Metalworking Fluid with Mycobacteria and Endotoxin Induces Hypersensitivity Pneumonitis in Mice
Bibliographic record
Abstract
BACKGROUND: Human cases of hypersensitivity pneumonitis (HP) have been reported among machinists for over 10 yr. Although mycobacteria have been implicated as causal agents, this has not been established in experimental studies and the mechanisms remain unclear. Other constituents of in-use metalworking fluids (MWFs) may also contribute to the development of lung disease. We investigated the potential for Mycobacterium immunogenum (MI) in MWFs to induce HP. METHODS: Mice were exposed intranasally for 3 wk to MI (isolated from MWFs), Saccharopolyspora rectivirgula (positive control), saline, endotoxin, MWFs spiked with endotoxin and/or MI, used MWFs, and particulate-fortified used MWFs. Responses were assessed 96 h after the last exposure. RESULTS: Mice exposed to MI in MWFs developed lung pathology consistent with HP along with significantly more monocytes and neutrophils in lung lavage, increased CD4+/CD8+ T-lymphocyte ratio, and marked pulmonary lymphocytosis on histologic examination when compared with saline-treated control mice. Mice with Grade 2 or higher pathology (0-4 point scale) exhibited significantly elevated macrophage inflammatory protein-1alpha and IL-10 and a trend toward higher RANTES 96 h after the final dose. Endotoxin coexposure augmented lung pathology. CONCLUSION: MWFs containing mycobacteria induced granulomatous lung lesions, peribronchiolar lymphocytosis, increased cell concentrations in lavage, and up-regulation of several cytokines. These findings are consistent with HP.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".