Evidence map›Paper›PMID 24684276›Full record

ArticleEnvironmental microbiology2015

Unmasking the measles-like parchment discoloration: molecular and microanalytical approach.

Guadalupe Piñar, Katja Sterflinger, Flavia Pinzari

Open access · hybridAbstract read
In one paragraph

Article in Environmental microbiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
59.0field-weighted citation impact, top 1% 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

25 citing papers in PubMed, 75 citations in OpenAlex.

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  19. New endophyticIMA fungus · 2017
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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

3 authors at 2 institutions in 2 countries.

Guadalupe PiñarInstitute of Applied Microbiology, Department of Biotechnology, University of Natural Resources and Life Sciences, Muthgasse 11, Vienna, 1190, Austria.
Katja Sterflinger
Flavia Pinzari
BOKU University · ATMinistero della cultura · IT

Funding

Austrian Science Fund FWF V 194
6 · The paper itself

Abstract

Many ancient parchments are defaced by red or purple maculae associated with localized destruction of collagen fibres. Although the main characteristics of this damage were present in most of the manuscripts analysed by many authors, no common microbial or fungal denominator has been found so far, and little or no correspondence between the microbial or fungal species isolated from materials could be addressed. In this study, culture-independent molecular methods and scanning electron microscopy (SEM) were used to identify fungal and bacterial communities on parchments affected by the purple stains. Protocols for c extraction and nucleic-acid-based strategies were selected for assays examining the community structure of fungi and bacteria on biodeteriorated parchment. Both SEM and molecular analysis detected the presence of bacterial and fungal cells in the damaged areas. Halophilic, halotolerant proteolytic bacterial species were selected by the saline environment provided by the parchment samples. As common microbial denominators, members of the Actinobacteria, mainly Saccharopolyspora spp. and species of Aspergillus, were detected in all investigated cases. It is proposed that a relationship exists between the phenomenon of purple spots on ancient parchments and that of the 'red heat' phenomenon, known to be present in some products manufactured with marine salt.

Indexed as

Skin PigmentationAspergillusCollagenDNA, BacterialDNA, FungalMicroscopy, Electron, ScanningSaccharopolysporaSkinSodium ChlorideCollagenDNA, BacterialDNA, FungalSodium Chloride

Identifiers

PMID24684276
PMCPMC4371641
OpenAlexW1836508885

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.