Evidence map›Paper›PMID 37770648›Full record

ArticleNature nanotechnology2024

A wearable aptamer nanobiosensor for non-invasive female hormone monitoring.

Cui Ye, Minqiang Wang, Jihong Min, Roland Yingjie Tay, Heather Lukas, Juliane R Sempionatto, Jiahong Li, Changhao Xu, Wei Gao

Open access · greenAbstract read
In one paragraph

Article in Nature nanotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 papers.

0numbers the graph read from it
0cells of the map it votes in
129citing papers in PubMed
41.5field-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

129 citing papers in PubMed, 378 citations in OpenAlex.

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69 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

9 authors at 1 institution in 1 country.

Cui Ye *Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA.
Minqiang Wang *Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA.ORCID 0000-0002-7775-8341
Jihong MinAndrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA.
Roland Yingjie TayAndrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA.ORCID 0000-0002-3341-0984
Heather LukasAndrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA.ORCID 0000-0002-8160-9066
Juliane R SempionattoAndrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA.
Jiahong LiAndrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA.
Changhao XuAndrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA.ORCID 0000-0002-6817-3341
Wei GaoAndrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA. weigao@caltech.edu.ORCID 0000-0002-8503-4562
California Institute of Technology · US

Funding

Laser-Engraved Wearable Sweat Sensors to Detect and Monitor Cardiometabolic DiseaseR01HL155815 · NHLBI · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI GAO, WEI · 2021 to 2025
$2.0M
Cutaneous uric acid and metabolite monitoring to improve individual response to pharmaceutical and dietary treatment in patients with goutR21DK132666 · NIDDK · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI FITZGERALD, JOHN D, GAO, WEI · 2022 to 2023
$461k
NHLBI NIH HHS R01 HL155815NIDDK NIH HHS R21 DK132666Office of Naval Research N00014-21-1-2483Office of Naval Research N00014-21-1-2845
6 · The paper itself

Abstract

Personalized monitoring of female hormones (for example, oestradiol) is of great interest in fertility and women's health. However, existing approaches usually require invasive blood draws and/or bulky analytical laboratory equipment, making them hard to implement at home. Here we report a skin-interfaced wearable aptamer nanobiosensor based on target-induced strand displacement for automatic and non-invasive monitoring of oestradiol via in situ sweat analysis. The reagentless, amplification-free and 'signal-on' detection approach coupled with a gold nanoparticle-MXene-based detection electrode offers extraordinary sensitivity with an ultra-low limit of detection of 0.14 pM. This fully integrated system is capable of autonomous sweat induction at rest via iontophoresis, precise microfluidic sweat sampling controlled via capillary bursting valves, real-time oestradiol analysis and calibration with simultaneously collected multivariate information (that is, temperature, pH and ionic strength), as well as signal processing and wireless communication with a user interface (for example, smartphone). We validated the technology in human participants. Our data indicate a cyclical fluctuation in sweat oestradiol during menstrual cycles, and a high correlation between sweat and blood oestradiol was identified. Our study opens up the potential for wearable sensors for non-invasive, personalized reproductive hormone monitoring.

Indexed as

Biosensing TechniquesMetal NanoparticlesWearable Electronic DevicesEstradiolFemaleGoldHumansSkinEstradiolGold

Identifiers

PMID37770648
PMCPMC10954395
OpenAlexW4387141445

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.