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Record W2752800982 · doi:10.1117/12.2283900

Bioluminescent pathogens as a tool to monitor infection in live animals

2017· article· en· W2752800982 on OpenAlexaff
Lubov Brovko

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicbioluminescence and chemiluminescence research
Canadian institutionsCanadian Institute for Public Safety Research and Treatment
Fundersnot available
KeywordsBioluminescenceBiologyMicrobiologySalmonellaAntimicrobialBacteriaGreen fluorescent proteinIn vivoBioreporterPathogenic bacteriaVirologyGeneReporter geneBiotechnologyGene expressionBiochemistry

Abstract

fetched live from OpenAlex

The study of pathogenic processes is mostly limited to in vitro assays, cell-culture techniques and post mortem examination of infected animals. A better understanding of the infectious process, efficiency of antimicrobial and antibiotic treatment as well as immunomodulatory effects of different food supplements could be achieved by in vivo real-time monitoring of bacterial colonization in live animals. It was proposed recently to use bacterial pathogens with luminescent or fluorescent phenotypes for photonic detection of bacterial cells in living hosts. 14 It was shown that both bacteria transformed with full cassette of luminescent genes from Xenorhabdus luminescens and with Green Fluorescent Protein (GFP) could be visualized in animal using whole-body luminescent or fluorescent imaging techniques with high sensitivity and in real time. We used this approach to investigate the effect of diet on the time-course of infection in mice orally infected with bioluminescent strain of Salmonella enteritidis.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.320
Teacher spread0.302 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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