Current Sarcoidosis Models and the Importance of Focusing on the Granuloma
Bibliographic record
Abstract
The inability to effectively model sarcoidosis in the laboratory or in animals continues to hinder the discovery and translation of new, targeted treatments. The granuloma is the signature pathological hallmark of sarcoidosis, yet there are significant knowledge gap exists with regard to how granulomas form. Significant progress toward improved therapeutic and prognostic strategies in sarcoidosis hinges on tractable experimental models that recapitulate the process of granuloma formation in sarcoidosis and allow for mechanistic insights into the molecular events involved. Through its inherent representation of the complex genetics underpinning immune cell dysregulation in sarcoidosis, a recently developed in vitro human granuloma model holds promise in providing detailed mechanistic insight into sarcoidosis–specific disease regulating pathways at play during early stages of granuloma formation. The purpose of this review is to critically evaluate current sarcoidosis models and assess their potential to progress the field towards the goal of improved therapies in this disease. We conclude with the potential integrated use of preclinical models to accelerate progress towards identifying and testing new drugs and drug combinations that can be rapidly brought to clinical trials.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".