Cellular crosstalk in fibrosis: Insights into macrophage and fibroblast dynamics
Bibliographic record
Abstract
Pathological fibrosis, the excessive deposition of extracellular matrix and tissue stiffening that causes progressive organ dysfunction, underlies diverse chronic diseases. The fibrotic microenvironment is driven by the dynamic microenvironmental interaction between various cell types; macrophages and fibroblasts play central roles in fibrotic disease initiation, maintenance, and progression. Macrophage functional plasticity to microenvironmental stimuli modulates fibroblast functionality by releasing pro-inflammatory cytokines, growth factors, and matrix remodeling enzymes that promote fibroblast proliferation, activation, and differentiation into myofibroblasts. Activated fibroblasts and myofibroblasts serve as the fibrotic effector cells, secreting extracellular matrix components and initiating microenvironmental contracture. Fibroblasts also modulate macrophage function by releasing their own pro-inflammatory cytokines and growth factors, creating bidirectional crosstalk that reinforces the chronic fibrotic cycle. The intricate interplay between macrophages and fibroblasts, including their secretomes and signaling interactions, leads to tissue damage and pathological loss of tissue function. In this review, we examine macrophage-fibroblast reciprocal dynamic interactions in pathological fibrotic conditions. We discuss the specific lineages and functionality of macrophages and fibroblasts implicated in fibrotic progression, with focus on their signal transduction pathways and secretory signaling that enables their pro-fibrotic behavior. We then finish with a set of recommendations for future experimentation to develop a set of potential targets for anti-fibrotic therapeutic candidates. Understanding the cellular interactions between macrophages and fibroblasts provides valuable insights into potential therapeutic strategies to mitigate fibrotic disease progression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".