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An arithmetic sequence is a structured arrangement of numbers where each term is derived by adding a constant value, known as the common difference, to the previous term. This consistent pattern allows for the efficient computation of any term within the sequence as well as the cumulative sum of multiple terms. The formula for finding the nth term of an arithmetic sequence is:Here, aₙ represents the nth term of the sequence, a is the first term, d is the common difference, and n is the...
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Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples
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Chronic Meningitis Investigated via Metagenomic Next-Generation Sequencing.

Michael R Wilson1,2, Brian D O'Donovan3, Jeffrey M Gelfand1,2

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Metagenomic next-generation sequencing (mNGS) identified diverse pathogens in challenging meningitis cases. A statistical scoring method effectively filtered contaminants, aiding accurate diagnosis of infectious meningitis.

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Area of Science:

  • Neuroscience
  • Infectious Diseases
  • Genomics

Background:

  • Diagnosing infectious causes of subacute or chronic meningitis is difficult.
  • Enhanced, unbiased diagnostic approaches are needed for accurate identification.

Purpose of the Study:

  • To present a case series of patients with diagnostically challenging subacute or chronic meningitis.
  • To utilize metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) supported by a statistical framework for improved diagnostics.

Main Methods:

  • Developed a weighted scoring metric using z scores from mNGS data of CSF from 7 meningitis patients and 94 noninfectious controls.
  • Applied the scoring metric to prioritize and rank mNGS results, filtering out environmental contaminants.

Main Results:

  • mNGS identified a parasitic worm (Taenia solium), HIV-1, and four fungi (Cryptococcus neoformans, Aspergillus oryzae, Histoplasma capsulatum, Candida dubliniensis) in 7 patients.
  • The scoring algorithm reduced reported microbial taxa by a mean of 87%, accurately identifying causative pathogens within the top 2 ranked microbes.

Conclusions:

  • mNGS successfully identified diverse pathogens in challenging meningitis cases.
  • A statistical scoring algorithm significantly improved data interpretation, distinguishing true pathogens from environmental contaminants.