Cited by 37Bacterial Identification and Susceptibility TestingBacterial Infections and VaccinesInfectious Encephalopathies and Encephalitis

A metagenomics-based diagnostic approach for central nervous system infections in hospital acute care setting

Open access
Hasan, Mohammad Rubayet, Sundararaju, Sathyavathi, Tang, Patrick, Tsui, Kin-Ming, Lopez, Andres Perez, Janahi, Mohammad, Tan, Rusung, Tilley, Peter·2020·Scientific Reports

Published version of a preprint

Abstract

Abstract The etiology of central nervous system (CNS) infections such as meningitis and encephalitis remains unknown in a large proportion of cases partly because the diversity of pathogens that may cause CNS infections greatly outnumber available test methods. Here we present a metagenomic next generation sequencing (mNGS) based approach for broad-range detection of pathogens associated with CNS infections, which is suitable for application in the acute care hospital setting. Using an Illumina MiSeq benchtop sequencer and the IDseq pipeline for identifying pathogens in metagenomic sequence data, we show that the analytical sensitivity of mNGS to detect pathogens is comparable to that of PCR in simulated cerebrospinal fluid (CSF) specimens. We then applied this method for pathogen detection in 74 CSF specimens from patients with suspected CNS infections that were previously tested by culture and/or PCR. Diagnostic accuracy, sensitivity and specificity of mNGS approach with reference to conventional methods were all 95%. Furthermore, confirmatory testing on specimens that gave discrepant results were mostly in favor of the mNGS assay. The clinical application of mNGS holds promise to benefit patients with CNS infections of unknown etiology.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41598-020-68159-z

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • mNGS has been shown to detect more neuroinvasive viruses accurately.

    “A new emerging technique that might be very broad yet highly accurate at detection is the mNGS, which has been shown to detect more neuroinvasive viruses accurately (32, 35).”

    From Viruses Causing Aseptic Meningitis: A Tertiary Medical Center Experience With a Multiplex PCR Assay (2020) · cites this paper as [32]

    The Results (Clinical validation of mNGS for pathogen detection in CSF; page unavailable) report that mNGS detected pathogens in CSF, but the additional pathogens identified beyond conventional methods were not viruses. The same section says a low-level HSV2 finding could not be verified. The Discussion notes that this DNA-specific method did not detect RNA viruses, so the paper does not establish that it accurately detects more neuroinvasive viruses. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 29 · Cited by 37

Research works printed but not linked: 3

  1. [3]Tunkel AR Beek D Scheld WM Bennett JE Dolin R Blaser MJ Mandell, Douglas, and Bennett's Principles and Practice of Infectious Diseases 2015 Amsterdam Elsevier Inc. 1097 1137
  2. [4]Bennett JE Bennett JE Dolin R Blaser MJ Mandell, Douglas, and Bennet’s Principles and Practice of Infectious Diseases 2015 Philadelphia Elsevier 1138 1143
  3. [5]Beckham JT Tyler KL Bennett JE Dolin R Blaser MJ Mandell, Douglas, and Bennet’s Principles and Practice of Infectious Diseases 2015 Philadelphia Elsevier 1144 1163

Authors
Hasan, M. R., Sundararaju, S., Tang, P., Tsui, K.-M., Lopez, A. P., Janahi, M., Tan, R., & Tilley, P.
Affiliations
  • Sidra Medical and Research Center (QA)
  • Weill Cornell Medical College in Qatar (QA)
  • University of British Columbia (CA)
Corresponding author
Hasan, Mohammad Rubayet
Venue
Scientific Reports
Published
2020-07-08
Volume
10
Issue
1
Pages
11194-11194
ISSN
2045-2322 (ISSN-L)
OpenAlex
W3041603820
Cited by
37
References
29
Citations by year
2026: 1 · 2025: 6 · 2024: 8 · 2023: 6 · 2022: 8 · 2021: 5 · 2020: 3
FWCI
3.64
Type
journal article
Language
en
Open access
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • BC Children's Hospital Foundation (British Columbia Children's Hospital Foundation (KRZ28061)
  • Sidra Internal Research Fund
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Bacterial Identification and Susceptibility Testing
  • Life Sciences › Immunology and Microbiology › Microbiology › Bacterial Infections and Vaccines
  • Health Sciences › Medicine › Infectious Diseases › Infectious Encephalopathies and Encephalitis
Keywords
central nervous system infection, meningitis, encephalitis, metagenomic next-generation sequencing, cerebrospinal fluid, pathogen detection, diagnostic accuracy
MeSH terms
Adolescent, Central Nervous System Infections, Cerebrospinal Fluid, Child, Child, Preschool, DNA, Bacterial, DNA, Viral, Feasibility Studies, Female, Hospitals, Humans, Infant, Infant, Newborn, Male, Retrospective Studies, Sensitivity and Specificity, Sequence Analysis, DNA, Computational Biology, Metagenome, Young Adult, Metagenomics, High-Throughput Nucleotide Sequencing, Datasets as Topic

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review