Evidence map›Paper›PMID 25808841›Full record

ArticleBiosensors2015

Label-free sensing of adenosine based on force variations induced by molecular recognition.

Jingfeng Li, Qing Li, Lucio Colombi Ciacchi, Gang Wei

Open access · goldAbstract read
In one paragraph

Article in Biosensors, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.5field-weighted citation impact, top 17% 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

1 citing paper in PubMed, 13 citations in OpenAlex.

  1. 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

4 authors at 1 institution in 1 country.

Jingfeng LiHybrid Materials Interfaces Group, Faculty of Production Engineering, University of Bremen, Am Fallturm 1, 28359 Bremen, Germany. jinfeng@uni-bremen.de.
Qing LiHybrid Materials Interfaces Group, Faculty of Production Engineering, University of Bremen, Am Fallturm 1, 28359 Bremen, Germany. qing_li@uni-bremen.de.
Lucio Colombi CiacchiHybrid Materials Interfaces Group, Faculty of Production Engineering, University of Bremen, Am Fallturm 1, 28359 Bremen, Germany. colombi@hmi.uni-bremen.de.
Gang WeiHybrid Materials Interfaces Group, Faculty of Production Engineering, University of Bremen, Am Fallturm 1, 28359 Bremen, Germany. wei@uni-bremen.de.
University of Bremen · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We demonstrate a simple force-based label-free strategy for the highly sensitive sensing of adenosine. An adenosine ssDNA aptamer was bound onto an atomic force microscopy (AFM) probe by covalent modification, and the molecular-interface adsorption force between the aptamer and a flat graphite surface was measured by single-molecule force spectroscopy (SMFS). In the presence of adenosine, the molecular recognition between adenosine and the aptamer resulted in the formation of a folded, hairpin-like DNA structure and hence caused a variation of the adsorption force at the graphite/water interface. The sensitive force response to molecular recognition provided an adenosine detection limit in the range of 0.1 to 1 nM. The addition of guanosine, cytidine, and uridine had no significant interference with the sensing of adenosine, indicating a strong selectivity of this sensor architecture. In addition, operational parameters that may affect the sensor, such as loading rate and solution ionic strength, were investigated.

Indexed as

AdenosineAptamers, PeptideBase SequenceDNA, Single-StrandedMicroscopy, Atomic ForceMolecular Probe TechniquesMolecular StructureSpectrum AnalysisAdenosineAptamers, PeptideDNA, Single-Stranded

Identifiers

PMID25808841
PMCPMC4384084
OpenAlexW1972581524

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.