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Record W4408121013 · doi:10.1016/j.bios.2025.117283

Electrochemical detection of Legionella pneumophila using DNAzymes and under continuous flow in cooling tower water

2025· article· en· W4408121013 on OpenAlexafffund
Enas Osman, Survanshu Saxena, Shuwen Qian, Jonathan L'Heureux‐Hache, Phoebe Li, Jinal Manek, Jimmy Gu, Todd Hoare, Yingfu Li, Leyla Soleymani

Bibliographic record

VenueBiosensors and Bioelectronics · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLegionella and Acanthamoeba research
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsLegionella pneumophilaLegionellaDeoxyribozymeMicrobiologyCooling towerPathogenDetection limitEnvironmental scienceWater coolingBiologyChemistryBacteriaChromatographyEngineering

Abstract

fetched live from OpenAlex

Rapid detection of Legionella pneumophila in cooling tower water is crucial to mitigate the fatal consequences of Legionnaires disease. This study presents a microfluidic system that employs RNA-cleaving DNAzymes (RCDs) for continuous real time monitoring of this pathogen directly in a single sample of cooling tower water without the need for lengthy bacterial culture. The RCDs, coupled to microgel magnetic beads, are programmed to release an electroactive DNA barcode in the presence of L. pneumophila , which is detected by a downstream electrochemical sensor in real time. Our system identifies key parameters such as peak current, slope of signal increase, and lag time that correlate with L. pneumophila concentration, achieving a limit of detection of 1.4 × 10 3 CFU/mL in buffer and 1.9 × 10 3 CFU/mL in cooling tower water, meeting regulatory requirements. This system was further used to identify different serotypes of L. pneumophila amongst other waterborne bacterial species including non pneumophila species of Legionella , creating a highly specific tool for identifying this high-risk pathogen.

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 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 score0.003
Threshold uncertainty score0.444

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 head0.008
GPT teacher head0.251
Teacher spread0.243 · 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 teacher head, 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

Citations5
Published2025
Admission routes2
Has abstractyes

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