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Phage therapy to treat unresponsive infections: A primer for the clinical microbiology laboratory staff

2025· article· en· W4406418403 on OpenAlexafffund
Josephine M. Davey-Young, Dinuri D. Punchihewa, Bernadette Ng, Jenna Wong, Greg J. German

Bibliographic record

VenueClinical Microbiology Newsletter · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsSt Joseph's Health CentreMaple Leaf FoodsMcMaster UniversityUniversity of TorontoUniversity of Ottawa
FundersCanadian Institutes of Health ResearchTemerty Faculty of Medicine, University of TorontoCentre for Research on Pandemic Preparedness and Health Emergencies
KeywordsPhage therapyPrimer (cosmetics)Clinical microbiologyMicrobiologyMedicineIntensive care medicineBiologyBacteriophageChemistryGenetics

Abstract

fetched live from OpenAlex

With the increase in antimicrobial resistance and subsequent need for alternatives to traditional antibiotics, phage therapy (PT) has gained a renewed interest. Much like antibiotics, bacteriophages or simply phages, have shown promise in eradicating bacterial infections; however, their fundamental differences require specific laboratory protocols and practices. As bacterial-specific viruses, they must be detected, replicated, and purified for safety and efficacy. The narrow spectrum of activity of phages provides a targeted approach to infection but also necessitates expansive libraries and susceptibility testing to match phages to bacteria. Such testing is not standardized, complicating both research and clinical efforts. This review then provides a background on PT in the clinical microbiology laboratory and an overview of such protocols and practices specific to PT, such as classic susceptibility testing methods and updated approaches. Also covered are the challenges and future directions for the field, as well as resources for clinical and research laboratory personnel.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.034
GPT teacher head0.377
Teacher spread0.343 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations2
Published2025
Admission routes2
Has abstractyes

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