Use of untargeted magnetic beads to capture <i>Mycobacterium smegmatis</i> and <i>Mycobacterium avium paratuberculosis</i> prior detection by mycobacteriophage D29 and Real-Time-PCR
Bibliographic record
Abstract
ABSTRACT Untargeted magnetic beads were evaluated to capture Mycobacterium smegmatis and Mycobacterium avium subspecies paratuberculosis (MAP) from spiked feces, milk, and urine. Untargeted magnetic beads recovered more M. smegmatis cells from PBS suspension than the typical centrifugation method; 96.31% of 1.68 × 10 4 CFU/mL viable M. smegmatis were recovered by beads and 0% by centrifugation. Likewise, the F57 -qPCR detection of MAP cells was different whether they were recovered by beads or centrifugation; cycle threshold (Ct) was lower (p<0.05) for the detection of MAP cells recovered by beads than centrifugation, indicative of higher sensitivity. Magnetic separation of MAP cells from milk, urine, and feces specimens were detected by F57 and IS900 sequences. Ct values demonstrated that beads captured no less than 10 9 CFU/mL from feces and no less than 10 4 CFU/mL from milk and urine suspensions. In another detection strategy, M. smegmatis coupled to magnetic beads were infected by mycobacteriophage D29. Plaque forming units were observed after 24 h of incubation from urine samples containing 2 × 10 5 and 2 × 10 3 CFU/mL M. smegmatis . The results of this study provide a promising tool for diagnosis of Johne’s disease and other mycobacterial diseases such as tuberculosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".