Evidence map›Paper›PMID 39554862›Full record

ArticleACS materials Au2024

Realizing the Potential of Commercial E-Textiles for Wearable Glucose Biosensing Application.

Moshfiq-Us-Saleheen Chowdhury, Sutirtha Roy, Krishna Prasad Aryal, Henry Leung, Richa Pandey

Abstract read
In one paragraph

Article in ACS materials Au, 2024. 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
–field-weighted citation impact
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.

  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

5 authors.

Moshfiq-Us-Saleheen ChowdhuryDepartment of Electrical and Software Engineering, University of Calgary, Calgary, Alberta T2N 1N4, Canada.ORCID https://orcid.org/0000-0001-9562-5916
Sutirtha RoyDepartment of Electrical and Software Engineering, University of Calgary, Calgary, Alberta T2N 1N4, Canada.ORCID https://orcid.org/0009-0008-8952-886X
Krishna Prasad AryalDepartment of Biomedical Engineering, University of Calgary, Calgary T2N 1N4, Alberta, Canada.
Henry LeungDepartment of Electrical and Software Engineering, University of Calgary, Calgary, Alberta T2N 1N4, Canada.
Richa PandeyDepartment of Biomedical Engineering, University of Calgary, Calgary T2N 1N4, Alberta, Canada.ORCID https://orcid.org/0000-0001-5070-0353

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advancements in wearable technology have enabled noninvasive health monitoring using biosensors. This research focuses on developing a textile-based sweat glucose sensor using commercially available conductive textiles, evading the complexity of traditional fabrication methods. A comparative analysis of three low-cost conductive textiles, Adafruit 1364, 1167, and 4762, has been conducted for electrochemical glucose detection with glucose-specific enzymes such as glucose oxidase (GOx) and glucose dehydrogenase (GDH). Adafruit 1364 outperformed others in morphological, electrochemical, and wearable properties. Cyclic voltammetry shows that Adafruit 1364 and 4762 effectively detect glucose at the potential of 0.23 and 0.08 V using glucose oxidase and 0.1 and 0.08 V using glucose dehydrogenase enzymes, respectively. Furthermore, chronoamperometry has been conducted to confirm the presence of glucose at 1 μM concentration. Differential pulse voltammetry was conducted to assess the sensitivity of the Adafruit 1364 fabric electrode using glucose solutions with concentrations of 0.05, 0.15, 0.25, and 0.5 mM. The electrode immobilized with GOx showed a sensitivity of 0.005 μA μM

Identifiers

PMID39554862
PMCPMC11565280

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.