Evidence map›Paper›PMID 37214177›Full record

ArticleMycoKeys2023

MycoPins: a metabarcoding-based method to monitor fungal colonization of fine woody debris.

Maria Shumskaya, Nicholas Lorusso, Urvi Patel, Madison Leigh, Panu Somervuo, Dmitry Schigel

Abstract read
In one paragraph

Article in MycoKeys, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Assessment of Fungal Succession in Decomposing Swine Carcasses (Journal of fungi (Basel, Switzerland) · 2023
    Article
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

6 authors.

Maria ShumskayaDepartment of Biology, Kean University, Union, USA Kean University Union United States of America.ORCID https://orcid.org/0000-0001-7916-462X
Nicholas LorussoDepartment of Biology, Kean University, Union, USA Kean University Union United States of America.ORCID https://orcid.org/0000-0002-3299-9205
Urvi PatelDepartment of Biology, Kean University, Union, USA Kean University Union United States of America.
Madison LeighDepartment of Biology, Kean University, Union, USA Kean University Union United States of America.
Panu SomervuoBiological and Environmental Sciences, University of Helsinki, Helsinki, Finland University of Helsinki Helsinki Finland.
Dmitry SchigelBiological and Environmental Sciences, University of Helsinki, Helsinki, Finland University of Helsinki Helsinki Finland.ORCID https://orcid.org/0000-0002-2919-1168

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The MycoPins method described here is a rapid and affordable protocol to monitor early colonization events in communities of wood-inhabiting fungi in fine woody debris. It includes easy to implement field sampling techniques and sample processing, followed by data processing, and analysis of the development of early dead wood fungal communities. The method is based on fieldwork from a time series experiment on standard sterilized colonization targets followed by the metabarcoding analysis and automated molecular identification of species. This new monitoring method through its simplicity, moderate costs, and scalability paves a way for a broader and scalable project pipeline. MycoPins establishes a standard routine for research stations or regularly visited field sites for monitoring of fungal colonization of woody substrates. The routine uses widely available consumables and therefore presents a unifying method for monitoring of fungi of this type.

Indexed as

Dead woodmetabarcodingmycobiomesaproxylic fungi

Identifiers

PMID37214177
PMCPMC10196935

What OpenQuestion holds

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LicenceCC BY
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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.