Recovery and Quantification of <i>Mycobacterium Immunogenum</i> DNA from Metalworking Fluids Using Dual-Labeled Probes
Bibliographic record
Abstract
Abstract Mycobacteria in metalworking fluids (MWF) are associated with hypersensitivity pneumonitis but are difficult to recover using culture. Quantitative PCR is a promising approach to quantify mycobacteria, but three challenges exist: mycobacterial cell lysis, high-yield DNA extraction, and removal of PCR inhibitors. We used Mycobacterium spp. primers to amplify polymorphic regions of 16S-rDNA flanked with highly conserved regions. A standard curve was constructed by cloning M. immunogenum amplification product. We developed single tube DNA extraction employing mixer mill cell disruption, enzymatic digestions (lysozyme, proteinase K) followed by a mechanical disruption, and column purification. MWF was spiked with M. immunogenum, and DNA was successfully extracted. Mycobacterial 16S-RNA genes were quantified by comparing PCR amplification detection (Cycle Threshold) from our samples with that obtained from the standard curve. Recovery and quantification of mycobacterial DNA from spiked samples approached 100 %. A rapid method for quantification of mycobacteria in MWF was demonstrated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".