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Record W2035838980 · doi:10.1080/15459620802446343

Real-Time PCR Quantification of<i>Mycobacterium immunogenum</i>in Used Metalworking Fluids

2008· article· en· W2035838980 on OpenAlexafffund
Marc Veillette, G. Pagé, Peter S. Thorne, Caroline Duchaine

Bibliographic record

VenueJournal of Occupational and Environmental Hygiene · 2008
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersNational Institute of Environmental Health SciencesInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail
KeywordsReal-time polymerase chain reactionHypersensitivity pneumonitisChromatographyStandard curvePolymerase chain reactionBiologyMicrobiologyChemistryMedicineGeneticsInternal medicineLung

Abstract

fetched live from OpenAlex

Rapid detection and quantification of Mycobacterium immunogenum in field samples of metalworking fluids (MWFs) is important for factory fluid surveillance programs. The applicability of the developed DNA extraction and quantitative real-time PCR (qPCR) methods to detect and quantify M. immunogenum in used MWFs was evaluated. Total DNA from these samples was extracted, and M. immunogenum measured by qPCR by comparison with a standard curve derived from plasmid vectors. PCR counts were compared with bacterial culture counts. PCR counts of M. immunogenum varied from 1.42 x 10(3) to 3.68 x 10(6) cells/mL of MWFs. Recovery of M. immunogenum by bacterial culture varied from 2.5% to 70% of qPCR count in corresponding samples. Quantitative PCR could be used to measure M. immunogenum load in MWF samples with greater sensitivity and shorter processing time than the classic bacterial culture-based approach. The proposed qPCR approach could be routinely used in real-time PCR-equipped laboratories to provide early detection of M. immunogenum and to control proliferation that probably leads to hypersensitivity pneumonitis in exposed workers.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.365

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 head0.038
GPT teacher head0.286
Teacher spread0.247 · 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 designObservational
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

Citations16
Published2008
Admission routes2
Has abstractyes

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