Evidence map›Paper›PMID 27350595›Full record

ArticleScientific reports2016

Formation and characteristics of biomimetic mineralo-organic particles in natural surface water.

Cheng-Yeu Wu, Jan Martel, Tsui-Yin Wong, David Young, Chien-Chun Liu, Cheng-Wei Lin, John D Young

Abstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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

6 citing papers in PubMed.

  1. Article
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  3. Review
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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

7 authors.

Cheng-Yeu WuLaboratory of Nanomaterials, Chang Gung University, Taoyuan, Taiwan, Republic of China.
Jan MartelLaboratory of Nanomaterials, Chang Gung University, Taoyuan, Taiwan, Republic of China.
Tsui-Yin WongLaboratory of Nanomaterials, Chang Gung University, Taoyuan, Taiwan, Republic of China.
David YoungLaboratory of Nanomaterials, Chang Gung University, Taoyuan, Taiwan, Republic of China.
Chien-Chun LiuGraduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan, Republic of China.
Cheng-Wei LinLaboratory of Nanomaterials, Chang Gung University, Taoyuan, Taiwan, Republic of China.
John D YoungLaboratory of Nanomaterials, Chang Gung University, Taoyuan, Taiwan, Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent studies have shown that nanoparticles exist in environmental water but the formation, characteristics and fate of such particles remain incompletely understood. We show here that surface water obtained from various sources (ocean, hot springs, and soil) produces mineralo-organic particles that gradually increase in size and number during incubation. Seawater produces mineralo-organic particles following several cycles of filtration and incubation, indicating that this water possesses high particle-seeding potential. Electron microscopy observations reveal round, bacteria-like mineral particles with diameters of 20 to 800 nm, which may coalesce and aggregate to form mineralized biofilm-like structures. Chemical analysis of the particles shows the presence of a wide range of chemical elements that form mixed mineral phases dominated by calcium and iron sulfates, silicon and aluminum oxides, sodium carbonate, and iron sulfide. Proteomic analysis indicates that the particles bind to proteins of bacterial, plant and animal origins. When observed under dark-field microscopy, mineral particles derived from soil-water show biomimetic morphologies, including large, round structures similar to cells undergoing division. These findings have important implications not only for the recognition of biosignatures and fossils of small microorganisms in the environment but also for the geochemical cycling of elements, ions and organic matter in surface water.

Identifiers

PMID27350595
PMCPMC4923871

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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.