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Record W4410219249 · doi:10.1016/j.indenv.2025.100099

Ultraviolet germicidal irradiation: Advances in viral inactivation and vaccine development

2025· article· en· W4410219249 on OpenAlexafffund
Kathleen K. M. Glover, Sunday S. Nunayon, Lexuan Zhong

Bibliographic record

VenueIndoor Environments · 2025
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUltraviolet irradiationVirologyIrradiationUltravioletChemistryMicrobiologyBiologyMaterials sciencePhysicsOptoelectronics

Abstract

fetched live from OpenAlex

Ultraviolet germicidal irradiation (UVGI) has gained global attention for preventing disease transmission. While UVGI technology’s utility in sterilization is well-established, there is growing interest in exploring its potential applications in vaccine development. Relevant studies are reviewed to provide a comprehensive perspective on the current state and future potential of UVGI in this domain. Specifically, this review investigates the effectiveness of UVGI in inactivating various pathogens and details the mechanisms through which different research groups have demonstrated their ability to prevent the transmission of various microbes. The novelty of this review, to the best of our knowledge, is the first to critically examine the feasibility of UVGI technology in vaccine development, focusing on its capacity to produce inactivated vaccines, immunogenicity profile, and scalability. The study also identifies existing research gaps, especially in developing novel vaccines using UVGI. By doing so, we aim to provide a comprehensive understanding of how UVGI can contribute to future strategies for preventing infectious diseases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.246
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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