Evidence map›Paper›PMID 33187247›Full record

ArticleMicroorganisms2020

Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of

Patrick Murigu Kamau Njage, Pimlapas Leekitcharoenphon, Lisbeth Truelstrup Hansen, Rene S Hendriksen, Christel Faes, Marc Aerts, Tine Hald

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Microorganisms, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.2field-weighted citation impact, top 13% 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

7 citing papers in PubMed, 29 citations in OpenAlex.

  1. Review
  2. Review
  3. A Critical Review of Risk Assessment Models forFoods (Basel, Switzerland) · 2023
    Review
  4. Article
  5. Article
  6. Article
  7. Review
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

7 authors at 2 institutions in 2 countries.

Patrick Murigu Kamau NjageResearch Group for Genomic Epidemiology, Division for Global Surveillance, National Food Institute, Technical University of Denmark, 2800 Lyngby, Denmark.ORCID 0000-0002-4364-5314
Pimlapas LeekitcharoenphonResearch Group for Genomic Epidemiology, Division for Global Surveillance, National Food Institute, Technical University of Denmark, 2800 Lyngby, Denmark.
Lisbeth Truelstrup HansenResearch Group for Microbiology and Hygiene, National Food Institute, Technical University of Denmark, 2800 Lyngby, Denmark.
Rene S HendriksenResearch Group for Genomic Epidemiology, Division for Global Surveillance, National Food Institute, Technical University of Denmark, 2800 Lyngby, Denmark.
Christel FaesInteruniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University Katholieke Universiteit Leuven, 3590 Diepenbeek, Belgium.ORCID 0000-0002-1878-9869
Marc AertsInteruniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University Katholieke Universiteit Leuven, 3590 Diepenbeek, Belgium.
Tine HaldResearch Group for Genomic Epidemiology, Division for Global Surveillance, National Food Institute, Technical University of Denmark, 2800 Lyngby, Denmark.
Technical University of Denmark · DKHasselt University · BE

Funding

Horizon 2020 643476
6 · The paper itself

Abstract

The application of high-throughput DNA sequencing technologies (WGS) data remain an increasingly discussed but vastly unexplored resource in the public health domain of quantitative microbial risk assessment (QMRA). This is due to challenges including high dimensionality of WGS data and heterogeneity of microbial growth phenotype data. This study provides an innovative approach for modeling the impact of population heterogeneity in microbial phenotypic stress response and integrates this into predictive models inputting a high-dimensional WGS data for increased precision exposure assessment using an example of

Indexed as

exposure assessmentfinite mixture modelsListeria monocytogenesmachine learningpredictive modelingquantitative microbial risk assessmentwhole genome sequencing

Identifiers

PMID33187247
PMCPMC7698238
OpenAlexW3102422922

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.