Evidence map›Paper›PMID 34483472›Full record

ReviewMRS bulletin2021

Bioelectrochemical platforms to study and detect emerging pathogens.

Mary C Machado, Marjon Zamani, Susan Daniel, Ariel L Furst

Open access · bronzeAbstract readReview
In one paragraph

Review in MRS bulletin, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 39% 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

2 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Review
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

4 authors at 2 institutions in 1 country.

Mary C MachadoSmith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, USA.
Marjon ZamaniDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, USA.
Susan DanielSmith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, USA.
Ariel L FurstDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, USA.ORCID 0000-0001-9583-9703
Cornell University · USMassachusetts Institute of Technology · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ongoing SARS-CoV-2 pandemic has emphasized the importance of technologies to rapidly detect emerging pathogens and understand their interactions with hosts. Platforms based on the combination of biological recognition and electrochemical signal transduction, generally termed bioelectrochemical platforms, offer unique opportunities to both sense and study pathogens. Improved bio-based materials have enabled enhanced control over the biotic-abiotic interface in these systems. These improvements have generated platforms with the capability to elucidate biological function rather than simply detect targets. This advantage is a key feature of recent bioelectrochemical platforms applied to infectious disease. Here, we describe developments in materials for bioelectrochemical platforms to study and detect emerging pathogens. The incorporation of host membrane material into electrochemical devices has provided unparalleled insights into the interaction between viruses and host cells, and new capture methods have enabled the specific detection of bacterial pathogens, such as those that cause secondary infections with SARS-CoV-2. As these devices continue to improve through the merging of hi-tech materials and biomaterials, the scalability and commercial viability of these devices will similarly improve. GRAPHIC ABSTRACT:

Indexed as

BioelectronicsBiomaterialsPathogens

Identifiers

PMID34483472
PMCPMC8407123
OpenAlexW3197435953

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.