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Record W4382011093 · doi:10.1002/advs.202301643

Phage‐Inducible Chromosomal Islands as a Diagnostic Platform to Capture and Detect Bacterial Pathogens

2023· article· en· W4382011093 on OpenAlexaff
Rodrigo Ibarra‐Chávez, Julien Reboud, José R. Penadés, Jonathan M. Cooper

Bibliographic record

VenueAdvanced Science · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsInstitute of Infection and Immunity
FundersMedical Research CouncilShanghai University of Electric PowerConsejo Nacional de Ciencia y TecnologíaScottish Funding CouncilEngineering and Physical Sciences Research CouncilNational Institute for Health and Care Research
KeywordsComputational biologyBiologyMicrobiology

Abstract

fetched live from OpenAlex

Abstract Phage‐inducible chromosomal islands (PICIs) are a family of phage satellites that hijack phage components to facilitate their mobility and spread. Recently, these genetic constructs are repurposed as antibacterial drones, enabling a new toolbox for unorthodox applications in biotechnology. To illustrate a new suite of functions, the authors have developed a user‐friendly diagnostic system, based upon PICI transduction to selectively enrich bacteria, allowing the detection and sequential recovery of Escherichia coli and Staphylococcus aureus . The system enables high transfer rates and sensitivities in comparison with phages, with detection down to ≈50 CFU mL −1 . In contrast to conventional detection strategies, which often rely on nucleic acid molecular assays, and cannot differentiate between dead and live organisms, this approach enables visual sensing of viable pathogens only, through the expression of a reporter gene encoded in the PICI. The approach extends diagnostic sensing mechanisms beyond cell‐free synthetic biology strategies, enabling new synthetic biology/biosensing toolkits.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.008
GPT teacher head0.254
Teacher spread0.246 · 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 designBench or experimental
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

Citations9
Published2023
Admission routes1
Has abstractyes

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