Evidence map›Paper›PMID 38764891›Full record

ArticleAdvanced sensor research2024

Advancements in Airborne Viral Nucleic Acid Detection with Wearable Devices.

Ryan Godin, Sepehr Hejazi, Nigel F Reuel

Abstract read
In one paragraph

Article in Advanced sensor research, 2024. 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
–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

3 citing papers in PubMed.

  1. Article
  2. Article
  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

3 authors.

Ryan GodinDepartment of Chemical and Biological Engineering, Iowa State University.
Sepehr HejaziDepartment of Chemical and Biological Engineering, Iowa State University.
Nigel F ReuelDepartment of Chemical and Biological Engineering, Iowa State University.

Funding

Unsupervised optimization of protein therapeutics using closed-loop in vitro synthesis, nanosensing,and deep-learningR35GM138265 · NIGMS · IOWA STATE UNIVERSITY · PI Nigel F Reuel · 2020 to 2026
$2.3M
NIGMS NIH HHS R35 GM138265
6 · The paper itself

Abstract

Wearable health sensors for an expanding range of physiological parameters have experienced rapid development in recent years and are poised to disrupt the way healthcare is tracked and administered. The monitoring of environmental contaminants with wearable technologies is an additional layer of personal and public healthcare and is also receiving increased focus. Wearable sensors that detect exposure to airborne viruses could alert wearers of viral exposure and prompt proactive testing and minimization of viral spread, benefitting their own health and decreasing community risk. With the high levels of asymptomatic spread of COVID-19 observed during the pandemic, such devices could dramatically enhance our pandemic response capabilities in the future. To facilitate advancements in this area, this review summarizes recent research on airborne viral detection using wearable sensing devices as well as technologies suitable for wearables. Since the low concentration of viral particles in the air poses significant challenges to detection, methods for airborne viral particle collection and viral sensing are discussed in detail. A special focus is placed on nucleic acid-based viral sensing mechanisms due to their enhanced ability to discriminate between viral subtypes. Important considerations for integrating airborne viral collection and sensing on a single wearable device are also discussed.

Identifiers

PMID38764891
PMCPMC11101210

What OpenQuestion holds

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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.