and pulmonary tuberculosis (n = 86). The training cohort revealed differences in microbial profiles, bacteriophage abundance, host gene and transposable element expression, immune cell composition, and tumor fraction derived from copy number variation (CNV). The integrated model (Model VI) achieved an AUC of 0.937 (95% CI, 0.910-0.964) in the training cohort and 0.847 (95% CI, 0.776-0.918) in the test cohort. A rule-in/rule-out strategy further improved accuracy in differentiating lung cancer from tuberculosis (accuracy = 0.896), fungal (accuracy = 0.915), and bacterial (accuracy = 0.907) infections. These findings highlight the potential of mNGS-based multimodal analysis as a cost-effective tool for early and accurate differential diagnosis.
Identifiers
What OpenQuestion holds
Registered trials
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.