Evaluation of In Situ Methods Used To Detect <i>Mycobacterium avium</i> subsp. <i>paratuberculosis</i> in Samples from Patients with Crohn's Disease
Bibliographic record
Abstract
In common with other diagnostic tests, detection of mycobacteria in tissue by microscopic examination is susceptible to spectrum bias. Since Crohn's disease is defined by the absence of detectable pathogenic organisms, the use of in situ techniques to search for Mycobacterium avium subsp. paratuberculosis in Crohn's disease samples requires validation of methods in a paucibacillary setting. To generate paucibacillary infection, C57BL/6 mice were artificially infected with Mycobacterium avium subsp. paratuberculosis strain K10 and M. tuberculosis H37Rv, yielding tissues harboring fewer than one bacillus per oil immersion field. Serial sections of organs were then studied by cell wall-based staining techniques (Ziehl-Neelsen and auramine rhodamine) and nucleic acid-based staining techniques (in situ hybridization [ISH] and indirect in situ PCR [IS PCR]). Microscopic examination and measurement of morphometric parameters of bacilli revealed that for all methods, Mycobacterium avium subsp. paratuberculosis bacilli were observed to be shorter, smaller, and less rod shaped than M. tuberculosis bacilli. Ziehl-Neelsen, auramine rhodamine stains, ISH targeting rRNA, and IS-PCR targeting the IS900 element afforded comparable sensitivities, but for all methods, visualization of individual bacterial forms required magnification x1,000. Auramine rhodamine staining and IS-PCR generated positive signals in negative controls, indicating the nonspecificity of these assays. Together, our results indicate that detection of Mycobacterium avium subsp. paratuberculosis bacilli in tissue requires oil immersion microscopy, that rRNA-ISH provides sensitivity and specificity comparable to those of Ziehl-Neelsen staining, and that the microscopic detection limit for Mycobacterium avium subsp. paratuberculosis in tissue is governed more by bacterial burden than by staining method.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".