Evidence map›Paper›PMID 24493942›Full record

ArticleMycobiology2013

Efficient Recovery of Lignocellulolytic Enzymes of Spent Mushroom Compost from Oyster Mushrooms, Pleurotus spp., and Potential Use in Dye Decolorization.

Seon-Hwa Lim, Yun-Hae Lee, Hee-Wan Kang

Abstract read
In one paragraph

Article in Mycobiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Hydrolytic Enzymes in the Secretome of the MushroomsMolecules (Basel, Switzerland) · 2025
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
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  13. Article
  14. Article
  15. Metabolic Profiling ofFrontiers in microbiology · 2018
    Article
  16. Article
  17. 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

3 authors.

Seon-Hwa LimGraduate School of Future Convergence Technology, Hankyong National University, Ansung 456-749, Korea.
Yun-Hae LeeMushroom Research Institute, GARES, Gwangju 464-870, Korea.
Hee-Wan KangGraduate School of Future Convergence Technology, Hankyong National University, Ansung 456-749, Korea. ; JK BioTech Co. Ltd., Ansung 456-749, Korea. ; Institute of Genetic Engineering, Hankyong National University, Ansung 456-749, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study was conducted in order to perform efficient extraction of lignocellulolytic enzymes amylase (EC 3.2.1.1), cellulase (EC 3.2.1.4), laccase (EC 1.10.3.2), and xylanase (EC 3.2.1.8) from spent mushroom compost (SMC) of Pleurotus ostreatus, P. eryngii, and P. cornucopiae. Optimal enzyme recovery was achieved when SMCs were extracted with 50 mM sodium citrate (pH 4.5) buffer at 4℃ for 2 hr. Amylase, cellulase, and xylanase activities showed high values in extracts from P. ostreatus SMC, with 2.97 U/g, 1.67 U/g, and 91.56 U/g, respectively, whereas laccase activity and filter paper degradation ability were highest in extracts from P. eryngii SMC, with values of 9.01 U/g and 0.21 U/g, respectively. Enzymatic activities varied according to the SMCs released from different mushroom farms. The synthetic dyes remazol brilliant blue R and Congo red were decolorized completely by the SMC extract of P. eryngii within 120 min, and the decolorization ability of the extract was comparable to that of 0.3 U of commercial laccase. In addition, laccase activity of the SMC extract from P. eryngii was compared to that of commercial enzymes or its industrial application in decolorization.

Indexed as

ActivityDecolorizationLignocellulolytic enzymesPleurotus spp.Spent mushroom compost

Identifiers

PMID24493942
PMCPMC3905125

What OpenQuestion holds

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Registered trials

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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.