Evidence map›Paper›PMID 27158249›Full record

ReviewIndustrial biotechnology (New Rochelle, N.Y.)2016

Endophytic Phytoaugmentation: Treating Wastewater and Runoff Through Augmented Phytoremediation.

Lauren K Redfern, Claudia K Gunsch

Abstract readReview
In one paragraph

Review in Industrial biotechnology (New Rochelle, N.Y.), 2016. 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. Phenol Removal Capacity of the Common Duckweed (Plants (Basel, Switzerland) · 2020
    Article
  4. 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

2 authors.

Lauren K RedfernDepartment of Civil and Environmental Engineering, Duke University , Durham, NC.
Claudia K GunschDepartment of Civil and Environmental Engineering, Duke University , Durham, NC.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Limited options exist for efficiently and effectively treating water runoff from agricultural fields and landfills. Traditional treatments include excavation, transport to landfills, incineration, stabilization, and vitrification. In general, treatment options relying on biological methods such as bioremediation have the ability to be applied in situ and offer a sustainable remedial option with a lower environmental impact and reduced long-term operating expenses. These methods are generally considered ecologically friendly, particularly when compared to traditional physicochemical cleanup options. Phytoremediation, which relies on plants to take up and/or transform the contaminant of interest, is another alternative treatment method which has been developed. However, phytoremediation is not widely used, largely due to its low treatment efficiency. Endophytic phytoaugmentation is a variation on phytoremediation that relies on augmenting the phytoremediating plants with exogenous strains to stimulate associated plant-microbe interactions to facilitate and improve remediation efficiency. In this review, we offer a summary of the current knowledge as well as developments in endophytic phytoaugmentation and present some potential future applications for this technology. There has been a limited number of published endophytic phytoaugmentation case studies and much remains to be done to transition lab-scale results to field applications. Future research needs include large-scale endophytic phytoaugmentation experiments as well as the development of more exhaustive tools for monitoring plant-microbe-pollutant interactions.

Identifiers

PMID27158249
PMCPMC4835827

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.