Evidence map›Paper›PMID 42642733›Full record

SynthesisBMC infectious diseases2026

The filtration performance of fabrics against particles of viral size: a systematic review.

Nam Xuan Ha, Anh Nguyen, Huynh Ngo Minh Trang, Jeza Muhamad Abdul Aziz, Nacir Dhouibi, Bao-Tran Do Le, Ahmad Altom, Mostafa Atef Amin, Pham Do Thuc Anh, Hadeir Said and 5 more

Abstract readSystematic Review
In one paragraph

Synthesis in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Nam Xuan HaHue University of Medicine and Pharmacy, Hue University, Hue City, Vietnam.ORCID http://orcid.org/0000-0003-2202-2916
Anh NguyenOnline Research Club, Nagasaki, Japan.ORCID http://orcid.org/0000-0003-4519-8084
Huynh Ngo Minh TrangOnline Research Club, Nagasaki, Japan.ORCID http://orcid.org/0000-0003-2406-4706
Jeza Muhamad Abdul AzizBiomedical Sciences, Komar University of Science and Technology, Sulaymaniyah, Iraq.ORCID http://orcid.org/0000-0002-1522-1880
Nacir DhouibiOnline Research Club, Nagasaki, Japan.ORCID http://orcid.org/0000-0001-8160-5471
Bao-Tran Do LeOnline Research Club, Nagasaki, Japan.ORCID http://orcid.org/0000-0003-3233-8965
Ahmad AltomOnline Research Club, Nagasaki, Japan.ORCID http://orcid.org/0000-0002-2053-593X
Mostafa Atef AminOnline Research Club, Nagasaki, Japan.ORCID http://orcid.org/0000-0003-0843-6047
Pham Do Thuc AnhOnline Research Club, Nagasaki, Japan.ORCID http://orcid.org/0000-0002-6450-4238
Hadeir SaidOnline Research Club, Nagasaki, Japan.ORCID http://orcid.org/0000-0003-3201-8910
Venkata Vasavi VadduriOnline Research Club, Nagasaki, Japan.ORCID http://orcid.org/0000-0002-4644-5262
Dao Ngoc Hien TamOnline Research Club, Nagasaki, Japan.ORCID http://orcid.org/0000-0003-0162-2373
Satoko OkubayashiAdvanced Fibro-Science, Kyoto Institute of Technology, Kyoto, Japan.ORCID http://orcid.org/0000-0003-3003-6760
Abdelrahman M MakramOnline Research Club, Nagasaki, Japan. abd-makram@hotmail.com.ORCID http://orcid.org/0000-0003-2011-8092
Nguyen Tien HuyOnline Research Club, Nagasaki, Japan. nguyentienhuy1972@gmail.com.ORCID http://orcid.org/0000-0002-9543-9440

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionWearing a mask is part of the comprehensive strategy to suppress transmission of the SARS-CoV-2 virus and respiratory tract infections. Despite available COVID-19 vaccines, wearing a mask has remained one of the simplest and most effective ways. There was, however, little data available regarding the filtration of fabrics in masks against infectious aerosols or particles of viral size. This study reviewed the filtration performance of fabrics against virus-sized particles.

methodsThe systematic review was conducted according to the PRISMA 2020 statement. The literature search started with seven electronic databases (PubMed, Google Scholar, Scopus, Institute of Science Index, EMBASE, Cochrane Library, SciFinder) and two preprints, then manual search. Quality assessment was performed on all included studies using an adaptive tool for in vitro studies.

resultsEighteen studies were finally included. Cotton was most commonly investigated, followed by polymers and nanofibers. A study reported filtration performance as droplet blocking efficiency (%), and materials with the highest blocking efficiency comprised polypropylene (99.7 [95.2-99.9]), cotton (98.9 [96.8-99.8]), silk (98.7 [81.1-99.8]), and polyester/polyamide (98.2 [90-99.8]). Other studies reported filtration efficiency (%). Fabrics showing highest filtration efficiency were polymers (polypropylene, polyethylene, spunbond/meltblown/spunbond) (100, 94.4, 93.9, respectively), nanofibers (nanofiber-fabric-layered composite, polyvinyl alcohol/nylon-taffeta composite) (99.8 ± 0.24, 97.3, respectively), cotton (cotton/chiffon, cotton quilt, cotton/flannel, cotton/silk) (97 ± 2, 96 ± 2, 95 ± 2, 94 ± 2, respectively). Quality assessment showed that most of the studies were adequate to evaluate the filtration performance against aerosol or virus particles.

conclusionSome fabrics showed effectiveness in protective applications for respiratory infections; particularly nanofibers and polymers were promising materials for specialized masks or respirators.

Indexed as

FiltrationMasksTextilesAerosolsCOVID-19HumansPandemicsParticle SizeRespiratory Aerosols and DropletsSARS-CoV-2AerosolsAerosolsAir filterFiltration efficiencyMasks/respiratorsNanofibers

Identifiers

PMID42642733
PMCPMC13508351

What OpenQuestion holds

Textmetadata
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.