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Record W3167550255 · doi:10.3390/v13061057

The Potential of Phage Therapy against the Emerging Opportunistic Pathogen Stenotrophomonas maltophilia

2021· review· en· W3167550255 on OpenAlexafffund
Jaclyn G. McCutcheon, Jonathan J. Dennis

Bibliographic record

VenueViruses · 2021
Typereview
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaDirectorate for Biological SciencesAlberta InnovatesUniversity of Alberta
KeywordsStenotrophomonas maltophiliaPhage therapyMicrobiologyVirulenceBiologyStenotrophomonasPathogenMultiple drug resistanceIsolation (microbiology)AntimicrobialBacteriophageAntibiotic resistanceBacteriaVirologyDrug resistanceAntibioticsPseudomonas aeruginosaPseudomonasGeneEscherichia coliGenetics

Abstract

fetched live from OpenAlex

The isolation and characterization of bacteriophages for the treatment of infections caused by the multidrug resistant pathogen Stenotrophomonas maltophilia is imperative as nosocomial and community-acquired infections are rapidly increasing in prevalence. This increase is largely due to the numerous virulence factors and antimicrobial resistance genes encoded by this bacterium. Research on S. maltophilia phages to date has focused on the isolation and in vitro characterization of novel phages, often including genomic characterization, from the environment or by induction from bacterial strains. This review summarizes the clinical significance, virulence factors, and antimicrobial resistance mechanisms of S. maltophilia, as well as all phages isolated and characterized to date and strategies for their use. We further address the limited in vivo phage therapy studies conducted against this bacterium and discuss the future research needed to spearhead phages as an alternative treatment option against multidrug resistant S. maltophilia.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.053
GPT teacher head0.319
Teacher spread0.266 · 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

Citations42
Published2021
Admission routes2
Has abstractyes

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