ReviewColloids and surfaces. B, Biointerfaces2021
Conventional and microfluidic methods for airborne virus isolation and detection.
Review in Colloids and surfaces. B, Biointerfaces, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed, 20 citations in OpenAlex.
- Microfluidics for the biological analysis of atmospheric ice-nucleating particles: Perspectives and challenges.Biomicrofluidics · 2025Review
- Research advances in microfluidic collection and detection of virus, bacterial, and fungal bioaerosols.Mikrochimica acta · 2024Review
- Concentrating viable airborne pathogens using a virtual impactor with a compact water-based condensation air sampler.Aerosol science and technology : the journal of the American Association for Aerosol Research · 2024Article
- Internet-of-medical-things integrated point-of-care biosensing devices for infectious diseases: Toward better preparedness for futuristic pandemics.Bioengineering & translational medicine · 2023Review
- On-site bioaerosol sampling and detection in microfluidic platforms.Trends in analytical chemistry : TRAC · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the COVID-19 pandemic, the threat of infectious diseases to public health and safety has become much more apparent. Viral, bacterial and fungal diseases have led to the loss of millions of lives, especially in the developing world. Diseases caused by airborne viruses like SARS-CoV-2 are difficult to control, as these viruses are easily transmissible and can circulate in the air for hours. To contain outbreaks of viruses such as SARS-CoV-2 and institute targeted precautions, it is important to detect them in air and understand how they infect their targets. Point-of-care (PoC) diagnostics and point-of-need (PoN) detection methods are necessary to rapidly test patient and environmental samples, so precautions can immediately be applied. Traditional benchtop detection methods such as ELISA, PCR and culture are not suitable for PoC and PoN monitoring, because they can take hours to days and require specialized equipment. Microfluidic devices can be made at low cost to perform such assays rapidly and at the PoN. They can also be integrated with air- and liquid-based sampling technologies to capture and analyze viruses from air and body fluids. Here, conventional and microfluidic virus detection methods are reviewed and compared. The use of air sampling devices to capture and concentrate viruses is discussed first, followed by a review of analysis methods such as immunoassays, RT-PCR and isothermal amplification in conventional and microfluidic platforms. This review provides an overview of the capabilities of microfluidics in virus handling and detection, which will be useful to infectious disease researchers, biomedical engineers, and public health agencies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.