Evidence map›Paper›PMID 29440575›Full record

ArticlemBio2018

A High-Throughput Approach for Identification of Nontuberculous Mycobacteria in Drinking Water Reveals Relationship between Water Age and

Sarah-Jane Haig, Nadine Kotlarz, John J LiPuma, Lutgarde Raskin

Open access · goldAbstract read
In one paragraph

Article in mBio, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
6.0field-weighted citation impact, top 3% of its field
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

33 citing papers in PubMed, 70 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Microbial Biofilms Dynamics and Functionality in an UrbanEnvironmental science & technology · 2026
    Article
  5. The effects of silver nitrate onFrontiers in public health · 2025
    Article
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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

4 authors at 1 institution in 1 country.

Sarah-Jane HaigDepartment of Civil & Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA.
Nadine KotlarzDepartment of Civil & Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA.
John J LiPumaDepartment of Pediatrics and Communicable Diseases, University of Michigan Medical School, Ann Arbor, Michigan, USA.
Lutgarde RaskinDepartment of Civil & Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA raskin@umich.edu.
University of Michigan–Ann Arbor · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nontuberculous mycobacteria (NTM) frequently detected in drinking water (DW) include species associated with human infections, as well as species rarely linked to disease. Methods for improved the recovery of NTM DNA and high-throughput identification of NTM are needed for risk assessment of NTM infection through DW exposure. In this study, different methods of recovering bacterial DNA from DW were compared, revealing that a phenol-chloroform DNA extraction method yielded two to four times as much total DNA and eight times as much NTM DNA as two commercial DNA extraction kits. This method, combined with high-throughput, single-molecule real-time sequencing of NTM

Indexed as

High-Throughput Screening AssaysBacterial LoadDNA, BacterialDNA-Directed RNA PolymerasesDrinking WaterMicrobiological TechniquesMolecular Diagnostic TechniquesMycobacterium Infections, NontuberculousNontuberculous MycobacteriaSequence Analysis, DNATime FactorsDNA, BacterialDNA-Directed RNA PolymerasesDrinking WaterRNA polymerase beta subunitDNA extractiondrinking waterMycobacterium aviumNTMPacBiopremise plumbing

Identifiers

PMID29440575
PMCPMC5821076
OpenAlexW2790132662

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.