Pathogenesis of Fibrosis Produced by Asbestos and Man-Made Mineral Fibers: What Makes a Fiber Fibrogenic?
Bibliographic record
Abstract
Recent studies have revealed a wide array of molecular and cellular changes in cells and whole lungs exposed to asbestos fibers, changes that are presumed to be related to asbestos-induced fibrogenesis. These include generation of reactive oxygen species (ROS), induction of cell signaling factors and proinflammatory cytokines, and induction of fibrogenic mediators. Tumor necrosis factor-alpha (TNFα) appears to play a crucial role, since mice with TNFα receptor genes knocked out are resistant to asbestos-induced fibrosis. However, many man-made mineral fibers (MMVF) are able to generate ROS, cell signaling factors, and proinflammatory cytokines (probably every fiber causes expression of TNFα), but there is no clear correlation between the ability of MMVF to initiate these events and their ability to produce fibrosis. Moreover, asbestos produces fibrosis in tracheal explant systems without increasing TNFα expression, and nonfibrogenic dusts induce fibrogenic mediators such as transforming growth factor-beta (TCFβ) and platelet-derived growth factor (PDGF) but not procollagen in such systems. It remains uncertain whether alveolar macrophages are central to fibrosis, as is often assumed, or whether fibers penetrating tissue are the real effector agents. Fiber length, biopersistence, and dose clearly do play a very important role in fibrogenesis, since short fibers, readily degraded fibers, and small numbers of fibers of any type are nonfibrogenic. There is some evidence to suggest that short and nonpersistent fibers produce quantitatively less of the mediators just described, but the ability of macrophages to clear fibers is probably crucial to preventing fibrosis. Thus, molecular and cellular events must combine in as yet uncertain ways with abnormalities at a more "'macroscopic" level before fibrosis can become established.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".