Evidence map›Paper›PMID 41776583›Full record

ArticleAnnals of clinical microbiology and antimicrobials2026

Airborne Mycobacterium tuberculosis inactivated by advanced photohydrolysis technology.

Jennifer E Peel, Alyssa A Varghese, Reina N Paez, Sadhana Chauhan, Janice J Endsley, William S Lawrence

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of clinical microbiology and antimicrobials, 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

6 authors.

Jennifer E PeelDepartment of Microbiology and Immunology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA.
Alyssa A VargheseDepartment of Microbiology and Immunology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA.
Reina N PaezDepartment of Microbiology and Immunology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA.
Sadhana ChauhanDepartment of Microbiology and Immunology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA.
Janice J EndsleyDepartment of Microbiology and Immunology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA.
William S LawrenceDepartment of Microbiology and Immunology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA. wslawren@utmb.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tuberculosis is a respiratory infectious disease that persists worldwide, largely due to the robustness and ease of dissemination of its causative agent, Mycobacterium tuberculosis. To mitigate the spread of infection, various measures have been developed to inactivate the bacteria in its aerosol form. Advanced photohydrolysis technology (APHT) was previously reported to inactivate both respiratory viral and bacterial pathogens; however, its efficacy against M. tuberculosis has yet to be evaluated. In this study, we assessed the ability of APHT to inactivate aerosolized M. tuberculosis. The bacteria were aerosolized into a custom chamber containing an APHT device, which was operated for 1 and 10 min after aerosolization. A control device lacking the APHT component was used for comparison. The APHT device achieved a 1.3-log

Indexed as

Air MicrobiologyDisinfectionMycobacterium tuberculosisAerosolsBacterial LoadHumansAerosolsAerosolInfection controlMycobacterium tuberculosisPhotohydrolysis

Identifiers

PMID41776583
PMCPMC13067662

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.