ArticleJournal of theoretical biology2017
Identifying mechanisms driving formation of granuloma-associated fibrosis during Mycobacterium tuberculosis infection.
Article in Journal of theoretical biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
39 citing papers in PubMed, 56 citations in OpenAlex.
- Post-Tuberculosis Lung Disease: Clinicopathologic Insights, Diagnosis, Prevention, and Management.MedComm · 2026Review
- Article
- Post-TB lung disease: pathogenesis, host-directed therapies, and the C3HeB/FeJ model for pre-clinical advances.Infection and immunity · 2026Review
- Tertiary Lymphoid Structures in Tuberculosis: Persistence, Protection, and Pathology.Immunological reviews · 2025Review
- The Intricate Process of Calcification in Granuloma Formation and the Complications FollowingBiomolecules · 2025Review
- Leveraging insights from cancer to improve tuberculosis therapy.Trends in molecular medicine · 2025Article
- Multiscale computational model predicts how environmental changes and treatments affect microvascular remodeling in fibrotic disease.PNAS nexus · 2025Article
- Pathogenesis of Post-Tuberculosis Lung Disease: Defining Knowledge Gaps and Research Priorities at the Second International Post-Tuberculosis Symposium.American journal of respiratory and critical care medicine · 2024Review
- Normalizing granuloma vasculature and matrix improves drug delivery and reduces bacterial burden in tuberculosis-infected rabbits.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Understanding the development of tuberculous granulomas: insights into host protection and pathogenesis, a review in humans and animals.Frontiers in immunology · 2024Review
- Long term management of people with post-tuberculosis lung disease.The Korean journal of internal medicine · 2024Review
- A brief overview of mathematical modeling of the within-host dynamics of Mycobacterium tuberculosis.Frontiers in applied mathematics and statistics · 2024Article
- Characterizing the immune response toFrontiers in immunology · 2024Review
- Editorial: Recent advancements in mycobacterial diseases research.Frontiers in microbiology · 2024Article
- Inflammation: The Link between Neural and Vascular Impairment in the Diabetic Retina and Therapeutic Implications.International journal of molecular sciences · 2023Review
- Automated quantitative assay of fibrosis characteristics in tuberculosis granulomas.Frontiers in microbiology · 2023Article
- Extracellular matrix proteins (fibronectin, collagen III, and collagen I) immunoexpression in goat tuberculous granulomas (Mycobacterium caprae).Veterinary research communications · 2022Article
- Immunologic and imaging signatures in post tuberculosis lung disease.Tuberculosis (Edinburgh, Scotland) · 2022Review
- Ocular mycobacterial lesions in cats.Veterinary pathology · 2022Article
- T cell transcription factor expression evolves over time in granulomas from Mycobacterium tuberculosis-infected cynomolgus macaques.Cell reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), is a pulmonary pathogen of major global concern. A key feature of Mtb infection in primates is the formation of granulomas, dense cellular structures surrounding infected lung tissue. These structures serve as the main site of host-pathogen interaction in TB, and thus to effectively treat TB we must clarify mechanisms of granuloma formation and their function in disease. Fibrotic granulomas are associated with both good and bad disease outcomes. Fibrosis can serve to isolate infected tissue from healthy tissue, but it can also cause difficulty breathing as it leaves scars. Little is known about fibrosis in TB, and data from non-human primates is just beginning to clarify the picture. This work focuses on constructing a hybrid multi-scale model of fibrotic granuloma formation, in order to identify mechanisms driving development of fibrosis in Mtb infected lungs. We combine dynamics of molecular, cellular, and tissue scale models from previously published studies to characterize the formation of two common sub-types of fibrotic granulomas: peripherally fibrotic, with a cuff of collagen surrounding granulomas, and centrally fibrotic, with collagen throughout granulomas. Uncertainty and sensitivity analysis, along with large simulation sets, enable us to identify mechanisms differentiating centrally versus peripherally fibrotic granulomas. These findings suggest that heterogeneous cytokine environments exist within granulomas and may be responsible for driving tissue scale morphologies. Using this model we are primed to better understand the complex structure of granulomas, a necessity for developing successful treatments for TB.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.