MétaCan
Menu
Back to cohort
Record W3008501285 · doi:10.2217/fmb-2019-0279

<i>In Vitro</i> Detection of Porphyrin-Producing Wound Bacteria with Real-Time Fluorescence Imaging

2020· article· en· W3008501285 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFuture Microbiology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsPrincess Margaret Cancer CentreSinai Health SystemUniversity Health NetworkOntario Council of University Libraries
Fundersnot available
KeywordsPorphyrinFluorescenceBacteriaBiofilmMicrobiologyFluorescence-lifetime imaging microscopyYeastFluorescence microscopeAgar plateAgarBiologyChemistryBiochemistryOptics

Abstract

fetched live from OpenAlex

Aim: Fluorescence imaging can visualize polymicrobial populations in chronic and acute wounds based on porphyrin fluorescence. We investigated the fluorescent properties of specific wound pathogens and the fluorescence detected from bacteria in biofilm. Methods: Utilizing Remel Porphyrin Test Agar, 32 bacterial and four yeast species were examined for red fluorescence under 405 nm violet light illumination. Polymicrobial biofilms, supplemented with δ-aminolevulinic acid, were investigated similarly. Results: A total of 28/32 bacteria, 1/4 yeast species and polymicrobial biofilms produced red fluorescence, in agreement with their known porphyrin production abilities. Conclusion: These results identify common wound pathogens capable of producing porphyrin-specific fluorescence and support clinical observations using fluorescence imaging to detect pathogenic bacteria in chronic wounds.

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.

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 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 score0.015
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

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

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 head0.003
GPT teacher head0.186
Teacher spread0.182 · 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