Evidence map›Paper›PMID 34878810›Full record

ArticleApplied and environmental microbiology2022

Current and Emerging Tools of Computational Biology To Improve the Detoxification of Mycotoxins.

Natalie Sandlin, Darius Russell Kish, John Kim, Marco Zaccaria, Babak Momeni

Open access · greenAbstract read
In one paragraph

Article in Applied and environmental microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.9field-weighted citation impact, top 9% 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

8 citing papers in PubMed, 18 citations in OpenAlex.

  1. Review
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  7. Review
  8. Microbial detoxification of mycotoxins in food.Frontiers in microbiology · 2022
    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

5 authors at 1 institution in 1 country.

Natalie Sandlin *Department of Biology, Boston Collegegrid.208226.c, Chestnut Hill, Massachusetts, USA.
Darius Russell Kish *Department of Biology, Boston Collegegrid.208226.c, Chestnut Hill, Massachusetts, USA.
John KimDepartment of Biology, Boston Collegegrid.208226.c, Chestnut Hill, Massachusetts, USA.
Marco ZaccariaDepartment of Biology, Boston Collegegrid.208226.c, Chestnut Hill, Massachusetts, USA.
Babak MomeniDepartment of Biology, Boston Collegegrid.208226.c, Chestnut Hill, Massachusetts, USA.ORCID 0000-0003-1271-5196
Boston College · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biological organisms carry a rich potential for removing toxins from our environment, but identifying suitable candidates and improving them remain challenging. We explore the use of computational tools to discover strains and enzymes that detoxify harmful compounds. In particular, we focus on mycotoxins-fungus-produced toxins that contaminate food and feed-and biological enzymes that are capable of rendering them less harmful. We discuss the use of established and novel computational tools to complement existing empirical data in three directions: discovering the prospect of detoxification among underexplored organisms, finding important cellular processes that contribute to detoxification, and improving the performance of detoxifying enzymes. We hope to create a synergistic conversation between researchers in computational biology and those in the bioremediation field. We showcase open bioremediation questions where computational researchers can contribute and highlight relevant existing and emerging computational tools that could benefit bioremediation researchers.

Indexed as

MycotoxinsComputational BiologyFood ContaminationMycotoxinsbiodegradationbioinformaticsbioremediationcomputational biologyenzymesexoenzymesmycotoxinstoxins

Identifiers

PMID34878810
PMCPMC8824263
OpenAlexW4200308871

What OpenQuestion holds

Textmetadata
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.