Unraveling the role of fibrosis in the TB Granuloma
Bibliographic record
Abstract
Abstract Tuberculosis (TB), a deadly infectious disease caused by the bacterium Mycobacterium tuberculosis (Mtb). The disease is characterized by the development of granulomas consisting of immune cells that form a cluster around the bacteria to limit bacterial growth and disease outcomes. Control of the TB epidemic is limited by a complicated drug regimen, development of antibiotic resistance, and the lack of an effective vaccine against infection and disease. Fibrosis is common in older granulomas, and has been associated with both positive and negative disease outcomes. Little is known about fibrosis in TB, partly due to the fact that fibroblasts are difficult to identify using traditional antibody-based techniques. To provide insight into the role that fibrosis plays at a single granuloma scale, we have developed a computational, agent-based model of granuloma formation in the lung following infection with Mtb. In previously published work we have identified the mechanisms driving fibrosis within a granuloma i.e. how the granuloma environment effects fibrosis. Using immunohistochemistry, we have characterized fibroblasts and early collagen deposition in TB granulomas. Here we have extended this work to look at how fibrosis affects the ability of a granuloma to control bacteria, focusing on the role of both fibroblasts. Predictions show that early fibrosis alters the structure of the granuloma with few long-term effects on bacterial control, however late fibrosis decreases the promotability of bacterial dissemination. This work has implications on treatment options for TB that typically cause early fibrosis to occur.
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 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".