Evidence map›Paper›PMID 31500222›Full record

ArticleSensors (Basel, Switzerland)2019

A Smart Tongue Depressor-Based Biosensor for Glucose.

Xiaojin Luo, Weihua Shi, Yiqun Liu, Pengju Sha, Yanan Chu, Yue Cui

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.3field-weighted citation impact, top 19% 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

3 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

6 authors at 1 institution in 1 country.

Xiaojin LuoCollege of Engineering, Peking University, Beijing 100871, China.
Weihua ShiCollege of Engineering, Peking University, Beijing 100871, China.
Yiqun LiuCollege of Engineering, Peking University, Beijing 100871, China.
Pengju ShaCollege of Engineering, Peking University, Beijing 100871, China.
Yanan ChuCollege of Engineering, Peking University, Beijing 100871, China.
Yue CuiCollege of Engineering, Peking University, Beijing 100871, China. ycui@pku.edu.cn.
Peking University · CN

Funding

National Thousand-Young-Talent Program of China NA
6 · The paper itself

Abstract

The development of new bioelectronic platforms for direct interactions with oral fluid could open up significant opportunities for healthcare monitoring. A tongue depressor is a widely used medical tool that is inserted into the mouth, where it comes into close contact with saliva. Glucose is a typical salivary biomarker. Herein, we report-for the first time-a tongue depressor-based biosensor for the detection of glucose in both phosphate buffer and real human saliva. Carbon nanotubes (CNTs) are attractive electronic materials, with excellent electrochemical properties. The sensor is constructed by printing CNTs and silver/silver chloride (Ag/AgCl) to form three electrodes in an electrochemical cell: Working, reference, and counter electrodes. The enzyme glucose oxidase (GOD) is immobilized on the working electrode. The glucose detection performance of the sensor is excellent, with a detection range of 7.3 μM to 6 mM. The glucose detection time is about 3 min. The discretion between healthy people's and simulated diabetic patients' salivary samples is clear and easy to tell. We anticipate that the biosensor could open up new opportunities for the monitoring of salivary biomarkers and advance healthcare applications.

Indexed as

Biosensing TechniquesElectrodesEnzymes, ImmobilizedGlucoseGlucose OxidaseHumansHydrogen PeroxideNanotubes, CarbonEnzymes, ImmobilizedGlucoseGlucose OxidaseHydrogen PeroxideNanotubes, Carbonbiosensorcarbon nanotubeenzymehydrogen peroxidesalivary glucosetongue depressor

Identifiers

PMID31500222
PMCPMC6767240
OpenAlexW2972193517

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.