Evidence map›Paper›PMID 32266329›Full record

ArticleMatter2020

Investigation of cortisol dynamics in human sweat using a graphene-based wireless mHealth system.

Rebeca M Torrente-Rodríguez, Jiaobing Tu, Yiran Yang, Jihong Min, Minqiang Wang, Yu Song, You Yu, Changhao Xu, Cui Ye, Waguih William IsHak and 1 more

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Matter, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 149 papers.

0numbers the graph read from it
0cells of the map it votes in
149citing papers in PubMed
22.1field-weighted citation impact, top 1% 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

149 citing papers in PubMed, 429 citations in OpenAlex.

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89 more citing papers are in PubMed but not listed here.

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

11 authors at 2 institutions in 1 country.

Rebeca M Torrente-RodríguezDepartment of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA.
Jiaobing TuDepartment of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA.
Yiran YangDepartment of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA.
Jihong MinDepartment of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA.
Minqiang WangDepartment of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA.
Yu SongDepartment of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA.
You YuDepartment of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA.
Changhao XuDepartment of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA.
Cui YeDepartment of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA.
Waguih William IsHakDepartment of Psychiatry and Behavioral Neurosciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
Wei GaoDepartment of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA.
California Institute of Technology · USCedars-Sinai Medical Center · US

Funding

Wearable Sweat Sensor for Non-invasive Wireless Monitoring of Heart FailureR21NR018271 · NINR · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI GAO, WEI · 2019 to 2020
$435k
NINR NIH HHS R21 NR018271
6 · The paper itself

Abstract

Understanding and assessing endocrine response to stress is crucial to human performance analysis, stress-related disorder diagnosis, and mental health monitoring. Current approaches for stress monitoring are largely based on questionnaires, which could be very subjective. To avoid stress-inducing blood sampling and to realize continuous, non-invasive, and real-time stress analysis at the molecular levels, we investigate the dynamics of a stress hormone, cortisol, in human sweat using an integrated wireless sensing device. Highly sensitive, selective, and efficient cortisol sensing is enabled by a flexible sensor array that exploits the exceptional performance of laser-induced graphene for electrochemical sensing. Herein, we report the first cortisol diurnal cycle and the dynamic stress response profile constructed from human sweat. Our pilot study demonstrates a strong empirical correlation between serum and sweat cortisol, revealing exciting opportunities offered by sweat analysis toward non-invasive dynamic stress monitoring via wearable and portable sensing platforms.

Indexed as

cortisolflexible sensorsgraphenemHealthstress hormonestress responsesweat

Identifiers

PMID32266329
PMCPMC7138219
OpenAlexW3008874451

What OpenQuestion holds

Textmetadata
LicenceTDM
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.